Question: A new 60 GHz wideband antenna using coupled half-mode/quarter-mode substrate integrated waveguide technology.

This paper introduces a quarter-mode substrate integrated waveguide (QMSIW) structure, which is essentially a quadrant segment of a square waveguide resonator, and explores its potential in antenna design. The QMSIW is derived by dividing the half-mode substrate integrated waveguide (HMSIW) along its quasi-magnetic wall when operating in TE101 and TE202 modes, akin to the process of halving a substrate integrated waveguide (SIW) to create HMSIW. The QMSIW retains the majority of the original SIW's field distribution and generates leaky waves through its dielectric aperture. By positioning the feeding port at one corner of the QMSIW, a linearly polarized radiation can be obtained when the QMSIW resonates in the TE101QM mode. Conversely, when the QMSIW resonates in the TE202QM mode, a circularly polarized (CP) wave is produced. An antenna prototype based on the QMSIW concept has been designed, fabricated, and tested, with experimental results closely aligning with the simulation outcomes, validating the efficacy of the QMSIW in antenna applications.

A simple technique is developed in this communication to increase the axial ratio (AR) bandwidth and achieve good impedance matching of a single-fed low profile circularly polarized (CP) stacked patch antenna. The proposed antenna is composed of a driven patch layer and a parasitic patch layer. The driven patch layer consists of a truncated main patch, a parasitic patch and a probe feeding structure while the stacked patch layer is comprised of five patches. The proposed antenna combines the attractive features such as low profile, wide impedance and AR bandwidths, high gain as well as easiness of design, manufacture and integration. The antenna operating at 6 GHz band is designed and fabricated on an FR4 substrate and the overall volume is 0.8 λ0 ×0.8 λ0 ×0.09 λ0. Measured results show that the antenna achieves an impedance bandwidth of more than 30% for dB, a 3-dB AR bandwidth of about 20.7%, and a gain level of over 7.9 dBi within the 3-dB AR bandwidth.

A new CPW to waveguide transition is developed based on the concept of tapered slot antenna and E-plane probe coupling. The transition consists of a tapered slotline probe and a slotline to CPW matching section. The current design has the advantages of broad bandwidth, compact size, low fabrication cost, and high reliability. The characteristics of a prototype transition are investigated by numerical simulation on Duroid substrate and WR-90 waveguide. The back-to-back combination is measured to verify the agreement with the simulated results and realization of this design.

A low-cost, fully-integrated antenna-in-package solution for 60 GHz phased-array systems is demonstrated. Sixteen patch antennas are integrated into a 28 mm × 28 mm ball grid array together with a flip-chip attached transmitter or receiver IC. The packages have been implemented using low temperature co-fired ceramic technology. 60 GHz interconnects, including flip-chip transitions and via structures, are optimized using full-wave simulation. Anechoic chamber measurement has shown ~ 5 dBi unit antenna gain across all four IEEE 802.15.3c channels, achieving excellent model-to-hardware correlation. The packaged transmitter and receiver ICs, mounted on evaluation boards, have demonstrated beam-steered, non-line-of-sight links with data rates up to 5.3 Gb/s.

In this paper a multiband fractal based rectangular microstrip patch antenna is designed. FR4 substrate having thickness of 1.58 mm is used as substrate material for the design of proposed antenna and microstrip feed line provides the excitation to the antenna. The antenna operating frequency range is from 1 to 10 GHz. The proposed antenna resonate at twelve different frequencies as 1.86, 2.33, 3.67, 4.57, 5.08, 6.06, 7.03, 7.75, 8.08, 8.84, 9.56 and 10 GHz and the return losses are -15.39, -16.48, -10.02, -17.29, -13.15, -23.41, -10.22, -11.28, -17.02, -10.94, -15.15 and -15.48 dB respectively. The proposed antenna is designed and simulated by using the Ansoft HFSS V13 (high frequency structure simulator) software.

A high gain wideband U-shaped patch antenna with two equal arms on poly tetra fluoro ethylene (PTFE) substrate is presented. An inverted U-shaped slot is introduced on the circular or square shaped ground plane just under the U-shaped patch. In this communication the effect of size and shape of the ground plane on impedance bandwidth is studied. Maximum impedance bandwidth of 86.79% (4.5–11.4 GHz) is obtained with circular shaped ground plane with diameter 36 mm. The highest gain achieved is 4.1 dBi. The simulated results are confirmed experimentally. The proposed antenna is simple in structure compared to the regular stacked or coplanar parasitic patch antennas. It is highly suitable for wireless communications.

A dual circularly polarized (CP) waveguide array antenna is presented for Ka-band wireless communication. A novel triple-resonant cavity is proposed to implement mode conversion and impedance matching in the <inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula>-element subarray. A ridge waveguide polarizer is integrated to form the left-hand circular polarization (LHCP) and the right-hand circular polarization (RHCP). A flyover-shaped full-corporate feeding network is designed to accommodate the dual polarizations and keep the wideband characteristic for a large-scale array. Subarray spacing is used to optimize the port-to-port isolation and decrease the sidelobe level at some definite angles. Computer numerically controlled machining technology is applied to fabricating a <inline-formula> <tex-math notation="LaTeX">$16 \times 16$ </tex-math></inline-formula>-element prototype. Measured results demonstrate a bandwidth of 16% from 27.6 to 32.4 GHz for the dual polarizations, in terms of good reflection coefficient and axial ratio. A maximum gain of 32.8 dBic is achieved at 28.8 GHz. A total efficiency over 60% is obtained throughout the above band for both LHCP and RHCP ports.

This paper presents the technique for increasing bandwidth of rectangular patch antenna from 0.88 GHz (7.76 - 8.64 GHz) to 6.75 GHz (3.49 - 10.24 GHz). This technique use inset feed patch antenna with modified ground plane for achieved widest bandwidth. We will propose three types of rectangular patch antenna: the simple rectangular patch fed by microstrip line, inset feed rectangular patch and inset feed rectangular patch with modifies ground plane. The final simulation result show that the lower edge of frequency is moved down from 7.76 GHz to 3.49 GHz and the higher edge of frequency is shifted up from 8.64 GHz to 10.24 GHz, which is the one selection for increasing bandwidth of rectangular patch antenna for wideband. Details of the increasing bandwidth of microstrip patch antenna are described, and simulation results for obtained wideband performance are presented by using IE3D Zeland software.

The radiation characteristics of the first higher order mode of microstrip lines are investigated. As a result, a simple travelling wave antenna element is scribed, having a larger bandwidth compared with resonator antennas. A method to excite the first higher order mode is shown. A single antenna element is treated theoretically and experimentally, and an array of four antenna elements is demonstrated.

A metamaterial-based broadband low-profile mushroom antenna is presented. The proposed antenna is formed using an array of mushroom cells and a ground plane, and fed by a microstrip line through a slot cut onto the ground plane. With the feeding slot right underneath the center gap between the mushroom cells, the dual resonance modes are excited simultaneously for the radiation at boresight. A transmission-line model integrated with the dispersion relation of a composite right/left-handed mushroom structure is applied to analyze the modes. The proposed dielectric-filled (εr=3.38) mushroom antenna with a low profile of 0.06λ0 ( λ0 is the operating wavelength in free space) and a ground plane of 1.10λ0×1.10λ0 attains 25% measured bandwidth with(|S11| <; - 10dB) 9.9-dBi average gain at 5-GHz band. Across the bandwidth, the antenna efficiency is greater than 76%, and cross-polarization levels are less than -20 dB.

A novel design concept to enhance the bandwidth of a differential-fed patch antenna using the dual-resonant radiation of a stepped-impedance resonator (SIR) is proposed. The SIR is composed of two distinctive portions: the radiating patch and a pair of open stubs. Initially, based on the transmission line model, the first and second odd-order radiative resonant modes, i.e., TM10 and TM30, of this SIR-typed patch antenna are extensively investigated. It is demonstrated that the frequency ratio between the dual-resonant modes can be fully controlled by the electrical length and the impedance ratios between the open stub and radiating patch. After that, the SIR-typed patch antenna is reshaped with stepped ground plane in order to increase the impedance ratio as highly required for wideband radiation. With this arrangement, these two radiative modes are merged with each other, resulting in a wide impedance bandwidth with a stable radiation pattern under dual-resonant radiation. Finally, the proposed antenna is designed, fabricated, and measured. It is verified in experiment that the impedance bandwidth (|Sdd11| <; -10 dB) of the proposed antenna has gained tremendous increment up to 10% (0.85-0.94 GHz) with two attenuation poles. Most importantly, the antenna has achieved a stable gain varying from 7.4 to 8.5 dB within the whole operating band, while keeping low-cross polarization.


Question: A new 60 GHz wideband antenna design using coupled half-mode/quarter-mode substrate integrated waveguide technology.

A Unique Dual-Band Textile Half Diamond HMSIW Antenna for Wearable On-Body Applications

This paper presents a new design for a dual-band textile antenna, specifically optimized for use in wearable on-body systems operating at 2.4 and 5.8 GHz within the Industrial, Scientific, and Medical (ISM) bands. The innovative design incorporates brass eyelets and a blend of conductive and non-conductive textile materials to create a compact, flexible half-mode substrate integrated waveguide (HMSIW) cavity with an integrated ground plane. This allows for efficient radiation away from the wearer, ensuring safety and performance.

To further reduce the size of the antenna, a series of shorting vias and slots are added, achieving additional miniaturization without compromising functionality. The antenna demonstrates outstanding free space performance, with measured impedance bandwidths of 4.9% and 5.1% at 2.4 and 5.8 GHz, respectively, and maximum free-space gains of 4.1 and 5.8 dBi. The efficiency levels are also commendable, at 72.8% and 85.6%.

When worn on the human body or bent around cylinders with radii of 75 and 40 mm, the antenna exhibits remarkable stability, with minimal frequency shifts. The measured on-body gain at 2.45 and 5.8 GHz is 4.4 and 5.7 dBi, respectively, accompanied by safe Specific Absorption Rate (SAR) values of 0.55 and 0.90 W/kg. These characteristics make the proposed antenna an ideal choice for wearable on-body systems, combining excellent performance with user safety and comfort.

This paper presents a novel transition between a microstrip line and a substrate integrated waveguide (SIW) in a multilayer substrate design environment. In order to achieve a low-loss broadband response, the transition, consisting of a tapered or multisectional ridged SIW and a tapered microstrip line, is modeled and designed by simultaneously considering both impedance matching and field matching. Characteristic impedance and guided wavelength calculated by using closed-form expressions based on a transverse resonant method are used to develop our design procedure. Effective broad bandwidth is obtained in two examples developed in this study, which are validated with simulated and measured results. This transition provides a simple way to design substrate integrated circuits with buried microstrip circuits in the multilayer substrate in which any ratio of impedance transform can be anticipated.

In this letter, a wideband compact circularly polarized (CP) patch antenna is proposed. This patch antenna consists of a printed meandering probe (M-probe) and truncated patches that excite orthogonal resonant modes to generate a wideband CP operation. The stacked patch is employed to further improve the axial-ratio (AR) bandwidth to fit the 5G Wi-Fi application. The proposed antenna achieves 42.3% impedance bandwidth and 16.8% AR bandwidth, respectively. The average gain within the AR bandwidth is 6.6 dBic with less than 0.5 dB variation. This work demonstrates a bandwidth broadening technique of an M-probe fed CP patch antenna. It is the first study to investigate and exhibit the M-probe could also provide the wideband characteristics in the dielectric loaded patch antenna. The potential applications of the antenna are 5G Wi-Fi and satellite communication systems.

The higher order mode substrate integrated waveguide (SIW) has advantages in the applications, which are: simplifying the structure with reduced fabrication cost and enhancing the stability of performance by relaxed fabrication tolerance. In this paper, two wideband TE20 mode excitation structures are presented and investigated for the SIW. A slot along the mid line in the longitudinal direction of the SIW is employed to convert the electromagnetic field pattern between slotline and the TE20 mode SIW. The second structure is based on the slot aperture coupling and can realize wideband direct transition between the TE20 mode SIW and microstrip line. Both transitions have a simple and compact structure, as well as broadband characteristics. The wideband transition performance is demonstrated in this paper. As application examples of the transitions, two broadband substrate integrated baluns are presented and fabricated based on the TE20 mode excitation structures and TE20 mode characteristics. The 180 ° out-of-phase and equal magnitude of the balun outputs in a wide frequency range can be achieved inherently. The balun based on the slotline excitation has a fractional bandwidth of 50.2%, from 7.3 to 12.2 GHz, with measured return loss, amplitude imbalance, and phase imbalance better than 10 and 0.45 dB and 3.8 °. The balun based on the aperture coupling excitation shows a fractional bandwidth of 50.3%, from 7 to 11.7 GHz, with measured return loss, insertion loss, amplitude imbalance, and phase imbalance better than 10, 1, and 0.27 dB and 2.4 °, respectively. Both baluns not only show good performance, but also demonstrate the existence of the TE20 mode in the SIW.

A planer micro-strip to waveguide transition structure is given in this paper. Coupled patch on the bottom of the substrate is excited by the micro-strip feed-line on the top, which realizes the transformation from the TE10 mode in the waveguide to the quasi-TEM mode on the micro-strip line. Modeling and simulation of the structure are conducted for the design by using electromagnetic simulation software HFSS. The measurement of the manufactured structure exhibits an insertion loss less than 0.5dB within 33GHz~37GHz. Compared to traditional waveguide E-plane probe or H-plane probe transition, this design has properties of low profile and easy integration.

In the rapid progress of commercial communication applications, the development of compact antenna has an important role. This paper presents the analysis on the performance of single band compact triple C shaped microstrip patch antenna for WLAN, WiMAX and Wi-Fi applications with center frequency at 2.5GHz. The microstrip antenna has a planar geometry and consists of a ground plane, a substrate, a compact patch as radiator. Different specifications of the proposed antenna are measured through computer simulation in free space. The antenna has an impedance bandwidth of 2500 MHz (2487MHz to 2512 MHz). The proposed antenna provides excellent SWR of 1.04 and return loss -32.80 dB while excited by a 50 O microstrip feed line. All simulation results are performed using the CST Microwave Studio.

A simple technique is developed in this communication to increase the axial ratio (AR) bandwidth and achieve good impedance matching of a single-fed low profile circularly polarized (CP) stacked patch antenna. The proposed antenna is composed of a driven patch layer and a parasitic patch layer. The driven patch layer consists of a truncated main patch, a parasitic patch and a probe feeding structure while the stacked patch layer is comprised of five patches. The proposed antenna combines the attractive features such as low profile, wide impedance and AR bandwidths, high gain as well as easiness of design, manufacture and integration. The antenna operating at 6 GHz band is designed and fabricated on an FR4 substrate and the overall volume is 0.8 λ0 ×0.8 λ0 ×0.09 λ0. Measured results show that the antenna achieves an impedance bandwidth of more than 30% for dB, a 3-dB AR bandwidth of about 20.7%, and a gain level of over 7.9 dBi within the 3-dB AR bandwidth.

The hemisphere lens-loaded Vivaldi antenna for the microwave imaging applications is designed and tested in this paper. The proposed antenna is designed to work in the wide frequency band of 1–14 GHz, and is fabricated on the FR-4 substrate. The directivity of the proposed Vivaldi antenna is enhanced using a hemispherical shape dielectric lens, which is fixed on the end-fire direction of the antenna. The proposed antenna is well suited for the microwave imaging applications because of the wide frequency range and high directivity. The design of the antenna is carried out using the CST microwave studio, and various parameters such as the return loss, the radiation pattern, the directivity, and input impedance are optimized. The maximum improvement of 4.19 dB in the directivity is observed with the designed hemisphere lens. The antenna design is validated by fabricating and testing it in an anechoic environment. Finally, the designed antenna is utilized to establish a setup for measuring the scattering coefficients of various objects and structures in the frequency band of 1–14 GHz. The two-dimensional (2D) microwave images of these objects are successfully obtained in terms of the measured wide band scattering data using a novel time domain inverse scattering approach, which shows the applicability of the proposed antenna.

A reduced-size wideband single-feed circularly polarized patch antenna is introduced for telemetry applications in S-band around 2300 MHz. The proposed structure consists of a slot-loaded patch antenna printed over an optimized metamaterial-inspired reactive impedance substrate (RIS). We demonstrate, step by step, the main role of each antenna element by comparing numerically and experimentally the performance of various antenna configurations: antenna over a single- or dual-layer substrate, standard patch or slot-loaded patch, antenna with or without RIS. The final optimized structure exhibits an axial-ratio bandwidth of about 15% and an impedance bandwidth better than 11%, which is much wider than the conventional printed antenna on the same materials.

In this paper, a novel design of coplanar micro strip patch antenna is presented for ultra wideband (UWB) and smart grid applications. It is designed on a dielectric substrate and fed by a coplanar wave guide (CPW). Micro strip patch antenna consists of a circular patch with an elliptical shaped eye opening in the center to provide multiband operations. The antenna has wide bandwidth of 5 GHz between 10.8-15.8 GHz and 3 GHz between 5.8-8.8 GHz, high return loss of -34 dB at 3.9 GHz and -29 dB at 6.8 GHz with satisfactory radiation properties. The parameters that affect the performance of the antenna in terms of its frequency domain characteristics are investigated. The antenna design has been simulated on Ansoft's High Frequency Structure Simulator (HFSS). It is a compact design of 47 × 47 mm2 area on Rogers RO3003 (tm) substrate with dielectric constant of 3 and thickness of 1.6 mm. The simulated antenna has three operating frequency bands at 2.82, 5.82-8.8, 10.8-15.8 GHz and shows the band-notch characteristic in the UWB band to avoid interferences, which is caused by WLAN at 5.15-5.825 GHz and Wi-MAX at 5.25-5.85 GHz. The designed antenna structure with 5.68 dBi gain is planar, simple and compact, hence it can be easily embedded in wireless communication systems and integrated with microwave circuitry for low manufacturing cost.

This communication proposes a wideband differential-fed slot antenna that works in its dominant mode ( λ/2 mode). The antenna has a near omnidirectional radiation pattern with vertical polarization, which is designed for near-ground wireless sensor node applications. A compact integrated T-slot balun and a loop to T-slot feeding structure are introduced to realize the differential feeding; the bandwidth is expanded to 30.7% (2.2 GHz-3.0 GHz) with a 3.8 dB peak gain. A detailed matching method without using lumped elements is proposed, which can be applied to various types of RFIC chips. A prototype is built and measured. Its differential impedance, antenna patterns, and gain are consistent with the simulation results.


Question: A new 60 GHz wideband antenna using coupled half-mode/quarter-mode substrate integrated waveguide design.

This paper presents a cost-effective and fully-integrated antenna-in-package (AiP) solution for 60 GHz phased-array systems, showcasing the integration of sixteen patch antennas within a compact 28 mm × 28 mm ball grid array. This innovative design is complemented by the attachment of a transmitter or receiver IC via flip-chip technology. The AiP is fabricated using low temperature co-fired ceramic (LTCC) technology, known for its reliability and low-loss characteristics at high frequencies. To ensure optimal performance, 60 GHz interconnects, including critical flip-chip transitions and via structures, are meticulously optimized through full-wave simulation techniques. The measured antenna gain, approximately 5 dBi across each unit antenna, is consistent across all four IEEE 802.15.3c channels, demonstrating a high degree of model-to-hardware correlation. The successful integration of the transmitter and receiver ICs within the package has facilitated the establishment of beam-steered, non-line-of-sight links, achieving data rates up to 5.3 Gb/s, thereby validating the effectiveness of this AiP solution for 60 GHz communications.

**Question**: A Hybrid Approach to Detecting Intrusions in Wireless Sensor Networks

**Documents**: An efficient hybrid algorithm for intrusion detection in wireless sensor networks
In this paper, we propose a hybrid approach for detecting intrusions in wireless sensor networks (WSNs). Our approach combines the strengths of both signature-based and anomaly-based detection methods. The signature-based approach uses a database of known attack signatures to identify intrusions, while the anomaly-based approach identifies deviations from normal behavior that may indicate an intrusion. We use a machine learning algorithm to classify network traffic and identify potential intrusions. Our hybrid algorithm is designed to be efficient and scalable, making it suitable for deployment in large-scale WSNs. We evaluate the performance of our algorithm using a dataset of network traffic from a real-world WSN deployment. Our results show that our hybrid approach is able to detect a wide range of intrusions with high accuracy, while minimizing false positives and false negatives.

**Similar Documents**: This paper introduces a novel hybrid algorithm for intrusion detection in wireless sensor networks (WSNs), designed to enhance security by leveraging the benefits of both signature-based and anomaly-based detection techniques. The signature-based component utilizes a comprehensive database of known attack signatures for intrusion identification, whereas the anomaly-based component focuses on detecting deviations from typical network behavior that could signal a security breach. A sophisticated machine learning algorithm is employed to analyze network traffic and flag potential

A center-fed circular microstrip patch antenna with a coupled annular ring is presented. This antenna has a low profile configuration with a monopole like radiation pattern. Compared to the center-fed circular patch antenna (CPA), the proposed antenna has a large bandwidth and similar radiation pattern. The proposed antenna is fabricated and tested. It resonates at 5.8 GHz, the corresponding impedance bandwidth and gain are 12.8% and 5.7 dBi, respectively. Very good agreement between the measurement and simulation for the return loss and radiation patterns is achieved.

The extensive growth of wireless communications industry is creating a big market opportunity. Wireless operators are currently searching for new solutions which would be implemented into the existing wireless communication networks to provide the broader bandwidth, the better quality and new value-added services. In the last decade, most commercial efforts were focused on the 1-10 GHz spectrum for voice and data applications for mobile phones and portable computers (Niknejad & Hashemi, 2008). Nowadays, the interest is growing in applications that use high rate wireless communications. Multigigabit-per-second communication requires a very large bandwidth. The Ultra-Wide Band (UWB) technology was basically used for this issue. However, this technology has some shortcomings including problems with interference and a limited data rate. Furthermore, the 3–5 GHz spectrum is relatively crowded with many interferers appearing in the WiFi bands (Niknejad & Hashemi, 2008). The use of millimeter wave frequency band is considered the most promising technology for broadband wireless. In 2001, the Federal Communications Commission (FCC) released a set of rules governing the use of spectrum between 57 and 66 GHz (Baldwin, 2007). Hence, a large bandwidth coupled with high allowable transmit power equals high possible data rates. Traditionally the implementation of 60 GHz radio technology required expensive technologies based on III-V compound semiconductors such as InP and GaAs (Smulders et al., 2007). The rapid progress of CMOS technology has enabled its application in millimeter wave applications. Currently, the transistors became small enough, consequently fast enough. As a result, the CMOS technology has become one of the most attractive choices in implementing 60 GHz radio due to its low cost and high level of integration (Doan et al., 2005). Despite the advantages of CMOS technology, the design of 60 GHz CMOS transceiver exhibits several challenges and difficulties that the designers must overcome. This chapter aims to explore the potential of the 60 GHz band in the use for emergent generation multi-gigabit wireless applications. The chapter presents a quick overview of the state-of-the-art of 60 GHz radio technology and its potentials to provide for high data rate and short range wireless communications. The chapter is organized as follows. Section 2 presents an overview about 60 GHz band. The advantages are presented to highlight the performance characteristics of this band. The opportunities of the physical layer of the IEEE

Substrate integrated waveguide (SIW) technology provides an attractive solution to the integration of planar and nonplanar circuits by using a planar circuit fabrication process. However, it is usually difficult to implement the negative coupling structure required for the design of compact canonical folded elliptic or quasi-elliptic cross-coupled bandpass filter on the basis of a single-layer SIW. In this paper, a special planar negative coupling scheme including a magnetic coupling post-wall iris and a balanced microstrip line with a pair of metallic via-holes is studied in detail. Two -band fourth-degree cross-coupled bandpass filters without and with source-load coupling using the negative coupling structures are then proposed and designed. The two novel SIW filters having the same center frequency of 20.5 GHz and respective passband width of 700 and 800 MHz are implemented on a single-layer Rogers RT/Duroid 5880 substrate with thickness of 0.508 mm. Measured results of those filters, which exhibit a high selectivity, and a minimum in-band insertion loss of approximately 0.9 and 1.0 dB, respectively, agree well with simulated results.

A compact in size microstrip antenna with multiband resonance features is described in this paper. A circular antenna with the partial ground is used as a primary antenna which resonated for WiMAX band (3.3–3.7 GHz). Further, primary antenna embedded with mushroom shape EBG (Electromagnetic Band Gap) and periodic DGS (Defected Ground Structure) to operate over WLAN band (5.1–5.8 GHz) and X-band from 8–12 GHz. Proposed antenna covers 2.4/3.5/5.5 GHz bands and complete X-band which also reflects that it has novel suitability to use for a ZigBee/Wi-Fi/Bluetooth applications. The proposed antenna is etched on FR-4 substrate and overall dimension is 20×26×1.6 mm3. The projected antenna has wide-ranging bandwidth with VSWR < 2. It provides favorable agreement between measured and simulated results.

A leaky-wave antenna (LWA) with circular polarization based on the composite right/left-handed (CRLH) substrate integrated waveguide (SIW) is investigated and presented. Series interdigital capacitors have been introduced into the circuit by etching the slots on the waveguide surface achieving a CRLH functionality. Two symmetrical leaky traveling-wave transmission lines with orthogonal polarizations are placed side-by-side and excited with 90° phase difference generating a pure circular polarization mode. The main beam of this antenna can be steered continuously by varying the frequency while maintaining a low axial ratio (below 3 dB) within the main beam direction. The performance of this LWA is verified through both full-wave simulation and measurement of a fabricated prototype showing a good agreement.

In this letter design of a novel, wideband and low loss microstrip line transition to ridge gap waveguide is described. The transition is made up of two sections. The first section is a microstrip line that feeds a slot on the ground plane. The microstrip line is placed inside a metal box. The second part is the slot to ridge gap waveguide matching section. The proposed design has a broadband coupling to ridge gap waveguide and covers the whole stop-band of the periodic gap structure. The design optimization is performed for the microstrip line, slot size and ridge gap waveguide matching ridges. A prototype in Ku-band is manufactured and measured that shows a return loss less than 14 dB and a back-to-back insertion loss smaller than 0.5 dB for the given bandwidth. The microstrip transition enables integration of monolithic microwave integrated circuits (MMICs) in ridge gap waveguide technology.

This letter investigates dual-band and wideband designs of dual-polarized cylindrical dielectric resonator antennas (DRAs). These designs make use of the fundamental HEM111 mode and higher-order HEM113 mode of the DRA. The strip- and slot-fed excitation methods are used for Ports 1 and 2 of the antennas, respectively. For demonstration, a dual-band dual-polarized DRA for DCS (1.71-1.88 GHz) and WLAN (2.4-2.48 GHz) bands and a wideband version that covers the 2.4-GHz WLAN band were designed. The S-parameters, radiation patterns, antenna gains, antenna efficiencies, and envelope correlations of the two designs are studied. It was found that the dual-band and wideband designs have port isolations of higher than 36 and 37 dB, respectively. Good agreement between the measured and simulated results is observed.

Abstract—The paper presents integrated probe for direct coupling to the WR-10 waveguide with the use of metal filled vias on both sides of the microstrip line. Design and optimization of this novel microstripto-waveguide transition has been performed using 3-D finite element method based software HFSS (High Frequency Structure Simulator). A back-to-back transition has been fabricated and measured between 75–110GHz. The measured return loss is higher than 10 dB and the insertion loss for a single microstrip-to-waveguide transition is about 1.15 dB.

A metamaterial-based broadband low-profile mushroom antenna is presented. The proposed antenna is formed using an array of mushroom cells and a ground plane, and fed by a microstrip line through a slot cut onto the ground plane. With the feeding slot right underneath the center gap between the mushroom cells, the dual resonance modes are excited simultaneously for the radiation at boresight. A transmission-line model integrated with the dispersion relation of a composite right/left-handed mushroom structure is applied to analyze the modes. The proposed dielectric-filled (εr=3.38) mushroom antenna with a low profile of 0.06λ0 ( λ0 is the operating wavelength in free space) and a ground plane of 1.10λ0×1.10λ0 attains 25% measured bandwidth with(|S11| <; - 10dB) 9.9-dBi average gain at 5-GHz band. Across the bandwidth, the antenna efficiency is greater than 76%, and cross-polarization levels are less than -20 dB.

A combination of slots and series-fed patch antenna array is introduced leading to an array antenna with wide bandwidth, low sidelobe level (SLL), and high front-to-back ratio (F/B). Three structures are analyzed. The first, the reference structure, is the conventional series-fed microstrip antenna array that operates at 16.26 GHz with a bandwidth of 3%, SLL of -24 dB, and F/B of 23 dB. The second is similar to the reference structure, but a large slot that covers the patch array is removed from the ground plane. This results in a wide operating bandwidth of over 72% for , with a 3 dB gain bandwidth of over 15.9%. The SLL of this structure is improved to more than at 16.26 GHz, and stable SLL of over 15.5-16.4 GHz band is also exhibited. This structure has a bi-directional radiation pattern. To make the pattern uni-directional and increase the F/B, a third structure is analyzed. In this structure, rather than one single large slot, an array of slots is used and a reflector is placed above the series-fed patch array. This increases the F/B to 40 dB. The simulated and measured results are presented and discussed.


Question: A new wideband coupled half-mode/quarter-mode substrate integrated waveguide antenna at 60 GHz.

This paper delves into the pivotal role wearable technology plays in enhancing healthcare and the quality of life. It begins by outlining the pressing challenges in contemporary medicine, emphasizing the necessity for preventive technological innovations. The focus then shifts to the development of the Wearable Motherboard™, also known as the Smart Shirt, which serves as a versatile platform for integrating various sensors and monitoring devices. This technology enables discreet, real-time health monitoring for individuals, both in-person and remotely. The paper proceeds to explore the multifaceted applications and transformative impact of this wearable technology across the lifespan, from preventing Sudden Infant Death Syndrome (SIDS) to empowering senior citizens to live independently. Concluding the discussion, the paper forecasts future advancements in wearable systems that are both comfortable and functional, with the ultimate goal of revolutionizing healthcare and significantly improving human well-being.

**Question**: An Efficient Multi-Objective Optimization Algorithm for Power System Operation

**Documents**: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The concept of elite strategy is adopted in HGAPSO, where the upper-half of the best-performing individuals in a population are regarded as elites. However, instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation operation on these enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design as follows. For recurrent neural network, a fully connected recurrent neural network is designed and applied to a temporal sequence production problem. For recurrent fuzzy network design, a Takagi-Sugeno-Kang-type recurrent fuzzy network is designed and applied to dynamic plant control. The performance of HGAPSO is compared to both GA and PSO in these recurrent networks design problems, demonstrating its superiority.

**Similar Documents**: This paper introduces the Hybrid Genetic

In this paper, a design of Substrate Intergrated Waveguide (SIW) Phase Shifter is designed by placing multiple rows of air holes in a single substrate which modifies effective dielectric constant of the material and as a result, the phase of the propagating wave is modified while maintaining insertion loss less than 0.8 dB. The proposed design operates in X band (8-12 GHz) having reflection coefficient better than -15 dB within a band of 3 GHz (31.5%) and a phase shift of 24.8°±4.7° throughout the operating band.

A new CPW to waveguide transition is developed based on the concept of tapered slot antenna and E-plane probe coupling. The transition consists of a tapered slotline probe and a slotline to CPW matching section. The current design has the advantages of broad bandwidth, compact size, low fabrication cost, and high reliability. The characteristics of a prototype transition are investigated by numerical simulation on Duroid substrate and WR-90 waveguide. The back-to-back combination is measured to verify the agreement with the simulated results and realization of this design.

Tightly coupled phased arrays (TCPAs) provide UWB performance due to their strong inter-element coupling. However, in finite tightly coupled phased array realizations, mutual coupling is reduced near the array edges, causing the edge elements to become narrowband. To address this issue, a nonuniform array excitation scheme, referred to as “characteristic mode (CM) excitation,” was recently proposed. A more practical alternative for designing UWB tightly coupled phased arrays is proposed here. In this paper, we present a strategy that employs uniform excitation of the central array elements and short-circuits the periphery elements. We report that at least for medium size arrays, this approach provides up to 3 dB more gain and 50% higher efficiency than typical resistive termination. This concept is demonstrated using a 7 × 7 linearly polarized dipole array, 60.96 cm × 60.96 cm (2' × 2') in size, for operation from 200 MHz-600 MHz. Feeding of the active elements is challenging due to several constraints on the feed design, including balanced to unbalanced transitions, impedance transformations, common mode suppression, compact size, low cost, etc. To address these issues we propose a novel array feed using a compact, ultrawideband balun (with 10:1 bandwidth for VSWR ). Simulated and measured data are provided for broadside and 30 scan in the H-plane.

This communication proposes a wideband differential-fed slot antenna that works in its dominant mode ( λ/2 mode). The antenna has a near omnidirectional radiation pattern with vertical polarization, which is designed for near-ground wireless sensor node applications. A compact integrated T-slot balun and a loop to T-slot feeding structure are introduced to realize the differential feeding; the bandwidth is expanded to 30.7% (2.2 GHz-3.0 GHz) with a 3.8 dB peak gain. A detailed matching method without using lumped elements is proposed, which can be applied to various types of RFIC chips. A prototype is built and measured. Its differential impedance, antenna patterns, and gain are consistent with the simulation results.

This communication presents a wideband circularly polarized (CP) 2 × 2 patch array using a sequential-phase feeding network. By combining three operating modes, both axial ratio (AR) and impedance bandwidths are enhanced and wider than those of previous published sequential-fed single-layer patch arrays. These three CP operating modes are tuned and matched by optimizing the truncated corners of patch elements and the sequential-phase feeding network. A prototype of the proposed patch array is built to validate the design experimentally. The measured -10-dB impedance bandwidth is 1.03 GHz (5.20-6.23 GHz), and the measured 3-dB AR bandwidth is 0.7 GHz (5.25-5.95 GHz), or 12.7% corresponding to the center frequency of 5.5 GHz. The measured peak gain is about 12 dBic and the gain variation is less than 3 dB within the AR bandwidth.

In this letter, a slotline to substrate integrated waveguide transition is proposed for the development of substrate integrated circuits. The insertion loss of the back-to-back transition is less than 1 dB from 8.7 to 9.0 GHz. With this transition, a planar magic-T is studied and designed. Measured results indicate a very good performance of the fabricated magic-T is observed within the experimental frequency range of 8.4-9.4 GHz. The amplitude and phase imbalances are less than 0.2 dB and 1.5deg, respectively.

We examine the current development of highly integrated 60-GHz radios with an interest in antenna-circuit interfaces. We design and analyze grid array antennas with special attention to the differential feeding and the patterned ground plane. More importantly, we integrate two grid array antennas in a package; propose the way of assembling it to the system printed circuit board; and demonstrate a total solution of low cost and thin profile to highly integrated 60-GHz radios. We show that the package in low temperature cofired ceramic (LTCC) technology measures only 13×13×0.575 mm3 ; can carry a 60-GHz radio die of current and future sizes with flip-chip bonding; and achieves good antenna performance in the 60-GHz band with maximum gain of 13.5 and 14.5 dBi for the single-ended and differential antennas, respectively.

A metamaterial-based broadband low-profile mushroom antenna is presented. The proposed antenna is formed using an array of mushroom cells and a ground plane, and fed by a microstrip line through a slot cut onto the ground plane. With the feeding slot right underneath the center gap between the mushroom cells, the dual resonance modes are excited simultaneously for the radiation at boresight. A transmission-line model integrated with the dispersion relation of a composite right/left-handed mushroom structure is applied to analyze the modes. The proposed dielectric-filled (εr=3.38) mushroom antenna with a low profile of 0.06λ0 ( λ0 is the operating wavelength in free space) and a ground plane of 1.10λ0×1.10λ0 attains 25% measured bandwidth with(|S11| <; - 10dB) 9.9-dBi average gain at 5-GHz band. Across the bandwidth, the antenna efficiency is greater than 76%, and cross-polarization levels are less than -20 dB.

In this communication, a novel wideband magnetoelectric (ME) dipole antenna is proposed and investigated. A microstrip line aperture-coupled feeding structure is used to excite the antenna. This simple feeding structure can significantly enhance the impedance bandwidth. An antenna prototype is fabricated and exemplified to validate the proposed concept. Measured results show that the proposed ME dipole antenna with microstrip line aperture-coupled excitation achieves an impedance bandwidth of 85% for VSWR < 2 from 2.5 to 6.2 GHz. Further, a measured maximum boresight gain up to 8 dBi is obtained and a 1 dB gain bandwidth is about 67% from 2.5 to 5 GHz. The antenna radiation patterns perform well over the 1 dB gain bandwidth range. A low cross-polarization level is obtained over the operating band.

A compact in size microstrip antenna with multiband resonance features is described in this paper. A circular antenna with the partial ground is used as a primary antenna which resonated for WiMAX band (3.3–3.7 GHz). Further, primary antenna embedded with mushroom shape EBG (Electromagnetic Band Gap) and periodic DGS (Defected Ground Structure) to operate over WLAN band (5.1–5.8 GHz) and X-band from 8–12 GHz. Proposed antenna covers 2.4/3.5/5.5 GHz bands and complete X-band which also reflects that it has novel suitability to use for a ZigBee/Wi-Fi/Bluetooth applications. The proposed antenna is etched on FR-4 substrate and overall dimension is 20×26×1.6 mm3. The projected antenna has wide-ranging bandwidth with VSWR < 2. It provides favorable agreement between measured and simulated results.


Question: Multi-level topic-based text classification using Wikipedia.

This paper explores the application of Support Vector Machines (SVMs) designed for one-class classification in information retrieval tasks. The study was carried out using the widely recognized Reuters dataset. Two SVM implementations were utilized—one based on Schölkopf et al.'s approach and another that identifies "outlier" data as a representation of the second class. Various kernel functions were tested for both SVM implementations, alongside different data representations, including binary vectors, tf-idf, and a novel "Hadamard" representation. Comparative analyses were conducted against one-class versions of the Rocchio algorithm, nearest neighbor, naive Bayes, and a specialized neural network classification method based on "bottleneck" compression filters. The results showed that Schölkopf's SVM variant outperformed all methods except the neural network approach, where it exhibited comparable performance, albeit occasionally inferior. However, the SVM methods proved to be highly sensitive to the choice of representation and kernel, an aspect that remains poorly understood. Consequently, for the time being, the neural network technique appears to be the most reliable option.

**Question**: A Comparative Study of Feature Selection Methods for Sentiment Analysis

**Documents**: A Comparative Study on Feature Selection for Sentiment Analysis using Machine Learning
This paper presents a comparative study on feature selection techniques for sentiment analysis using machine learning algorithms. The feature selection techniques used are Chi-Square, Information Gain, and ReliefF. The machine learning algorithms used are Naive Bayes, Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN). The study was conducted on two datasets, one from Twitter and the other from Yelp. The results show that the Information Gain feature selection technique combined with the SVM machine learning algorithm yields the best performance for sentiment analysis. However, the performance of the feature selection techniques varies with the machine learning algorithm and the dataset used. The study also shows that the performance of the feature selection techniques is affected by the size of the dataset and the number of features selected. The paper concludes with a discussion on the limitations of the study and future work.

**Similar Documents**: This study undertakes an extensive comparison of feature selection methods applied to sentiment analysis tasks, utilizing machine learning algorithms. The investigation focuses on three prominent feature selection techniques: Chi-Square, Information Gain, and ReliefF. These methods were evaluated alongside three machine learning algorithms: Naive Bayes, Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN). Two distinct datasets were employed for the analysis—one sourced from Twitter

We present a non-traditional retrieval problem we call subtopic retrieval. The subtopic retrieval problem is concerned with finding documents that cover many different subtopics of a query topic. In such a problem, the utility of a document in a ranking is dependent on other documents in the ranking, violating the assumption of independent relevance which is assumed in most traditional retrieval methods. Subtopic retrieval poses challenges for evaluating performance, as well as for developing effective algorithms. We propose a framework for evaluating subtopic retrieval which generalizes the traditional precision and recall metrics by accounting for intrinsic topic difficulty as well as redundancy in documents. We propose and systematically evaluate several methods for performing subtopic retrieval using statistical language models and a maximal marginal relevance (MMR) ranking strategy. A mixture model combined with query likelihood relevance ranking is shown to modestly outperform a baseline relevance ranking on a data set used in the TREC interactive track.

A goodtext classifieris a classifierthat efficiently categorizeslarge setsof text documentsin a reasonabletime frameand with an acceptableaccuracy, and that provides classificationrules that are humanreadablefor possible fine-tuning. If the training of the classifier is also quick, this couldbecomein someapplicationdomainsa goodasset for the classifier . Many techniquesand algorithmsfor automatictext categorizationhavebeendevised.According to publishedliterature, somearemoreaccuratethanothers, andsomeprovidemore interpretableclassificationmodels thanothers. However, nonecancombineall thebeneficial propertiesenumer atedabove. In this paper, we presenta novel approach for automatictext categorizationthat borrowsfrom market basket analysistechniquesusingassociation rule miningin thedata-miningfield. We focuson two major problems:(1) findingthebesttermassociationrules in a textual databaseby generating and pruning; and (2) using the rules to build a text classifier . Our text categorization methodprovesto beefficientandeffective, andexperimentsonwell-knowncollectionsshowthattheclassifier performswell. In addition,trainingaswell asclassification arebothfastandthegeneratedrulesarehumanreadable.

We consider the problem of semi-supervised learning to extract categories (e.g., academic fields, athletes) and relations (e.g., PlaysSport(athlete, sport)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semi-supervised training using only a few labeled examples is typically unreliable because the learning task is underconstrained. This paper pursues the thesis that much greater accuracy can be achieved by further constraining the learning task, by coupling the semi-supervised training of many extractors for different categories and relations. We characterize several ways in which the training of category and relation extractors can be coupled, and present experimental results demonstrating significantly improved accuracy as a result.

Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome.

Human languages are naturally ambiguous, which makes it difficult to automatically understand the semantics of text. Most vector space models (VSM) treat all occurrences of a word as the same and build a single vector to represent the meaning of a word, which fails to capture any ambiguity. We present sense-aware semantic analysis (SaSA), a multi-prototype VSM for word representation based on Wikipedia, which could account for homonymy and polysemy. The “sense-specific” prototypes of a word are produced by clustering Wikipedia pages based on both local and global contexts of the word in Wikipedia. Experimental evaluation on semantic relatedness for both isolated words and words in sentential contexts and word sense induction demonstrate its effectiveness.

With its rapid users growth, Twitter has become an essential source of information about what events are happening in the world. It is critical to have the ability to derive the topics from Twitter messages (tweets), that is, to determine and characterize the main topics of the Twitter messages (tweets). However, tweets are very short in nature and therefore the frequency of term co-occurrences is very low. The sparsity in the relationship between tweets and terms leads to a poor characterization of the topics when only the content of the tweets is used. In this paper, we exploit the relationships between tweets and propose intLDA, a variant of Latent Dirichlet Allocation (LDA) that goes beyond content and directly incorporates the relationship between tweets. We have conducted experiments on a Twitter dataset and evaluated the performance in terms of both topic coherence and tweettopic accuracy. Our experiments show that intLDA outperforms methods that do not use relationship information. Keywords-Topic Derivation; Twitter; Tweets Relationship;

Processing language requires the retrieval of concepts from memory in response to an ongoing stream of information. This retrieval is facilitated if one can infer the gist of a sentence, conversation, or document and use that gist to predict related concepts and disambiguate words. This article analyzes the abstract computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference. This leads to a novel approach to semantic representation in which word meanings are represented in terms of a set of probabilistic topics. The topic model performs well in predicting word association and the effects of semantic association and ambiguity on a variety of language-processing and memory tasks. It also provides a foundation for developing more richly structured statistical models of language, as the generative process assumed in the topic model can easily be extended to incorporate other kinds of semantic and syntactic structure.

We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], and Hofmann's aspect model , also known as probabilistic latent semantic indexing (pLSI) [3]. In the context of text modeling, our model posits that each document is generated as a mixture of topics, where the continuous-valued mixture proportions are distributed as a latent Dirichlet random variable. Inference and learning are carried out efficiently via variational algorithms. We present empirical results on applications of this model to problems in text modeling, collaborative filtering, and text classification.

While Named Entity extraction is useful in many natural language applications, the coarse categories that most NE extractors work with prove insufficient for complex applications such as Question Answering and Ontology generation. We examine one coarse category of named entities, persons, and describe a method for automatically classifying person instances into eight finergrained subcategories. We present a supervised learning method that considers the local context surrounding the entity as well as more global semantic information derived from topic signatures and WordNet. We reinforce this method with an algorithm that takes advantage of the presence of entities in multiple contexts.

Databases of text and text-annotated data constitute a significant fraction of the information available in electronic form. Searching and browsing are the typical ways that users locate items of interest in such databases. Faceted interfaces represent a new powerful paradigm that proved to be a successful complement to keyword searching. Thus far, the identification of the facets was either a manual procedure, or relied on apriori knowledge of the facets that can potentially appear in the underlying collection. In this paper, we present an unsupervised technique for automatic extraction of facets useful for browsing text databases. In particular, we observe, through a pilot study, that facet terms rarely appear in text documents, showing that we need external resources to identify useful facet terms. For this, we first identify important phrases in each document. Then, we expand each phrase with ";context"; phrases using external resources, such as WordNet and Wikipedia, causing facet terms to appear in the expanded database. Finally, we compare the term distributions in the original database and the expanded database to identify the terms that can be used to construct browsing facets. Our extensive user studies, using the Amazon Mechanical Turk service, show that our techniques produce facets with high precision and recall that are superior to existing approaches and help users locate interesting items faster.


Question: Multi-level topical classification of text using Wikipedia as a reference.

We introduce a probabilistic generative model for collections of discrete data, particularly suited for text, that refines and extends several earlier models, including the naive Bayes/unigram model, the mixture of unigrams model [6], and Hofmann's aspect model, also known as probabilistic latent semantic indexing (pLSI) [3]. Specifically, in the domain of text modeling, our model assumes that each document is a composite of topics, with the proportions of these topics being modeled as latent Dirichlet random variables, which are continuous. Variational algorithms are utilized for efficient inference and learning within this model. We validate the effectiveness of this model through empirical studies on text modeling, collaborative filtering, and text categorization tasks, demonstrating its superiority over prior approaches.

**Question**: A Comparative Study of Deep Learning Techniques for Image Recognition

**Documents**: Convolutional Neural Networks for Image Recognition
Convolutional Neural Networks (CNNs) have emerged as a powerful tool for image recognition, achieving state-of-the-art performance on a variety of benchmarks. This paper provides a comprehensive review of the recent advancements in CNNs for image recognition tasks. We discuss the key architectural components of CNNs, including convolutional layers, pooling layers, and fully connected layers, and explain how they contribute to the network's ability to recognize complex patterns in images. We also explore various techniques for improving the performance of CNNs, such as data augmentation, dropout, and batch normalization. The paper concludes with an analysis of the latest trends in CNN research and future directions for the field.

**Similar Documents**: This paper offers an in-depth analysis of Convolutional Neural Networks (CNNs) as a leading method for image recognition, highlighting their exceptional performance on diverse benchmark datasets. We delve into the foundational architecture of CNNs, examining the roles of convolutional layers, pooling layers, and fully connected layers in discerning intricate patterns within images. Additionally, we review strategies aimed at enhancing CNN performance, including data augmentation, dropout, and batch normalization. The paper culminates in an examination of the most recent developments in CNN research and speculates on potential future innovations in this rapidly evolving field.

Expertise retrieval has been largely unexplored on data other than the W3C collection. At the same time, many intranets of universities and other knowledge-intensive organisations offer examples of relatively small but clean multilingual expertise data, covering broad ranges of expertise areas. We first present two main expertise retrieval tasks, along with a set of baseline approaches based on generative language modeling, aimed at finding expertise relations between topics and people. For our experimental evaluation, we introduce (and release) a new test set based on a crawl of a university site. Using this test set, we conduct two series of experiments. The first is aimed at determining the effectiveness of baseline expertise retrieval methods applied to the new test set. The second is aimed at assessing refined models that exploit characteristic features of the new test set, such as the organizational structure of the university, and the hierarchical structure of the topics in the test set. Expertise retrieval models are shown to be robust with respect to environments smaller than the W3C collection, and current techniques appear to be generalizable to other settings.

PurposeThe immense quantity of available unstructured text documents serve as one of the largest source of information. Text classification can be an essential task for many purposes in information retrieval, such as document organization, text filtering and sentiment analysis. Ensemble learning has been extensively studied to construct efficient text classification schemes with higher predictive performance and generalization ability. Providing diversity among the classification algorithms is a key issue in the ensemble design. Design/methodology/approachAn ensemble scheme based on hybrid supervised clustering is presented for text classification. In the presented scheme, supervised hybrid clustering, which is based on cuckoo search algorithm and k-means, is introduced to partition the data samples of each class into clusters so that training subsets with higher diversities can be provided. Each classifier is trained on the diversified training subsets and the predictions of individual classifiers are combined by the majority voting rule. The predictive performance of the proposed classifier ensemble is compared to conventional classification algorithms (such as Naïve Bayes, logistic regression, support vector machines and C4.5 algorithm) and ensemble learning methods (such as AdaBoost, Bagging and Random Subspace) using eleven text benchmarks. FindingThe experimental results indicate that the presented classifier ensemble outperforms the conventional classification algorithms and ensemble learning methods for text classification. Originality/valueThe presented ensemble scheme is the first to use supervised clustering to obtain diverse ensemble for text classification

Image annotation, the task of automatically generating description words for a picture, is a key component in various image search and retrieval applications. Creating image databases for model development is, however, costly and time consuming, since the keywords must be hand-coded and the process repeated for new collections. In this work we exploit the vast resource of images and documents available on the web for developing image annotation models without any human involvement. We describe a probabilistic model based on the assumption that images and their co-occurring textual data are generated by mixtures of latent topics. We show that this model outperforms previously proposed approaches when applied to image annotation and the related task of text illustration despite the noisy nature of our dataset.

Twitter is widely used all over the world, and a huge number of hot topics are generated by Twitter users in real time. These topics are able to reflect almost every aspect of people’s daily lives. Therefore, the detection of topics in Twitter can be used in many real applications, such as monitoring public opinion, hot product recommendation and incidence detection. However, the performance of traditional topic detection methods is still far from perfect largely owing to the tweets’ features, such as their limited length and arbitrary abbreviations. To address these problems, we propose a novel framework (MVTD) for Twitter topic detection using multiview clustering, which can integrate multirelations among tweets, such as semantic relations, social tag relations and temporal relations. We also propose some methods for measuring relations among tweets. In particular, to better measure the semantic similarity of tweets, we propose a new document similarity measure based on a suffix tree (STVSM). In addition, a new keyword extraction method based on a suffix tree is proposed. Experiments on real datasets show that the performance of MVTD is much better than that of a single view, and it is useful for detecting topics from Twitter.

While the Web of data is attracting increasing interest and rapidly growing in size, the major support of information on the surface Web are still multimedia documents. Semantic annotation of texts is one of the main processes that are intended to facilitate meaning-based information exchange between computational agents. However, such annotation faces several challenges such as the heterogeneity of natural language expressions, the heterogeneity of documents structure and context dependencies. While a broad range of annotation approaches rely mainly or partly on the target textual context to disambiguate the extracted entities, in this paper we present an approach that relies mainly on formalized-knowledge expressed in RDF datasets to categorize and disambiguate noun phrases. In the proposed method, we represent the reference knowledge bases as co-occurrence matrices and the disambiguation problem as a 0-1 Integer Linear Programming (ILP) problem. The proposed approach is unsupervised and can be ported to any RDF knowledge base. The system implementing this approach, called KODA, shows very promising results w.r.t. state-of-the-art annotation tools in cross-domain experimentations.

The increased popularity of tagging during the last few years can be mainly attributed to its embracing by most of the recently thriving user-centric content publishing and management Web 2.0 applications. However, tagging systems have some limitations that have led researchers to develop methods that assist users in the tagging process, by automatically suggesting an appropriate set of tags. We have tried to model the automated tag suggestion problem as a multilabel text classification task in order to participate in the ECML/PKDD 2008 Discovery Challenge.

For many domains, structured knowledge is in short supply, while unstructured text is plentiful. Knowledge Base Population (KBP) is the task of building or extending a knowledge base from text, and systems for KBP have grown in capability and scope. However, existing datasets for KBP are all limited by multiple issues: small in size, not open or accessible, only capable of benchmarking a fraction of the KBP process, or only suitable for extracting knowledge from title-oriented documents (documents that describe a particular entity, such as Wikipedia pages). We introduce and release CC-DBP, a web-scale dataset for training and benchmarking KBP systems. The dataset is based on Common Crawl as the corpus and DBpedia as the target knowledge base. Critically, by releasing the tools to build the dataset, we enable the dataset to remain current as new crawls and DBpedia dumps are released. Also, the modularity of the released tool set resolves a crucial tension between the ease that a dataset can be used for a particular subtask in KBP and the number of different subtasks it can be used to train or benchmark.

Categorization of documents is challenging, as the number o f discriminating words can be very large. We present a nearest neighbor classification scheme for text categoriz ation in which the importance of discriminating words is learned using mutual information and weight adjustment tec hniques. The nearest neighbors for a particular document are then computed based on the matching words and their weigh ts. We evaluate our scheme on both synthetic and real world documents. Our experiments with synthetic data sets s how that this scheme is robust under different emulated conditions. Empirical results on real world documents demo nstrate that this scheme outperforms state of the art classification algorithms such as C4.5, RIPPER, Rainbow, an d PEBLS.

In recent years we have seen a tremendous growth in the volume of text documents available on the Internet, digital libraries, news sources, and company-wide intrane ts. Automatic text categorization, which is the task of assigning text documents to pre-specified classes (topics o r themes) of documents, is an important task that can help both in organizing as well as in finding information on these h uge resources. Text categorization presents unique challenges due to the large number of attributes present in t he data set, large number of training samples, and attribute dependencies. In this paper we focus on a simple linear-time centroid-based document classification algorithm, that despite its simplicity and robust performance, has not been extensively studied and analyzed. Our extensive experiments show that this centroid-based classifier consi stently and substantially outperforms other algorithms su ch as Naive Bayesian, k-nearest-neighbors, and C4.5, on a wide range of datasets. O ur analysis shows that the similarity measure used by the centroid-based scheme allows it to class ify new document based on how closely its behavior matches the behavior of the documents belonging to differen t classes, as measured by the average similarity between the documents. This matching allows it to dynamically adjus t for classes with different densities. Furthermore, our analysis shows that the similarity measure of the centroidbased scheme accounts for dependencies between the terms in the different classes. We believe that this feature is the reason why it consistently outperforms other classifiers th at cannot take these dependencies into account.

This paper describes how we managed to use the WordNet ontology for the GeoCLEF 2005 English monolingual task. Both a query expansion method, based on the expansion of geographical terms by means of WordNet synonyms and meronyms, and a method based on the expansion of


Question: Multi-layered topical text classification using Wikipedia.

This paper explores the initial findings on identifying Twitter users' areas of interest through an analysis of the entities they reference in their tweets. Twitter, a microblogging platform, enables users to share short, often informal updates about their daily activities. These updates are known as "tweets." Our method involves utilizing a knowledge base to clarify and classify the entities mentioned within these tweets. Subsequently, we generate a "topic profile" for each user, which encapsulates their primary interests, by identifying recurring categories that encompass the mentioned entities. Our preliminary results show a promising ability to accurately pinpoint the key interests of the users in our study, demonstrating the effectiveness of our approach even at this early stage.

In recent years, many deep-learning based models are proposed for text classification. This kind of models well fits the training set from the statistical point of view. However, it lacks the capacity of utilizing instance-level information from individual instances in the training set. In this work, we propose to enhance neural network models by allowing them to leverage information from k-nearest neighbor (kNN) of the input text. Our model employs a neural network that encodes texts into text embeddings. Moreover, we also utilize k-nearest neighbor of the input text as an external memory, and utilize it to capture instance-level information from the training set. The final prediction is made based on features from both the neural network encoder and the kNN memory. Experimental results on several standard benchmark datasets show that our model outperforms the baseline model on all the datasets, and it even beats a very deep neural network model (with 29 layers) in several datasets. Our model also shows superior performance when training instances are scarce, and when the training set is severely unbalanced. Our model also leverages techniques such as semi-supervised training and transfer learning quite well.

In this paper, we describe an end-to-end system that automatically extracts RDF triples describing entity relations and properties from unstructured text. This system is based on a pipeline of text processing modules that includes a semantic parser and a coreference solver. By using coreference chains, we group entity actions and properties described in different sentences and convert them into entity triples. We applied our system to over 114,000 Wikipedia articles and we could extract more than 1,000,000 triples. Using an ontology-mapping system that we bootstrapped using existing DBpedia triples, we mapped 189,000 extracted triples onto the DBpedia namespace. These extracted entities are available online in the N-Triple format1.

Our inspiration comes from Nell (Never Ending Language Learning), a computer program running at Carnegie Mellon University to extract structured information from unstructured web pages. We consider the problem of semi-supervised learning approach to extract category instances (e.g. country(USA), city(New York)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semisupervised approaches using a small number of labeled examples together with many unlabeled examples are often unreliable as they frequently produce an internally consistent, but nevertheless, incorrect set of extractions. We believe that this problem can be overcome by simultaneously learning independent classifiers in a new approach named Coupled Bayesian Sets algorithm, based on Bayesian Sets, for many different categories and relations (in the presence of an ontology defining constraints that couple the training of these classifiers). Experimental results show that simultaneously learning a coupled collection of classifiers for random 11 categories resulted in much more accurate extractions than training classifiers through original Bayesian Sets algorithm, Naive Bayes, BaS-all and Coupled Pattern Learner (the category extractor used in NELL).

In this paper we have presented a brief current state of the Art for Arabic text representation and classification methods. First we describe some algorithms applied to classification on Arabic text. Secondly, we cite all major works when comparing classification algorithms applied on Arabic text, after this, we mention some authors who proposing new classification methods and finally we investigate the impact of preprocessing on Arabic TC.

A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying "hot topics" by examining temporal dynamics and tagging abstracts to illustrate semantic content.

Asynchronous conversations are conversations where participants collaborate with each other at different times (e.g., email, blog, forum). The huge amount of textual data generated everyday in these conversations calls for automated methods of conversational text analysis. Topic segmentation and labeling is often considered a prerequisite for higher-level conversation analysis and has been shown to be useful in many NLP applications including summarization, information extraction and conversation visualization. Topic segmentation in asynchronous conversation refers to the task of clustering the sentences into a set of coherent topical clusters. In [6, 7], we presented unsupervised and supervised topic segmentation models for asynchronous conversations, which to our knowledge are the state-of-the-art. This study concerns topic labeling, that is, given a set of topical clusters in a conversation, the task is to assign appropriate topic labels to the clusters. For example, five topic labels in a Slashdot [2] blog conversation about releasing a new game called Daggerfall are game contents and size, game design, bugs/faults, other gaming options and performance/speed issues. Such topic labels can serve as a concise summary of the conversation and can also be used for indexing. Ideally, topic labels should be meaningful, semantically similar to the underlying topic, general (i.e., broad coverage of the topic) and discriminative (or exclusive) when there are multiple topics [10]. Traditionally, the top K terms in a multinomial topic model (e.g., LDA [3]) are used to represent a topic. However, as pointed out by [10], at the word-level, topic labels may become too general that it can impose cognitive difficulties on a user to interpret the meaning of the topic by associating the words together. On the other hand, if the labels are expressed at the sentence-level, they may become too specific to cover the whole theme of the topic. Based on these observations, [10] and other recent studies (e.g., [9]) advocate for phrase-level topic labels. This is also consistent with the monologue corpora built as part of the Topic Detection and Tracking (TDT) project [1] as well as with our own email and blog conversational corpora in which human annotators without specific instructions spontaneously generated labels at the phrase-level. Considering all these factors, we also treat phrase-level as the right level of granularity for a label in this work. Few prior studies have addressed the topic labeling problem in different settings [10, 9]. Common to their approaches is that they first mine topics in the form of topic-word distributions from the whole corpus using topic models like LDA. Then, they try to label the topics (i.e., topic-word distributions) with an appropriate label using the statistical association metrics (e.g., point-wise mutual information, t-test) computed from either the source corpus or an external knowledge base (e.g., Wikipedia). In contrast, our task is to label the topical clusters in a given conversation, where topics are closely related and distributional variations are subtle (e.g., game contents and size, game design). Therefore, corpus-based statistical association metrics are not reliable in our case. Also at the conversation-level, the topics are too specific to find their labels in an external source. To our knowledge, none has studied this problem before. Therefore, there is no standard corpus and no agreed-upon evaluation metrics available. Our contributions aim to remedy these problems. First, we present a blog and an email corpora annotated with topics. Second, we propose to generate topic labels using an extractive approach, that finds the most representative phrases from the text without relying on an external source. Since, graph-based key phrase ranking has proved to be the state-of-the-art (unsupervised) method [11], we adopt the same framework. We propose a novel biased random walk model that exploits the fact that the leading sentences in a topic often carry the most informative clues for its label. However, the phrases extracted only by considering the sentences in a topic may ignore the global aspect of the conversation. As another contribution, we propose to re-rank the phrases extracted from the whole conversation with respect to the individual topics and include the relevant ones. Experimental results show that our approach outperforms other ranking models including a general random walk model (i.e., TextRank) proposed by [11], a leadoriented model and a frequency-based model, and including the relevant conversation-level phrases improves the performance.

We consider the problem of semi-supervised learning to extract categories (e.g., academic fields, athletes) and relations (e.g., PlaysSport(athlete, sport)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semi-supervised training using only a few labeled examples is typically unreliable because the learning task is underconstrained. This paper pursues the thesis that much greater accuracy can be achieved by further constraining the learning task, by coupling the semi-supervised training of many extractors for different categories and relations. We characterize several ways in which the training of category and relation extractors can be coupled, and present experimental results demonstrating significantly improved accuracy as a result.

The popularity of distance education programs is increasing at a fast pace. En par with this development, online communication in fora, social media and reviewing platforms between students is increasing as well. Exploiting this information to support fellow students or institutions requires to extract the relevant opinions in order to automatically generate reports providing an overview of pros and cons of different distance education programs. We report on an experiment involving distance education experts with the goal to develop a dataset of reviews annotated with relevant categories and aspects in each category discussed in the specific review together with an indication of the sentiment. Based on this experiment, we present an approach to extract general categories and specific aspects under discussion in a review together with their sentiment. We frame this task as a multi-label hierarchical text classification problem and empirically investigate the performance of different classification architectures to couple the prediction of a category with the prediction of particular aspects in this category. We evaluate different architectures and show that a hierarchical approach leads to superior results in comparison to a flat model which makes decisions independently. This work has been performed while the first and last authors were at Bielefeld University.

To take the first step beyond keyword-based search toward entity-based search, suitable token spans ("spots") on documents must be identified as references to real-world entities from an entity catalog. Several systems have been proposed to link spots on Web pages to entities in Wikipedia. They are largely based on local compatibility between the text around the spot and textual metadata associated with the entity. Two recent systems exploit inter-label dependencies, but in limited ways. We propose a general collective disambiguation approach. Our premise is that coherent documents refer to entities from one or a few related topics or domains. We give formulations for the trade-off between local spot-to-entity compatibility and measures of global coherence between entities. Optimizing the overall entity assignment is NP-hard. We investigate practical solutions based on local hill-climbing, rounding integer linear programs, and pre-clustering entities followed by local optimization within clusters. In experiments involving over a hundred manually-annotated Web pages and tens of thousands of spots, our approaches significantly outperform recently-proposed algorithms.

Topic modeling has traditionally been studied for single text collections and applied to social media data represented in the form of text documents. With the emergence of many social media platforms, users find themselves using different social media for posting content and for social interaction. While many topics may be shared across social media platforms, users typically show preferences of certain social media platform(s) over others for certain topics. Such platform preferences may even be found at the individual level. To model social media topics as well as platform preferences of users, we propose a new topic model known as MultiPlatform-LDA (MultiLDA). Instead of just merging all posts from different social media platforms into a single text collection, MultiLDA keeps one text collection for each social media platform but allowing these platforms to share a common set of topics. MultiLDA further learns the user-specific platform preferences for each topic. We evaluate MultiLDA against TwitterLDA, the state-of-the-art method for social media content modeling, on two aspects: (i) the effectiveness in modeling topics across social media platforms, and (ii) the ability to predict platform choices for each post. We conduct experiments on three real-world datasets from Twitter, Instagram and Tumblr sharing a set of common users. Our experiments results show that the MultiLDA outperforms in both topic modeling and platform choice prediction tasks. We also show empirically that among the three social media platforms, “Daily matters” and “Relationship matters” are dominant topics in Twitter, “Social gathering”, “Outing” and “Fashion” are dominant topics in Instagram, and “Music”, “Entertainment” and “Fashion” are dominant topics in Tumblr.


Question: Multi-layered topical text classification utilizing Wikipedia.

This paper explores the implementation of statistical methods in the realm of speech recognition, an area where accuracy and efficiency are paramount. It is widely acknowledged that mastering the content of this book does not necessitate an intense, one-day reading marathon. Engaging with its material in short, frequent intervals can prevent the sense of monotony that might arise from prolonged, uninterrupted reading sessions. The text is designed to be accessible and engaging, aiming to avoid the trap of reader disinterest. It is only when a reader finds the subject matter unappealing that boredom may ensue. The book "Statistical Methods for Speech Recognition" is crafted to cater to the interests of a broad audience, offering insights and knowledge that are universally sought after in the field of speech recognition technology. It delves into the statistical techniques that underpin modern speech recognition systems, making it a valuable resource for professionals and students alike.

The main objective of a text summarization system is to identify the most important information from the given text and present it to the end users. In this paper, Wikipedia articles are given as input to system and extractive text summarization is presented by identifying text features and scoring the sentences accordingly. The text is first pre-processed to tokenize the sentences and perform stemming operations. We then score the sentences using the different text features. Two novel approaches implemented are using the citations present in the text and identifying synonyms. These features along with the traditional methods are used to score the sentences. The scores are used to classify the sentence to be in the summary text or not with the help of a neural network. The user can provide what percentage of the original text should be in the summary. It is found that scoring the sentences based on citations gives the best results.

Human languages are naturally ambiguous, which makes it difficult to automatically understand the semantics of text. Most vector space models (VSM) treat all occurrences of a word as the same and build a single vector to represent the meaning of a word, which fails to capture any ambiguity. We present sense-aware semantic analysis (SaSA), a multi-prototype VSM for word representation based on Wikipedia, which could account for homonymy and polysemy. The “sense-specific” prototypes of a word are produced by clustering Wikipedia pages based on both local and global contexts of the word in Wikipedia. Experimental evaluation on semantic relatedness for both isolated words and words in sentential contexts and word sense induction demonstrate its effectiveness.

This paper presents an approach for knowledge discovery in texts extracted from the Web. Instead of analyzing words or attribute values, the approach is based on concepts, which are extracted from texts to be used as characteristics in the mining process. Statistical techniques are applied on concepts in order to find interesting patterns in concept distributions or associations. In this way, users can perform discovery in a high level, since concepts describe real world events, objects, thoughts, etc. For identifying concepts in texts, a categorization algorithm is used associated to a previous classification task for concept definitions. Two experiments are presented: one for political analysis and other for competitive intelligence. At the end, the approach is discussed, examining its problems and advantages in the Web context.

Support tickets from customers contain much hidden information. Unsupervised machine learning methods are able to discover this hidden information. In this paper we propose the categorization of support tickets using clustering methods in combination with topic models. Furthermore label generation techniques are used to generate meaningful names for these categories. The results are compared with related

A lot of knowledge about procedures and how-tos are described in text. Recently, extracting semantic relations from the procedural text has been actively explored. Prior work mostly has focused on finding relationships among verb-noun pairs or clustering of extracted pairs. In this paper, we investigate the problem of learning individual procedure-specific relationships (e.g. is method of, is alternative of, or is subtask of ) among sentences. To identify the relationships, we propose an end-to-end neural network architecture, which can selectively learn important procedure-specific relationships. Using this approach, we could construct a how-to knowledge base from the largest procedure sharing-community, wikihow.com. The evaluation of our approach shows that it outperforms the existing entity relationship extraction algorithms.

Recent approaches to text classification have used two different first-order probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey and Croft 1996; Koller and Sahami 1997). Others use a multinomial model, that is, a uni-gram language model with integer word counts (e.g. Lewis and Gale 1994; Mitchell 1997). This paper aims to clarify the confusion by describing the differences and details of these two models, and by empirically comparing their classification performance on five text corpora. We find that the multi-variate Bernoulli performs well with small vocabulary sizes, but that the multinomial performs usually performs even better at larger vocabulary sizes—providing on average a 27% reduction in error over the multi-variate Bernoulli model at any vocabulary size.

Despite the success of deep learning on many fronts especially image and speech, its application in text classification often is still not as good as a simple linear SVM on n-gram TF-IDF representation especially for smaller datasets. Deep learning tends to emphasize on sentence level semantics when learning a representation with models like recurrent neural network or recursive neural network, however from the success of TF-IDF representation, it seems a bag-of-words type of representation has its strength. Taking advantage of both representions, we present a model known as TDSM (Top Down Semantic Model) for extracting a sentence representation that considers both the word-level semantics by linearly combining the words with attention weights and the sentence-level semantics with BiLSTM and use it on text classification. We apply the model on characters and our results show that our model is better than all the other characterbased and word-based convolutional neural network models by (Zhang et al., 2015) across seven different datasets with only 1% of their parameters. We also demonstrate that this model beats traditional linear models on TF-IDF vectors on small and polished datasets like news article in which typically deep learning models surrender.

Twitter, a micro-blogging service, provides users with a framework for writing brief, often-noisy postings about their lives. These posts are called "Tweets." In this paper we present early results on discovering Twitter users' topics of interest by examining the entities they mention in their Tweets. Our approach leverages a knowledge base to disambiguate and categorize the entities in the Tweets. We then develop a "topic profile," which characterizes users' topics of interest, by discerning which categories appear frequently and cover the entities. We demonstrate that even in this early work we are able to successfully discover the main topics of interest for the users in our study.

We propose a Bayesian nonparametric topic model that rep- resents relationships between given labels and the corre- sponding words/phrases, from supervised articles. Unlike existing supervised topic models, our proposal, supervised N-gram topic model (SNT), focuses on both a number of topics and power-law distribution in the word frequencies to extract topic specific N-grams. To achieve this goal, SNT takes a Bayesian nonparametric approach to topic sampling, which generates word distribution jointly with the given variable in textual order, and then form each N-gram word as a hierarchy of Pitman-Yor process priors. Experiments on labeled text data show that SNT is useful as a generative model for discovering more phrases that complement human experts and domain specific knowledge than the existing al- ternatives. The results show that SNT can be applied to various tasks such as automatic annotation.

Large-sized repositories of learning objects (LOs) are difficult to create and also to maintain. In this paper we propose a way to reduce this drawback by improving the classification mechanisms of the LO repositories. Specifically, we present a solution to automate the LO classification of the Universia repository, a collection of more than 15 million of LOs represented according to the IEEE LOM standard. Although a small part of these LOs is correctly classified, most are unclassified and therefore searching and accessing information is difficult. Our solution makes use of the categories provided by DBpedia, a linked data repository, to automatically improve its classification through a graph-based filtering algorithm, which selects the most suitable categories for describing a LO. Once selected, these categories will classify the LO, linking its classification metadata with a set of DBpedia categories.