Question: DeepX: A software tool for speeding up low-power deep learning inference on mobile devices.

Introducing EmotionSense: An Adaptive Mobile Platform for Social Psychology Research

The ubiquitous nature of modern smartphones, coupled with their advanced sensing, processing, and communication features, positions them as a powerful tool for conducting social and psychological experiments in a non-intrusive manner. This paper introduces EmotionSense, a software platform designed to leverage these capabilities for experimental social psychology research. EmotionSense is capable of monitoring individual emotions, activities, verbal exchanges, and proximity interactions among social group members. A key feature of the system is its adaptability, achieved through a declarative language that enables the expression of rules aimed at optimizing power usage. We evaluate the prototype of EmotionSense on Nokia Symbian phones through a series of small-scale experiments to assess its accuracy and energy efficiency. Additionally, we report on a real-world deployment that explores participants' emotions and interactions. Our findings are cross-validated with data from user questionnaires and compared to results obtained from traditional social psychological studies. Notably, we demonstrate the automatic detection of speakers' and participants' emotions using classifiers that run locally on standard mobile devices, and we establish correlations between speaking, interactions, activities, and locations.

Mobile computing continuously evolve through the sustained effort of many researchers. It seamlessly augments users' cognitive abilities via compute-intensive capabilities such as speech recognition, natural language processing, etc. By thus empowering mobile users, we could transform many areas of human activity. This article discusses the technical obstacles to these transformations and proposes a new architecture for overcoming them. In this architecture, a mobile user exploits virtual machine (VM) technology to rapidly instantiate customized service software on a nearby cloudlet and then uses that service over a wireless LAN; the mobile device typically functions as a thin client with respect to the service. A cloudlet is a trusted, resource-rich computer or cluster of computers that's well-connected to the Internet and available for use by nearby mobile devices. Our strategy of leveraging transiently customized proximate infrastructure as a mobile device moves with its user through the physical world is called cloudlet-based, resource-rich, mobile computing. Crisp interactive response, which is essential for seamless augmentation of human cognition, is easily achieved in this architecture because of the cloudlet's physical proximity and one-hop network latency. Using a cloudlet also simplifies the challenge of meeting the peak bandwidth demand of multiple users interactively generating and receiving media such as high-definition video and high-resolution images. Rapid customization of infrastructure for diverse applications emerges as a critical requirement, and our results from a proof-of-concept prototype suggest that VM technology can indeed help meet this requirement.

The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing.ieee.org/lpirc) is an annual competition started in 2015. The competition identifies the best technologies that can classify and detect objects in images efficiently (short execution time and low energy consumption) and accurately (high precision). Over the four years, the winners’ scores have improved more than 24 times. As computer vision is widely used in many battery-powered systems (such as drones and mobile phones), the need for low-power computer vision will become increasingly important. This paper summarizes LPIRC 2018 by describing the three different tracks and the winners’ solutions.

Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of low-precision fixed-point computations, we observe the rounding scheme to play a crucial role in determining the network’s behavior during training. Our results show that deep networks can be trained using only 16-bit wide fixed-point number representation when using stochastic rounding, and incur little to no degradation in the classification accuracy. We also demonstrate an energy-efficient hardware accelerator that implements low-precision fixed-point arithmetic with stochastic rounding.

The inherently limited processing power and battery lifetime of mobile phones hinder the possible execution of computationally intensive applications like content-based video analysis or 3D modeling. Offloading of computationally intensive application parts from the mobile platform into a remote cloud infrastructure or nearby idle computers addresses this problem. This paper presents our Mobile Augmentation Cloud Services (MACS) middleware which enables adaptive extension of Android application execution from a mobile client into the cloud. Applications are developed by using the standard Android development pattern. The middleware does the heavy lifting of adaptive application partitioning, resource monitoring and computation offloading. These elastic mobile applications can run as usual mobile application, but they can also reach transparently remote computing resources. Two prototype applications using the MACS middleware demonstrate the benefits of the approach. The evaluation shows that applications, which involve complicated algorithms and large computations, can benefit from offloading with around 95% energy savings and significant performance gains compared to local execution only.

Floating-point (FP) multipliers are the main energy consumers in many modern embedded digital signal processing (DSP) and multimedia systems. For lossy applications, minimizing the precision of FP multiplication operations under the acceptable accuracy loss is a well-known approach for reducing the energy consumption of FP multipliers. This paper proposes a multiple-precision FP multiplier to efficiently trade the energy consumption with the output quality. The proposed FP multiplier can perform lowprecision multiplication that generates 8−, 14−, 20−, or 26bit mantissa product through an iterative and truncated modified Booth multiplier. Energy saving for lowprecision multiplication is achieved by partially suppressing the computation of mantissa multiplier. In addition, the proposed multiplier allows the bitwidth of mantissa in the multiplicand, multiplier, and output product to be dynamically changed when it performs different FP multiplication operations to further reduce energy consumption. Experimental results show that the proposed multiplier can achieve 59 %, 71 %, 73 %, and 82 % energy saving under 0.1 %, 1 %, 5 %, and 11 % accuracy loss, respectively, for the RGB-to-YUV & YUV-to-RGB conversion when compared to the conventional IEEE single-precision multiplier. In addition, the results also exhibit that the proposed multiplier can obtain more energy reduction than previous multipleprecision iterative FP multipliers.

以容忍。还有一点让人印象深刻: 对于不同的程序,这些提升和能 耗降低并不需要使用不同的加速 器实现方式——它们通过相同的 近似加速器得到,那些加速器可 用于所有程序中所有类型的可近 似代码区。 除了文中描述的之外,通过让 近似计算引擎在明显低得多的电压 下运行,还能节约更多的能源。业 界增长的真正引擎——登纳德缩放 (Dennard scaling)——允许晶体 管减少尺寸,同时消耗更少的能源, 得到更高的性能。但是登纳德缩放 正趋近极限。设备的尺寸已经缩减 到了原子级别。而且,在不牺牲设 备可靠性的情况下,无法继续显著 降低电压。新技术会带来不错的能 耗性能,但由于需要利用冗余来补 偿它们的不可靠性,这一优势被抵 消了。因此,对于传统的交易系统 而言,未来的技术不大可能大幅提 升其能耗性能。然而,对于那些生 成近似结果的系统(比如作者描述 的系统),需要的冗余可能有所不 同且要简单得多。对于这些系统类 型,低电压运行是可行的。 未 来, 当 我 们 使 用 缩 小 的 CMOS 和后硅技术,利用海量计算 处理世上产生的海量数据时,近似 计算似乎是我们最有可能取得成功 的希望。下面的文章就是为实现此 目标所需开展的工作的一个很好的 例子。

OpenCL FPGA has recently gained great popularity with emerging needs for workload acceleration such as Convolutional Neural Network (CNN), which is the most popular deep learning architecture in the domain of computer vision. While OpenCL enhances the code portability and programmability of FPGA, it comes at the expense of performance. The key challenge is to optimize the OpenCL kernels to efficiently utilize the flexible hardware resources in FPGA. Simply optimizing the OpenCL kernel code through various compiler options turns out insufficient to achieve desirable performance for both compute-intensive and data-intensive workloads such as convolutional neural networks . In this paper, we first propose an analytical performance model and apply it to perform an in-depth analysis on the resource requirement of CNN classifier kernels and available resources on modern FPGAs. We identify that the key performance bottleneck is the onchip memory bandwidth. We propose a new kernel design to effectively address such bandwidth limitation and to provide an optimal balance between computation, on-chip, and off-chip memory access. As a case study, we further apply these techniques to design a CNN accelerator based on the VGG model. Finally, we evaluate the performance of our CNN accelerator using an Altera Arria 10 GX1150 board. We achieve 866 Gop/s floating point performance at 370MHz working frequency and 1.79 Top/s 16-bit fixed-point performance at 385MHz. To the best of our knowledge, our implementation achieves the best power efficiency and performance density compared to existing work.

As communication and sensing capabilities of mobile devices increase, mobile applications are becoming increasingly complex. The ability of computation offloading, which is one of the main features of mobile edge computing gains relevance as a technique to improve the battery lifetime of mobile devices and increase the performance of applications. In this paper, we describe the offloading system model and present an innovative architecture, called "MVR", contributing to computation offloading in mobile edge computing.

Low precision networks in the reinforcement learning (RL) setting are relatively unexplored because of the limitations of binary activations for function approximation. Here, in the discrete action ATARI domain, we demonstrate, for the first time, that low precision policy distillation from a high precision network provides a principled, practical way to train an RL agent. As an application, on 10 different ATARI games, we demonstrate real-time end-to-end game playing on lowpower neuromorphic hardware by converting a sequence of game frames into discrete actions.

Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community (Krizhevsky et al., 2012) for their superior predictive properties including robustness to overfitting. However their application to financial market prediction has not been previously researched, partly because of their computational complexity. This paper describes the application of DNNs to predicting financial market movement directions. A critical step in the viability of the approach in practice is the ability to effectively deploy the algorithm on general purpose high performance computing infrastructure. Using an Intel Xeon Phi co-processor with 61 cores, we describe the process for efficient implementation of the batched stochastic gradient descent algorithm and demonstrate a 11.4x speedup on the Intel Xeon Phi over a serial implementation on the Intel Xeon.


Question: Regarding the clarification of the term 'ontology'.

This paper delves into the concept of "ontology" within the realm of knowledge engineering, a term that has surged in popularity but whose definition remains nebulous due to its multifaceted usage. Focusing on the discourse within the field of artificial intelligence (AI), we identify and clarify several distinct interpretations of "ontology," advocating for precise terminological distinctions concerning phrases like "ontology," "conceptualization," and "ontological commitment." The paper begins by discussing the capitalization of "Ontology," a term typically reserved for philosophical studies, before exploring the potential ambiguity between an ontology as an abstract conceptual framework at the knowledge level and an ontology as a tangible tool at the symbol level, designed for specific applications. Central to our analysis is a critical examination of Gruber's influential definition of ontology as a specification of a conceptualization, aiming to demystify the term and provide a clearer understanding of its role in knowledge representation.

a r t i c l e i n f o a b s t r a c t We present a detailed semantics for linguistic spatial expressions supportive of computational processing that draws substantially on the principles and tools of ontological engineering and formal ontology. We cover language concerned with space, actions in space and spatial relationships and develop an ontological organization that relates such expressions to general classes of fixed semantic import. The result is given as an extension of a linguistic ontology, the Generalized Upper Model, an organization which has been used for over a decade in natural language processing applications. We describe the general nature and features of this ontology and show how we have extended it for working particularly with space. Treaitng the semantics of natural language expressions concerning space in this way offers a substantial simplification of the general problem of relating natural spatial language to its contextualized interpretation. Example specifications based on natural language examples are presented, as well as an evaluation of the ontology's coverage, consistency, predictive power, and applicability.

The online learning called e-learning is a new learning path that offers to learners to study at their own pace and at the moments that suit them. It is in this perspective that the semantic web has known its emergence in the field of e-learning to offer platforms content more personalized and more adapted to student's and teacher's needs. Since Moodle is the most popular e-learning platform, we propose in this paper to build its OWL ontology by exploring the representative data that we collected from its UML class diagram. The choice of UML class diagram as a basis for data collection for the development of the ontology is justified by the fact that the transition from UML to OWL ontology brings ontology development process closer to the wider software engineering population. The built ontology brings also great benefit in the field of the Social Learning Network Analysis. Because it gives the opportunity to study the behavior of the platform users by giving meaning to their relationships instead of modelling them only as knots and edges.

The alignment of terminologies can be considered as a kind of “translation” between two or more terminologies that aims to enhance the communication among people coming from different domains or expertise. In this paper we introduce the Visual Terminology Alignment tool (VisTA) that enables the exact alignment for RDF/SKOS-like terminologies, suitable for integrating knowledge bases, rather than information retrieval systems. The tool provides a simple and friendly web-based user interface for the alignment between two terminologies, while it visualizes the terminology hierarchies, enables the interactive alignment process, and presents the alignment result. The latter is a native RDF/SKOS graph that interconnects the two terminology graphs, supporting interoperability and extending the search capabilities over the integrated semantic graph, using the broader and exact match properties.

Several technologies are emerging that provide new ways to capture, store, present and use knowledge. This book is the first to provide a comprehensive introduction to five of the most important of these technologies: Knowledge Engineering, Knowledge Based Engineering, Knowledge Webs, Ontologies and Semantic Webs. For each of these, answers are given to a number of key questions (What is it? How does it operate? How is a system developed? What can it be used for? What tools are available? What are the main issues?). The book is aimed at students, researchers and practitioners interested in Knowledge Management, Artificial Intelligence, Design Engineering and Web Technologies. During the 1990s, Nick worked at the University of Nottingham on the application of AI techniques to knowledge management and on various knowledge acquisition projects to develop expert systems for military applications. In 1999, he joined Epistemics where he worked on numerous knowledge projects and helped establish knowledge management programmes at large organisations in the engineering, technology and legal sectors. He is author of the book"Knowledge Acquisition in Practice", which describes a step-by-step procedure for acquiring and implementing expertise. He maintains strong links with leading research organisations working on knowledge technologies, such as knowledge-based engineering, ontologies and semantic technologies.

Decoding the important differences in terminology, underlying philosophy, and value systems between two similar categories of software.

Many organizations nowadays face the problem of accessing existing data sources by means of flexible mechanisms that are both powerful and efficient. Ontologies are widely considered as a suitable formal tool for sophisticated data access. The ontology expresses the domain of interest of the information system at a high level of abstraction, and the relationship between data at the sources and instances of concepts and roles in the ontology is expressed by means of mappings. In this paper we present a solution to the problem of designing effective systems for ontology-based data access. Our solution is based on three main ingredients. First, we present a new ontology language, based on Description Logics, that is particularly suited to reason with large amounts of instances. The second ingredient is a novel mapping language that is able to deal with the so-called impedance mismatch problem, i.e., the problem arising from the difference between the basic elements managed by the sources, namely data, and the elements managed by the ontology, namely objects. The third ingredient is the query answering method, that combines reasoning at the level of the ontology with specific mechanisms for both taking into account the mappings and efficiently accessing the data at the sources.

Conceptual modeling aims to capture the knowledge of a domain as perceived by the modeler and represent it in a way that will help further system analysis and design. Object-oriented methodology has been taken as a common practice for software system analysis and design because of its obvious advantages of handling the “software crisis” (Booch, 1994). Object-oriented analysis (OOA) is “a method of analysis that examines requirements from the perspectives of the classes and objects” (Booch, 1994). Object-oriented design (OOD) is a method of design “encompassing the process of OO decomposition and a notation for depicting both logical and physical as well as static and dynamic models of the system” (Booch, 1994).

We think of Match as an operator which takes two graph-like structures (e.g., conceptual hierarchies or ontologies) and produces a mapping between those nodes of the two graphs that correspond semantically to each other. Semantic matching is a novel approach where semantic correspondences are discovered by computing, and returning as a result, the semantic information implicitly or explicitly codified in the labels of nodes and arcs. In this paper we present an algorithm implementing semantic matching, and we discuss its implementation within the S-Match system. We also test S-Match against three state of the art matching systems. The results, though preliminary, look promising, in particular for what concerns precision and recall.

The concept of thesaurus has evolved from a list of conceptually interrelated words to today's controlled vocabularies, where terms form complex structures through semantic relationships. This term comes from the Latin and has turn been derived from the Greek "θησαυρός", which means treasury according to the Spanish Royal Academy, in whose dictionary it is also defined as: 'name given by its authors to certain dictionaries, catalogues and anthologies'. The increase in scientific communication and productivity made it essential to develop keyword indexing systems. At that time, Howerton spoke of controlled lists to refer to concepts that were heuristically or intuitively related. According to Roberts (1984), Mooers was the first to relate thesauri to information retrieval systems; Taube established the foundations of post-coordination, while Luhn dealt, at a basic level, with the creation of thesauri using automatic techniques. Brownson (1957) was the first to use the term to refer to the issue of translating concepts and their relationships expressed in documents into a more precise language free of ambiguities in order to facilitate information retrieval. The ASTIA Thesaurus was published in the early 1960s (Currás, 2005), already bearing the characteristics of today's thesauri and taking on the need for a tool to administer a controlled vocabulary in terms of indexing, thereby giving rise to the concept of documentary language.

Provenance refers to the origin of a particular object. In computational terms, provenance is a historical record of the derivation of data that can help to understand the current record. In this context, this work presents a proposal for software processes improvement using a provenance data model and an ontology. This improvement can be obtained by process data execution analysis with an approach called PROV-Process, which uses a layer for storing process provenance and an ontology based on PROV-O.


Question: Regarding the clarification of the term 'ontology'.

This paper offers a comprehensive review of the Knowledge Engineering field's evolution over the past 15 years, focusing on the significant paradigm shift from a transfer perspective to a modeling perspective. Two influential approaches that have significantly influenced Knowledge Engineering research are highlighted: Role-limiting Methods and Generic Tasks. To elucidate the concepts and methodologies that have emerged in recent years, three modeling frameworks are presented: CommonKADS, MIKE, and PROTÉGÉ-II. The discussion is further enriched by an in-depth examination of key methodological advancements, including specification languages for knowledge-based systems, problem-solving methods, and the development of ontologies. The paper concludes by exploring the interconnections between Knowledge Engineering and other disciplines, such as Software Engineering, Information Integration, and Knowledge Management, illustrating how these fields interrelate and complement each other in the broader context of knowledge processing and management.

The link between classification and knowledge is explored. Classification schemes have properties that enable the representation of entities and relationships in structures that reflect knowledge of the domain being classified. The strengths and limitations of four classificatory approaches are described in terms of their ability to reflect, discover, and create new knowledge. These approaches are hierarchies, trees, paradigms, and faceted analysis. Examples are provided of the way in which knowledge and the classification process affect each other. INTRODUCTION Developments in our ability to store and retrieve large amounts of' information have stimulated an interest in new ways to exploit this information for advancing human knowledge. This article describes the relationship between knowledge representation (as manifested in classifications) and the processes of knowledge discovery and creation. How does the classification process enable or constrain knowing something or discovering new knowledge about something? In what ways might we develop classifications that will enhance our ability to discover meaningful information in our data stores? The first part of the article describes several representative classificatory structures-hierarchies, trees, paradigms, and faceted analysis with the aim of identifying how these structures serve as knowledge representations and in what ways they can be used for knowledge discovery and creation. The second part of the discussion includes examples from existing classification schemes and discusses how the schemes reflect or fail to reflect knowledge. KNOWLEDGE, THEORY, AND CLASSIFICATION Scholars in many fields, from philosophy to cybernetics, have long discussed the concept of knowledge and the problems of representing knowledge in information systems. The distinction is drawn between merely observing, perceiving, or even describing things and truly knowing them. To know implies a process of integration of facts about objects and the context in which the objects and processes exist. Even in colloquial usage, knowledge about someone or something is always expressed in terms of deep relationships and meanings as well as its place in time and space. To know cars means not only understanding car mechanics but also knowledge of the interplay of the mechanical processes and perhaps even factors such as aesthetics, economics, and psychology. The process of knowledge discovery and creation in science has traditionally followed the path of systematic exploration, observation, description, analysis, and synthesis and testing of phenomena and facts, all conducted within the communication framework of a particular research community with its accepted methodology and set of techniques. We know the process is not entirely rational but often is sparked and then fueled by insight, hunches, and leaps of faith (Bronowski, 1978). Moreover, research is always conducted within a particular political and cultural reality (Olson, 1998). Each researcher and, on a larger scale, each research community at various points must gather up the disparate pieces and in some way communicate what is known, expressing it in such a way as to be useful for further discovery and understanding. A variety of formats exist for the expression of knowledge e.g., theories, models, formulas, descriptive reportage of many sorts, and polemical essays. Of these formats, science particularly values theories and models because they are a “symbolic dimension of experience as opposed to the apprehension of brute fact” (Kaplan, 1963, p. 294) and can therefore be symbolically extended to cover new experiences. A theory thus explains a particular fact by abstracting the relationship of that fact to other facts. Grand, or covering, theories explain facts in an especially eloquent way and in a very wide (some would say, universal) set of situations. Thus, Darwinian, Marxist, or Freudian theories, for example, attempt to explain processes and behaviors in many contexts, but they do so at a high level of abstraction. There are relatively few grand theories, however, and we rely on the explanatory and descriptive usefulness of more “local” theoriestheories that explain a more limited domain but with greater specificity. CLASSIFICATION AS KNOWLEDGE REPRESENTATION How are theories built? How does knowledge accumulate and then get shaped into a powerful representation? There are, of course, many processes involved, but often one of them is the process of classification. Classification is the meaningful clustering of experience. The process of classification can be used in a formative way and is thus useful during the preliminary stages of inquiry as a heuristic tool in discovery, analysis, and theorizing (Davies, 1989). Once concepts gel and the relationships among concepts become understood, a classification can be used as a rich representation of what is known and is thus useful in communication and in generating a fresh cycle of exploration, comparison, and theorizing. Kaplan (1963) states that “theory is not the aggregate of the new laws but their connectedness, as a bridge consists of girders only in that the girders are joined together in a particular way” (p. 297). A good classification functions in much the same way that a theory does, connecting concepts in a useful structure. If successful, it is, like a theory, descriptive, explanatory, heuristic, fruitful, and perhaps also elegant, parsimonious, and robust (Kwasnik, l992b). There are many approaches to the process of classification and to the construction of the foundation of classification schemes. Each kind of classification process has different goals, and each type of classification scheme has different structural properties as well as different strengths and weaknesses in terms of knowledge representation and knowledge discovery. The following is a representative sample of some common approaches and structures. HIERARCHIES We have inherited our understanding of hierarchical classifications from Aristotle (Ackrill, 1963), who posited that all nature comprised a unified whole. The whole could be subdivided, like a chicken leg at the joint, into “natural” classes, and each class further into subclasses, and so on-this process following an orderly and systematic set of rules of association and distinction. How do we know what a natural dividing place is, and how do we arrive at the rules for division and subdivision? According to Aristotle, only exhaustive observation can reveal each entity’s true (essential) attributes, and only philosophy can guide us in determining the necessary and sufficient attributes for membership in any given class. In fact, according to Aristotle’s philosophy, it is only when an entity is properly classed, and its essential properties identified, that we can say we truly know it. This is the aim of science, he claims-i.e., to unambiguously classify all phenomena by their essential (true) qualities. While Aristotle’s legacy is alive in spirit in modern applications of classification, most practitioners recognize that a pure and complete hierarchy is essentially possible only in the ideal. Nevertheless, in knowledge domains that have theoretical foundations (such as germ theory in medicine and the theory of evolution in biology), hierarchies are the preferred structures for knowledge representation (see, for example, the excerpt from the Medical Subject Headings [MeSH] in Figure 1. Based on the MeSH excerpt in Figure 1, note that hierarchies have strict structural requirements: Inclusiveness: The top class (in this case, EYEDISEASES) is the most inclusive class and describes the domain of the classification. The top class includes all its subclasses and sub-subclasses. Put another way, all the classes in the example are included in the top class: EYEDISEASES. Species/differentia: A true hierarchy has only one type of relationship between its superand subclasses and this is the generic relationship, also known as species/differentia, or more colloquially as the is-a relationship. In a generic relationship, ALLERGIC CONJUNCTIVITIS is a kind of CONJUNCTIVITIS, which in turn is a kind of CONJUNCTIVAL DISEASE, which in turn is a kind of EYEDISEASE. Inheritance: This requirement of strict class inclusion ensures that everything that is true for entities in any given class is also true for entities in its subclasses and sub-subclasses. Thus whatever is true of EYE DISEASES (as a whole) is also true of CONJUNCTIVAL Whatever is true of CONJUNCTIVAL DISEASES (as a whole) is also true of CONJUNCTIVITIS, and so on. This property is called inheritance, that is, attributes are inherited by a subclass from its superclass. Transitivity: Since attributes are inherited, all sub-subclasses are members of not only their immediate superclass but of every superclass above that one. Thus if BACTERIAL CONJUNCTIVITIS is a kind Of CONJUNCTIVITIS, and CONJUNCTIVITIS is a kind Of CONJUNCTIVAL DISEASE, then, by the rules of transitivity, BACTERIAL CONJUNCTIVITIS is also a kind of CONJUNCTIVAL DISEASE, and so on. This property is called transitivity. Systematic and predictable rules for association and distinction: The rules for grouping entities in a class (i.e., creating a species) are determined beforehand, as are the rules for creating distinct subclasses (differentia). Thus all entities in a given class are like each other in some predictable (and predetermined) way, and these entities differ from entities in sibling classes in some predictable (and predetermined) way. In the example above, CONJUNCTIVAL and CORNEAL DISEASES are alike in that they are both kinds of EYEDISEASES. They are differentiated from each other along some predictable and systematic criterion of distinction (in this case “part of the eye affected”) Mutual exclusivity: A given entity can belong to only one class. This property is called mutual exclusivity. Necessary and sufficient criteria: In a pure

CorMet is a corpus-based system for discovering metaphorical mappings between concepts. It does this by finding systematic variations in domain-specific selectional preferences, which are inferred from large, dynamically mined Internet corpora. Metaphors transfer structure from a source domain to a target domain, making some concepts in the target domain metaphorically equivalent to concepts in the source domain. The verbs that select for a concept in the source domain tend to select for its metaphorical equivalent in the target domain. This regularity, detectable with a shallow linguistic analysis, is used to find the metaphorical interconcept mappings, which can then be used to infer the existence of higher-level conventional metaphors. Most other computational metaphor systems use small, hand-coded semantic knowledge bases and work on a few examples. Although Cor Met's only knowledge base is Word Net (Fellbaum 1998) it can find the mappings constituting many conventional metaphors and in some cases recognize sentences instantiating those mappings. CorMet is tested on its ability to find a subset of the Master Metaphor List (Lakoff, Espenson, and Schwartz 1991).

As the editors of this volume remind us, polysemy has been a vexing issue for the understanding of language since antiquity. For half a century, it has been a major bottleneck for natural language processing. It contributed to the failure of early machine translation research (remember Bar-Hillel’s famous pen and box example) and is still plaguing most natural language processing and information retrieval applications. A recent issue of this journal described the state of the art in automatic sense disambiguation (Ide and Véronis 1998), and Senseval system competitions have revealed the immense difficulty of the task (http://www.sle.sharp.co.uk/senseval2). However, no significant progress can be made on the computational aspects of polysemy without serious advances in theoretical issues. At the same time, theoretical work can be fostered by computational results and problems, and language-processing applications can provide a unique test bed for theories. It was therefore an excellent idea to gather both theoretical and applied contributions in the same book. Yael Ravin and Claudia Leacock are well-known names to those who work on the theoretical and computational aspects of word meaning. In this volume, they bring together a collection of essays from leading researchers in the field. As far as I can tell, these essays are not reprints or expanded versions of conference papers, as is often the case for edited works; instead, they seem to have been specially commissioned for the purposes of this book, which makes it even more exciting to examine. The book is composed of 11 chapters. It is not formally divided into parts, but chapters dealing more specifically with the computational aspects of polysemy are grouped together at the end (and constitute about one-third of the volume). Chapter 1 is an overview written by the volume editors. Yael Ravin and Claudia Leacock survey the main theories of meaning and their treatment of polysemy. These include the classical Aristotelian approach revived by Katz and Fodor (1963); Rosch’s (1977) prototypical approach, which has its roots in Wittgenstein’s Philosophical Investigations (1953); and the relational approach recently exemplified by WordNet (Fellbaum 1998), which (although the authors do not mention it) can be traced back to Peirce’s (1931–1958) and Selz’s (1913, 1922) graphs and which gained popularity with Quillian’s (1968) semantic networks. In the course of this overview, Ravin and Leacock put the individual chapters into perspective by relating them to the various theories.

In this paper we introduce the TTCS system, so named after Terms To Conceptual Spaces, that exploits a resource-driven approach relying on BabelNet, NASARI and ConceptNet. TTCS takes in input a term and its context of usage and produces as output a specific type of vector-based semantic representation, where conceptual information is encoded through the Conceptual Spaces (a geometric framework for common-sense knowledge representation and reasoning). The system has been evaluated in a twofold experimentation. In the first case we assessed the quality of the extracted common-sense conceptual information with respect to human judgments with an online questionnaire. In the second one we compared the performances of a conceptual categorization system that was run twice, once fed with extracted annotations and once with hand-crafted annotations. In both cases the results are encouraging and provide precious insights to make substantial improvements.

Ontology is widely used as a mean to represent and share common concepts and knowledge from a particular domain or specialisation. As a knowledge representation, the knowledge within an ontology must be able to evolve along with the recent changes and updates within the community practice. In this paper, we propose a new Ontology-based Information Extraction (OBIE) system that extends existing systems in order to enrich and validate an ontology. Our model enables the ontology to find related recent knowledge in the domain from communities, by exploiting their underlying knowledge as keywords. The knowledge extraction process uses ontology-based and pattern-based information extraction technique. Not only the extracted knowledge enriches the ontology, it also validates contradictory instance-related statements within the ontology that is no longer relevant to recent practices. We determine a confidence value during the enrichment and validation process to ensure the stability of the enriched ontology. We implement the model and present a case study in herbal medicine domain. The result of the enrichment and validation process shows promising results. Moreover, we analyse how our proposed model contributes to the achievement of a richer and stable ontology.

The concept of thesaurus has evolved from a list of conceptually interrelated words to today's controlled vocabularies, where terms form complex structures through semantic relationships. This term comes from the Latin and has turn been derived from the Greek "θησαυρός", which means treasury according to the Spanish Royal Academy, in whose dictionary it is also defined as: 'name given by its authors to certain dictionaries, catalogues and anthologies'. The increase in scientific communication and productivity made it essential to develop keyword indexing systems. At that time, Howerton spoke of controlled lists to refer to concepts that were heuristically or intuitively related. According to Roberts (1984), Mooers was the first to relate thesauri to information retrieval systems; Taube established the foundations of post-coordination, while Luhn dealt, at a basic level, with the creation of thesauri using automatic techniques. Brownson (1957) was the first to use the term to refer to the issue of translating concepts and their relationships expressed in documents into a more precise language free of ambiguities in order to facilitate information retrieval. The ASTIA Thesaurus was published in the early 1960s (Currás, 2005), already bearing the characteristics of today's thesauri and taking on the need for a tool to administer a controlled vocabulary in terms of indexing, thereby giving rise to the concept of documentary language.

Subjectivity taggingis distinguishing sentences used to present opinions and evaluations from sentences used to objectively present factual information. There are numerous applications for which subjectivity tagging is relevant, including information extraction and information retrieval. This paper identifies strong clues of subjectivity using the results of a method for clustering words according to distributional similarity (Lin 1998), seeded by a small amount of detailed manual annotation. These features are then further refined with the addition of lexical semantic features of adjectives, specifically polarityandgradability(Hatzivassiloglou & McKeown 1997), which can be automatically learned from corpora. In 10-fold cross validation experiments, features based on both similarity clusters and the lexical semantic features are shown to have higher precision than features based on each alone.

Declaration This dissertation is the result of my own work done under the guidance of my supervisor, and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text. This dissertation is not substantially the same as any that I have submitted or will be submitting for a degree or diploma or other qualification at this or any other University. This dissertation does not exceed the regulation length of 60,000 words, including tables and footnotes. 5 Summary We introduce two abstract notions of equational algebraic system, called Equational System (ES) and Term Equational System (TES), in order to achieve sufficient expressivity as needed in modern applications in computer science. These generalize the classical concept of (enriched) algebraic theory of Kelly and Power [1993]. We also develop a theory for constructing free algebras for ESs and a theory of equational reasoning for TESs. In Part I, we introduce the general abstract, yet practical, concept of equational system and develop finitary and transfinitary conditions under which we give an explicit construction of free algebras for ESs. This free construction extends the well-known construction of free algebras for ω-cocontinuous endofunctors to an equational setting, capturing the intuition that free algebras consist of freely constructed terms quotiented by given equations and congruence rules. We further show the monadicity and cocom-pleteness of categories of algebras for ESs under the finitary and transfinitary conditions. To illustrate the expressivity of equational systems, we exhibit various examples including two modern applications, the Σ-monoids of Fiore et al. [1999] and the π-algebras of Stark [2005]. In Part II, we introduce the more concrete notion of term equational system, which is obtained by specializing the concept of equational system, but remains more general than that of enriched algebraic theory. We first develop a sound logical deduction system, called Term Equational Logic (TEL), for equational reasoning about algebras of TESs. Then, to pursue a complete logic, we give an internal completeness result, from which together with the explicit construction of free algebras one can typically synthesize sound and complete rewriting-style equational logics. To exemplify this scenario, we give two applications: multi-sorted algebraic theories and nominal equational theories of Clouston and Pitts [2007] and of Gabbay and Mathijssen [2007]. 7 Acknowledgements First and foremost, I am deeply grateful to my Ph.D. supervisor Marcelo Fiore for his patient guidance and support, without which this thesis would …

The paper aims at providing a description of EARTh, the Environmental Application Reference Thesaurus. It represents a general-purpose thesaurus for the environment, which has been published as a SKOS dataset in the Linked Open Data cloud. It promises to become a core tool for indexing and discovery environmental resources by refining and extending GEMET, which is considered the de facto standard when speaking of general-purpose thesaurus for the environment in Europe, besides it has been interlinked to popular LOD datasets as AGROVOC, EUROVOC, DBPEDIA and UMTHES. The paper illustrates the main characteristics of EARTh as a guide to its usage. It clarifies (i) the methodology adopted to define the EARTh content; (ii) the design and technological choices made when publishing EARTh as Linked Data; (iii) the information pertaining to its access and maintenance. Descriptions of EARTh applications and future relevance are highlighted. CNR-IIA-EKOLab Environmental Knowledge Organization Laboratory, Area della Ricerca di Roma 1, Via Salaria Km 29,300 C.P. 10,I-00016 Monterotondo stazione RM, {difranco, vds, plini} @iia.cnr.it

Methods to design of formal ontologies have been in focus of research since the early nineties when their importance and conceivable practical application in engineering sciences had been understood. However, often significant customization of generic methodologies is required when they are applied in tangible scenarios. In this paper, we present a methodology for ontology design developed in the context of data integration. In this scenario, a targeting ontology is applied as a mediator for distinct schemas of individual data sources and, furthermore, as a reference schema for federated data queries. The methodology has been used and evaluated in a case study aiming at integration of buildings' energy and carbon emission related data. We claim that we have made the design process much more efficient and that there is a high potential to reuse the methodology.


Question: Regarding the clarification of the term 'ontology'.

Exploring the Essence of Ontology
In various academic and professional circles, the term "ontology" is employed with varying connotations, reflecting its broad and evolving usage. Originating from a rich philosophical tradition, it has recently taken on new meanings within the realm of computational knowledge engineering. This shift began with a seminal, albeit informal, characterization of ontologies as "explicit specifications of conceptualizations." Our paper revisits and expands upon these initial efforts to clarify and formalize this definition, delving into the core concepts of conceptualization and explicit specification. We also highlight the significance of shared explicit specifications, emphasizing their role in facilitating understanding and collaboration across different domains. Through this exploration, we aim to provide a comprehensive and nuanced understanding of the term "ontology," bridging the gap between its philosophical origins and contemporary computational applications.

In this paper we explore synergies between the machine learning and knowledge representation fields by considering how scientific knowledge is represented in these areas. We illustrate some of the knowledge obtained through machine learning methods, providing two contrasting examples of such models: probabilistic graphical models (aka Bayesian networks) and artificial neural networks (including deep learning networks). From knowledge representation, we give an overview of ontological representations, qualitative reasoning, and planning. Then we discuss potential synergies that would benefit both areas.

In the context of ontology engineering, the ontology understanding is the basis for its further development and reuse. One intuitive effective approach to support ontology understanding is the process of ontology summarization which highlights the most important concepts of an ontology. Ontology summarization identifies an excerpt from an ontology that contains the most relevant concepts and produces an abridged ontology. In this article, we present a method for summarizing ontologies that represent a data source schema or describe a knowledge domain. We propose an algorithm to produce a personalized ontology summary based on user-defined parameters, e.g. summary size. The relevance of a concept is determined through user indication or centrality measures, commonly used to determine the relative importance of a vertex within a graph. The algorithm searches for the best ontology summary, i.e., the one containing the most relevant ontology concepts respecting all the original relationships between concepts and the parameters set by the user. Experiments were done comparing our generated ontology summaries against golden standard summaries as well as summaries produced by methods available in related work. We achieved in average more than 62.5% of similarity with golden standard summaries.

This paper presents the results of a set of methods to cluster WordNet word senses. The methods rely on different information sources: confusion matrixes from Senseval-2 Word Sense Disambiguation systems, translation similarities, hand-tagged examples of the target word senses and examples obtained automatically from the web for the target word senses. The clustering results have been evaluated using the coarsegrained word senses provided for the lexical sample in Senseval-2. We have used Cluto, a general clustering environment, in order to test different clustering algorithms. The best results are obtained for the automatically obtained examples, yielding purity values up to 84% on average over 20 nouns.

The paper presents our exploration into an organization ontology for the TOVE enterprise model. Its primary focus has been in linking structure and behavior through the concept of empowerment. Empowerment is the right of an organization agent to perform status changing actions. This linkage is critical to the unification of enterprise models and their executability.

There exists a large body of scienti c literature devoted to ontology matching, aligning, mapping translating and merging. With it, comes a long list (90+) of tools that support various aspects of these operations. We have approached such tools from the perspective of the INTER-IoT project, in which one of the goals is to facilitate semantic interoperability of Internet of Things platforms. Thus, we had to answer a question: what is actually available when one needs to align/merge ontologies. Here, we summarize our ndings.

In this paper we introduce SAMOD, a.k.a. Simplified Agile Methodology for Ontology Development, a novel agile methodology for the development of ontologies by means of small steps of an iterative workflow that focuses on creating well-developed and documented models starting from exemplar domain descriptions. RASH: https://w3id.org/people/essepuntato/papers/samod-owled2016.html

similarity. Units not yet reached asymptote: 0 Goodness of network: 0.61 Calculating the best mappings after 72 cycles. Best mapping of SMART is HUNGRY. 0.70 Best mapping of TALL is FRIENDLY. 0.71 Best mapping of TIMID is FRISKY. 0.54 Best mopping of TOM is BLACKIE. 0.70 Best mapping of STEVE is FIDO. 0.54 Best mopping of BILL is ROVER. 0.71 by ACME for this example. The five sentences corresponding to the five propositions in each analog (e.g., “Bill is smart”) were listed in adjacent colunms on a piece of paper. Sentences related to the same individual were listed consecutively; otherwise, the order was scrambled. Across subjects two different orders were used. The instructions simply stated, “Your task is to figure out what in the left set of sentences corresponds to what in the right set of sentences.” Subjects were also told that the meaning of the words was irrelevant. The three individuals and three attributes of the analog on the left were listed on the bottom of the page; for each element, subjects were to write down what they believed to be the corresponding element of the analog on the right. Three minutes were allowed for completion of the task. A group of 8 UCLA students in an undergraduate psychology class served as subjects. Five subjects produced the same set of six correspondences identified by ACME, 2 subjects produced four of the six, and 1 subject was unable to understand the task. These results indicate that finding the isomorphism for this example is within the capability of many college students. 344 HOLYOAK AND THAGARD Structure and Pragmatics in Metaphor To explore the performance of ACME in metaphorical mapping, the program was given predicate-calculus representations of the knowledge underlying a metaphor that has been analyzed in detail by Kittay (1987). The metaphor is derived from a passage in Plato’s Theuetetus in which Socrates declares himself to be a “midwife of ideas,” elaborating the metaphor at length. Table 19 contains predicate-calculus representations based upon Kittay’s analysis of the source analog r.?ncerning the role of a midwife and of the target analog concerning the role of a philosopher-teacher. Roughly, Socrates claims that he is like a midwife in that he introduces the student to intellectual partners, just as a midwife often serves first as a matchmaker; Socrates helps the student evaluate the truth or falsity of his ideas much as a midwife helps a mother to deliver a child. This metaphor was used to provide an illustration of the manner in which structural and pragmatic constraints interact in ACME. Table 19 presents predicate-calculus representations of two versions of the metaphor: an isomorphic version based directly upon Kittay’s analysis, and a nonisomorphic version created by adding irrelevant and misleading information to the representation of the “Socrates” target analog. The best mappings obtained for each object and predicate in the target, produced by three runs of ACME, are reported in Table 20. The asymptotic activations of the best mappings are also presented. A mapping of “none” means that no mapping unit had an asymptotic activation greater than .20. The run reported in the first column used the isomorphic version without any pragmatic weights. The network settles with a correct set of mappings after 34 cycles. Thus Socrates maps to the midwife, his student o the mother, the student’s intellectual partner to the father, and the idea to the child. (Note that there is a homomorphic mapping of the predicates thinks-about and tests-truth to in-labor-with.) The propositions expressing causal relations in the two analogs are not essential here; deletion of them still allows a complete mapping to be discovered. A very different set of mappings is reported in the middle column of Table 20 for the nonisomorphic version of the “Socrates” analog. This version provides additional knowledge about Socrates that would be expected to produce major interference with discovery of the metaphoric relation between the two analogs. The nonisomorphic version contains the information that Socrates drinks hemlock juice, which is of course irrelevant to the metaphor. Far worse, the representation encodes the information that Socrates him’self was matched to his wife by a midwife; and that Socrates’ wife had a child with the help of this midwife. Clearly, this nonisomorphic extension will cause the structural and semantic constraints on mapping to support a much more superficial set of correspondences between the two situations. And indeed, in this second run ACME finds only the barest fragments of the intended metaphoric mappings when the network settles ANALOGICAL MAPPING 345 TABLE 19 Predicate-Calculus Representatlans of Knowledge Underlying the Metaphor “Socrates is a Mldwlfe of Ideas”‘(lsamarphlc and Nanlsomorphlc Verslons)

Over the last three decades, a methodological pluralism has developed within information systems (IS) research. Various disciplines and many research communities as well, contribute to this discussion. However, working on the same research topic or studying the same phenomenon does not necessarily ensure mutual understanding. Especially within this multidisciplinary and international context, the epistemological assumptions made by different researchers may vary fundamentally. These assumptions exert a substantial impact on how concepts like validity, reliability, quality and rigour of research are understood. Thus, the extensive publication of epistemological assumptions is, in effect, almost mandatory. Hence, the aim of this paper is to develop an epistemological framework which can be used for systematically analysing the epistemological assumptions in IS research. Rather than attempting to identify and classify IS research paradigms, this research aims at a comprehensive discussion of epistemology within the context of IS. It seeks to contribute to building the basis for identifying similarities as well as differences between distinct IS approaches and methods. In order to demonstrate the epistemological framework, the consensus-oriented interpretivist approach to conceptual modelling is used as an example.

WordNet is perhaps the most important and widely used lexical resource for natural language processing systems up to now. WordNet: An Electronic Lexical Database, edited by Christiane Fellbaum, discusses the design of WordNet from both theoretical and historical perspectives, provides an up-to-date description of the lexical database, and presents a set of applications of WordNet. The book contains a foreword by George Miller, an introduction by Christiane Fellbaum, seven chapters from the Cognitive Sciences Laboratory of Princeton University, where WordNet was produced, and nine chapters contributed by scientists from elsewhere. Miller's foreword offers a fascinating account of the history of WordNet. He discusses the presuppositions of such a lexical database, how the top-level noun categories were determined, and the sources of the words in WordNet. He also writes about the evolution of WordNet from its original incarnation as a dictionary browser to a broad-coverage lexicon, and the involvement of different people during its various stages of development over a decade. It makes very interesting reading for casual and serious users of WordNet and anyone who is grateful for the existence of WordNet. The book is organized in three parts. Part I is about WordNet itself and consists of four chapters: "Nouns in WordNet" by George Miller, "Modifiers in WordNet" by Katherine Miller, "A semantic network of English verbs" by Christiane Fellbaum, and "Design and implementation of the WordNet lexical database and search software" by Randee Tengi. These chapters are essentially updated versions of four papers from Miller (1990). Compared with the earlier papers, the chapters in this book focus more on the underlying assumptions and rationales behind the design decisions. The description of the information contained in WordNet, however, is not as detailed as in Miller (1990). The main new additions in these chapters include an explanation of sense grouping in George Miller's chapter, a section about adverbs in Katherine Miller's chapter, observations about autohyponymy (one sense of a word being a hyponym of another sense of the same word) and autoantonymy (one sense of a word being an antonym of another sense of the same word) in Fellbaum's chapter, and Tengi's description of the Grinder, a program that converts the files the lexicographers work with to searchable lexical databases. The three papers in Part II are characterized as "extensions, enhancements and

The goal of giving a well-defined meaning to information is currently shared by endeavors such as the Semantic Web as well as by current trends within Knowledge Management. They all depend on the large-scale formalization of knowledge and on the availability of formal metadata about information resources. However, the question how to provide the necessary formal metadata in an effective and efficient way is still not solved to a satisfactory extent. Certainly, the most effective way to provide such metadata as well as formalized knowledge is to let humans encode them directly into the system, but this is neither efficient nor feasible. Furthermore, as current social studies show, individual knowledge is often less powerful than the collective knowledge of a certain community.As a potential way out of the knowledge acquisition bottleneck, we present a novel methodology that acquires collective knowledge from the World Wide Web using the GoogleTM API. In particular, we present PANKOW, a concrete instantiation of this methodology which is evaluated in two experiments: one with the aim of classifying novel instances with regard to an existing ontology and one with the aim of learning sub-/superconcept relations.


Question: Regarding the clarification of the term 'ontology'.

Constructing a Comprehensive Ontology for Machine Translation Enhancement

Introduction

Linguistic Resource Expansion

Knowledge-based machine translation (KBMT) systems have demonstrated remarkable performance in specialized domains, yet scaling them to handle the complexities of newspaper text remains a challenge. The bottleneck lies in the development of knowledge resources, including lexicons, grammar rules, and world models, which require extensive manual crafting. The PANGLOSS project explores the feasibility of semi-automatically acquiring these resources on a massive scale. This paper highlights the creation of a vast ontology, or knowledge base, to support KBMT systems. This ontology encompasses representations of approximately 70,000 common objects, processes, qualities, and relations.

The Construction Process

The ontology was developed through the amalgamation of diverse sources, such as online dictionaries, semantic networks, and bilingual resources, using semi-automated techniques. These methodologies, such as the conceptual alignment of semantic hierarchies, are adaptable for various tasks involving the transfer of knowledge between different knowledge bases. Other techniques, like bilingual mapping, enable knowledge engineers to construct indices in secondary languages, such as Spanish or Japanese, for a knowledge base originally in English.

This endeavor at the USC/Information Sciences Institute (4676 Admiralty Way, Marina del Rey, CA 90292), led by researchers knight and luk (knight,luk@isi.edu), showcases the potential for significantly enhancing the capabilities of KBMT systems through the strategic development of large-scale ontologies.

USC/Information Sciences Institute
4676 Admiralty Way
Marina del Rey, CA 90292
knight,luk@isi.edu

In organizing the knowledge in a field of study, it is common to use classification techniques to organize concepts and approaches along dimensions of interest. In technology domains, an advance often appears in the form of a new way or method for achieving an objective. This paper proposes to use goal modeling to map the means-ends knowledge (“know-how”) in a domain. A know-how map highlights the structure of recognized problems and known solutions in the domain, thus facilitating gap identification and prompting new research and innovation. We contrast the proposed goal-oriented approach with a claim-oriented approach, using Web Page Ranking as a sample domain.

The significance of employment in a setup of society is quite evident. Publishing jobs online opened the research opportunities to study different methods for the automation of job offers retrieval — from the internet. We need a classification mechanism, from machine learning or some other discipline, to automate job offers retrieval. In this paper, we devised an ontology-based classifier for job offers classification. More than 5000 job offers were collected from different job offers websites. The ontology-based classification works in three stages, concepts are extracted from job offers text description as feature vectors, the minimum threshold for the classification is calculated, and a classification model is developed. Rather using machine learning algorithms, we used an ontology for classification. We evaluated this classifier according to machine learning evaluation model, training, and test dataset. Our classifier showed more than 0.9 accuracy, precision, and recall for both training and test dataset. Our finding paves the way to automate the job offers classification and retrieval.

Knowledge can often be represented using entities connected by relations. For example, the fact that tennis ball is round can be represented as “TennisBall HasShape Round”, where a “TennisBall” is one entity, “HasShape” is a relation and “Round” is another entity. A knowledge base is a way to store such structured information, a knowledge base stores triples of the “an entity-relation-an entity” form, and a real world knowledge base often has millions or billions of such triples. There are several well-known knowledge bases including FreeBase [1], WordNet [2], YAGO [3], etc. They are important in fields like reasoning and question answering; for instance if one asks “what is the shape of a tennis ball”, we can search the knowledge base for the triple as “TennisBall HasShape Round” and output “round” as the answer.

Decoding the important differences in terminology, underlying philosophy, and value systems between two similar categories of software.

As part of the information fusion task we wish to automatically fuse information derived from the text extraction process with data from a structured knowledge base. This process will involve resolving, aggregating, integrating and abstracting information via the methodologies of Knowledge Representation and Reasoning into a single comprehensive description of an individual or event. This report surveys the key principles underlying research in the field of Knowledge Representation and Reasoning. It represents an initial step in deciding upon a Knowledge Representation and Reasoning system for our information fusion task. APPROVED FOR PUBLIC RELEASE

Geometry and Meaning is an interesting book about a relationship between geometry and logic defined on certain types of abstract spaces and how that intimate relationship might be exploited when applied in computational linguistics. It is also about an approach to information retrieval, because the analysis of natural language, especially negation, is applied to problems in IR, and indeed illustrated throughout the book by simple examples using search engines. It is refreshing to see IR issues tackled from a different point of view than the standard vector space (Salton, 1968). It is an enjoyable read, as intended by the author, and succeeds as a sort of tourist guide to the subject in hand. The early part of the book concentrates on the introduction of a number of elementary concepts from mathematics: graph theory, linear algebra (especially vector spaces), lattice theory, and logic. These concepts are well motivated and illustrated with good examples, mostly of a classificatory or taxonomic kind. One of the major goals of the book is to argue that non-classical logic, in the form of a quantum logic, is a candidate for analyzing language and its underlying logic, with a promise that such an approach could lead to improved search engines. The argument for this is aided by copious references to early philosophers, scientists, and mathematicians, creating the impression that when Aristotle, Descartes, Boole, and Grassmann were laying the foundations for taxonomy, analytical geometry, logic, and vector spaces, they had a more flexible and broader view of these subjects than is current. This is especially true of logic. Thus the historical approach taken to introducing quantum logic (chapter 7) is to show that this particular kind of logic and its interpretation in vector space were inherent in some of the ideas of these earlier thinkers. Widdows claims that Aristotle was never respected for his mathematics and that Grassmann’s Ausdehnungslehre was largely ignored and left in obscurity. Whether Aristotle was never admired for his mathematics I am unable to judge, but certainly Alfred North Whitehead (1925) was not complimentary when he said:

In general, landscape architecture includes analysis, planning, design, administration and management of natural and artificial. An important aspect is the formation of socalled sustainable landscapes that allow maximum use of the environment, natural resources and promote sustainable restoration of ecosystems. For such purposes, a designer needs a complete database with existing and suitable plants, but no designing tool has one. Therefore it is presented the structure and the development of on ontology suitable for storing and managing all information and knowledge about plants. The advantage is that the format of the ontology allows the storage of any plant species (e.g. live or fossil) and automated reasoning. Ontology is a formal conceptualization of a particular knowledge about the world, through the explicit representation of basic concepts, relations, and inference rules about themselves. Therefore the ontology may be used by a design tool for helping the designer and choosing the best options for a sustainable landscape. Keywords—environment; landscapes; ontology; ontology-based simulation; sustainable landscapes

Rules and ontologies play a key role in the layered architecture of the Semantic Web, as they are used to ascribe meaning to, and to reason about, data on the Web. While the Ontology Layer of the Semantic Web is quite developed, and the Web Ontology Language (OWL) is a W3C recommendation since a couple of years already, the rules layer is far less developed and an active area of research; a number of initiatives and proposals have been made so far, but no standard as been released yet. Many implementations of rule engines are around which deal with Semantic Web data in one or another way. This article gives a comprehensive, although not exhaustive, overview of such systems, describes their supported languages, and sets them in relation with theoretical approaches for combining rules and ontologies as foreseen in the Semantic Web architecture. In the course of this, we identify desired properties and common features of rule languages and evaluate existing systems against their support. Furthermore, we review technical problems underlying the integration of rules and ontologies, and classify representative proposals for theoretical integration approaches into different categories.

In his book “Conceptual Spaces The Geometry of Thought”, Peter Gärdenfors [1] presents a pioneering theory for representing conceptual knowledge, the basic construct of human thinking and reasoning [4]. The conceptual level is not seen as an alternative to traditional approaches of knowledge representation in artificial intelligence, namely symbolic or subsymbolic methods. Instead, it is meant to complement both approaches. The book is highly recommendable and worth reading, as it does not only tackle many fundamental problems of knowledge representation such as grounding [3], concept formation and similarity comparisons [2], but also outlines novel and enlightening ideas how to overcome these. The book introduces the notion of a conceptual space as a framework for representing knowledge at the conceptual level. It is motivated by contrasting it to other levels of knowledge representation: The highest level, the symbolic level, conceptualizes the human brain as a computing device. Knowledge is represented based on a language consisting of a set of symbols. Logical operations can be performed on these symbols to infer new knowledge. Human reasoning is modeled as a symbol manipulation process. Classical, symbolic artificial intelligence does not very well support central cognitive processes such as the acquisition or formation of new concepts and similarity comparisons. The lowest level, the subsymbolic knowledge representation, is oriented towards the neuro-biological structure of the human brain. Concepts are implicitly represented via activation patterns within the neural network. Learning is modeled by modifying the activation of neurons. Explicit representation of knowledge and concepts is not possible. At the intersection between the symbolic and the subsymbolic level, Gärdenfors introduces the conceptual level. The theory of conceptual spaces is based on semantic spaces with a geometric structure: A conceptual space is formed by a set of quality dimensions. One or several quality dimensions model one domain. An important example used throughout the book is the color domain represented by the quality dimensions hue, saturation and brightness. Conceptual spaces have a cognitive foundation because domains can be grounded in qualities perceivable by the human sensory apparatus. Concepts are represented as conceptual regions described by their properties on the quality dimensions. The geometric structure of conceptual spaces makes it possible to determine distances and therefore provides an inherent similarity measure by taking the distance in the conceptual space as indicator of the semantic similarity. The notion of similarity is an important construct for modeling categorization and concept formation. Using similarity for reasoning can also reflect well the vagueness typical for human reasoning. The strong focus on the cognitive foundation makes the book particularly valuable. It contains many challenging claims which are related to various disciplines by giving evidence from a wide range of literature. This shows the huge and highly interdisciplinary background of the author. Unfortunately, Gärdenfors describes his theory only at a very abstract level and forbears from describing algorithms for the formalization of his theory. The realization of a computational model for conceptual spaces bears many practical problems which still have to be solved. Moreover, no empirical evidence is given for his pioneering, sometimes revolutionary ideas. However, these shortcomings should be considered as challenges to solve in the future. The target audience of the book is highly interdisciplinary: since Gärdenfors tackles the problem of cognitive knowledge representation from a psychologic and computer science perspective as well as from a philosophic, neuroscience and linguistic point of view, this book is worth reading for researchers from many different areas. It is required reading for researchers in cognitive science or artificial intelligence interested in knowledge representation. The book has a clear structure and is very well written. The convincing examples throughout the book illustrate the findings very well and make it easy to understand. Therefore I would also deem Gärdenfors’ book to be suitable for students as introducing literature to various problem fields in cognitive science. It gives readers from related areas the chance to look beyond one’s own nose and get to know an interdisciplinary way of thinking. The book certainly meets the expectations of the highly interdisciplinary research area cognitive science.

Fine-grained sense distinctions are one of the major obstacles to successful Word Sense Disambiguation. In this paper, we present a method for reducing the granularity of the WordNet sense inventory based on the mapping to a manually crafted dictionary encoding sense hierarchies, namely the Oxford Dictionary of English. We assess the quality of the mapping and the induced clustering, and evaluate the performance of coarse WSD systems in the Senseval-3 English all-words task.


Question: Regarding the clarification of the term 'ontology'.

This paper delves into the foundational principles guiding the creation of ontologies aimed at facilitating knowledge sharing among software entities within the realm of Artificial Intelligence. From an engineering standpoint, the development of formal ontologies is approached as a deliberate design process, with these ontologies being seen as purpose-built artifacts that can be objectively assessed against a set of predefined design criteria. The authors outline the critical role ontologies play in enabling knowledge sharing activities and propose a framework of criteria to inform the development of ontologies for such purposes. Through case studies focused on the design of ontologies for engineering mathematics and bibliographic data, the application of these criteria is illustrated. The paper further discusses key design decisions, evaluates alternative representation options, and assesses their alignment with the established design criteria.

In the cultural heritage domain information systems are increasingly deployed, digital representations of physical objects are produced in immense numbers and there is a strong political pressure on memory institutions to make their holdings accessible to the public in digital form. The sector splits into a set of disciplines with highly specialized fields. Due to the resulting diversity, one can hardly speak about a “domain” in the sense of “domain ontologies” [33]. On the other side, study and research of the past is highly interdisciplinary. Characteristically, archaeology employs a series of “auxiliary” disciplines, such as archaeometry, archaeomedicine, archaeobotany, archaeometallurgy, archaeoastronomy, etc., but also historical sources and social theories. Interoperability between various highly specialized systems, integrated information access and information integration increasingly becomes a demand to support research, professional heritage administration, preservation, public curiosity and education. Therefore the sector is characterized by a complex schema integration problem of associating complementary information from various dedicated systems, which can be efficiently addressed by formal ontologies [14,32,33]. There is a proliferation of specialized terminology, but terminology is less used as a means of agreement between experts than as an intellectual tool for hypothesis building based on discriminating phenomena. Automated classification is a long established discipline of archaeology, but few terminological systems are widely accepted. The sector is, however, more focused on establishing knowledge about facts and context in the past than about classes of things and the laws of their behavior. Respectively, the concatenation of related facts by co-reference [56] to particulars, such as things, people, places, periods is a major open issue. Knowledge Organisation Systems (KOS, [62]) describing people and places are employed to a certain degree, and pose similar technical problems as ontologies, but the required scale is very large. In this chapter, we describe how ontologies are and could be employed to improve information management in the cultural heritage sector.

In this paper, we present an approach based on Ontology Learning and Natural Language Processing for automatic construction of expressive Ontologies, specifically in OWL DL with ALC expressivity, from a natural language text. The viability of our approach is demonstrated through the generation of descriptions of complex axioms from concepts defined by users and glossaries found at Wikipedia. We evaluated our approach in an experiment with entry sentences enriched with hierarchy axioms, disjunction, conjunction, negation, as well as existential and universal quantification to impose restriction of properties. The obtained results prove that our model is an effective solution for knowledge representation and automatic construction of expressive Ontologies. Thereby, it assists professionals involved in processes for obtain, construct and model knowledge domain.

The concept of thesaurus has evolved from a list of conceptually interrelated words to today's controlled vocabularies, where terms form complex structures through semantic relationships. This term comes from the Latin and has turn been derived from the Greek "θησαυρός", which means treasury according to the Spanish Royal Academy, in whose dictionary it is also defined as: 'name given by its authors to certain dictionaries, catalogues and anthologies'. The increase in scientific communication and productivity made it essential to develop keyword indexing systems. At that time, Howerton spoke of controlled lists to refer to concepts that were heuristically or intuitively related. According to Roberts (1984), Mooers was the first to relate thesauri to information retrieval systems; Taube established the foundations of post-coordination, while Luhn dealt, at a basic level, with the creation of thesauri using automatic techniques. Brownson (1957) was the first to use the term to refer to the issue of translating concepts and their relationships expressed in documents into a more precise language free of ambiguities in order to facilitate information retrieval. The ASTIA Thesaurus was published in the early 1960s (Currás, 2005), already bearing the characteristics of today's thesauri and taking on the need for a tool to administer a controlled vocabulary in terms of indexing, thereby giving rise to the concept of documentary language.

This article discusses about the marketing theory from the perspective of philosophy of science. The discussion focused on the aspects of the ontology, epistemology, and axiology. From the aspect of the ontology, it seems that the essence of marketing is a useful science and mutually beneficial for both the marketer and the stakeholders, or in other words that marketing knowledge is useful knowledge for the benefit of humankind that can be realized through the exchange process. Side of the ontology covers what the substance of marketing knowledge, the substance of truth and reality that is inherent with marketing. Meanwhile, aspects of epistemology cover a variety of approaches, methods, sources, structure and validation or marketing truth. Finally, axiology fields are related to ethics in marketing and marketing research. Marketing ethics and ethics in marketing research is a crucial matter, including in this case is trust.

As the editors of this volume remind us, polysemy has been a vexing issue for the understanding of language since antiquity. For half a century, it has been a major bottleneck for natural language processing. It contributed to the failure of early machine translation research (remember Bar-Hillel’s famous pen and box example) and is still plaguing most natural language processing and information retrieval applications. A recent issue of this journal described the state of the art in automatic sense disambiguation (Ide and Véronis 1998), and Senseval system competitions have revealed the immense difficulty of the task (http://www.sle.sharp.co.uk/senseval2). However, no significant progress can be made on the computational aspects of polysemy without serious advances in theoretical issues. At the same time, theoretical work can be fostered by computational results and problems, and language-processing applications can provide a unique test bed for theories. It was therefore an excellent idea to gather both theoretical and applied contributions in the same book. Yael Ravin and Claudia Leacock are well-known names to those who work on the theoretical and computational aspects of word meaning. In this volume, they bring together a collection of essays from leading researchers in the field. As far as I can tell, these essays are not reprints or expanded versions of conference papers, as is often the case for edited works; instead, they seem to have been specially commissioned for the purposes of this book, which makes it even more exciting to examine. The book is composed of 11 chapters. It is not formally divided into parts, but chapters dealing more specifically with the computational aspects of polysemy are grouped together at the end (and constitute about one-third of the volume). Chapter 1 is an overview written by the volume editors. Yael Ravin and Claudia Leacock survey the main theories of meaning and their treatment of polysemy. These include the classical Aristotelian approach revived by Katz and Fodor (1963); Rosch’s (1977) prototypical approach, which has its roots in Wittgenstein’s Philosophical Investigations (1953); and the relational approach recently exemplified by WordNet (Fellbaum 1998), which (although the authors do not mention it) can be traced back to Peirce’s (1931–1958) and Selz’s (1913, 1922) graphs and which gained popularity with Quillian’s (1968) semantic networks. In the course of this overview, Ravin and Leacock put the individual chapters into perspective by relating them to the various theories.

In the context of ontology engineering, the ontology understanding is the basis for its further development and reuse. One intuitive effective approach to support ontology understanding is the process of ontology summarization which highlights the most important concepts of an ontology. Ontology summarization identifies an excerpt from an ontology that contains the most relevant concepts and produces an abridged ontology. In this article, we present a method for summarizing ontologies that represent a data source schema or describe a knowledge domain. We propose an algorithm to produce a personalized ontology summary based on user-defined parameters, e.g. summary size. The relevance of a concept is determined through user indication or centrality measures, commonly used to determine the relative importance of a vertex within a graph. The algorithm searches for the best ontology summary, i.e., the one containing the most relevant ontology concepts respecting all the original relationships between concepts and the parameters set by the user. Experiments were done comparing our generated ontology summaries against golden standard summaries as well as summaries produced by methods available in related work. We achieved in average more than 62.5% of similarity with golden standard summaries.

The research objective is to establish an approach for supporting the classification of text documents referring to a specified domain. The focus is on the preliminary topic assignment to the documents used for training the model. The method implements domain ontology as background knowledge. The idea consists in extracting the preliminary topics for training the classifier by means of unsupervised machine learning on a text corpus and further alignment of the document vectors to concepts of the ontology. The results obtained by classification of new documents supervised by e-governance ontology with several machine learning algorithms showed sufficient match of their content to the ontology concepts. A conclusion is drawn that the approach can support the automatic extraction of documents relevant to any domain described by ontology.

Our work is concerned with the development of knowledge structures to support correct-by-design cyber-physical systems (CPS). This class of systems is defined by a tight integration of software and physical processes, the need to satisfy stringent constraints on performance and safety, and a reliance on automation for the management of system functionality and decision making. To assure correctness of functionality with respect to requirements, there is a strong need for system models to account for semantics of the domains involved. This paper introduces a new ontological-based knowledge and reasoning framework for decision support for CPS. It enables the development of determinate, provable and executable CPS models supported by sound semantics strengthening the model-driven approach to CPS design. An investigation into the structure of basic description logics (DL) has identified the needed semantic extensions to enable the web ontology language (OWL) as the ontological language for our framework. The SROIQ DL has been found to be the most appropriate logic-based knowledge formalism as it maps to OWL 2 and ensures its decidability. Thus, correct, stable, complete and terminating reasoning algorithms are guaranteed with this SROIQ-backed language. The framework takes advantage of the commonality of data and information processing in the different domains involved to overcome the barrier of heterogeneity of domains and physics in CPS. Rules-based reasoning processes are employed. The framework provides interfaces for semantic extensions and computational support, including the ability to handle quantities for which dimensions and units are semantic parameters in the physical world. Together, these capabilities enable the conversion of data to knowledge and their effective use for efficient decision making and the study of system-level properties, especially safety. We exercise these concepts in a traffic light time-based reasoning system. 2016 Elsevier Ltd. All rights reserved.

Nilsson, N.J., Logic and artificial intelligence, Artificial Intelligence 47 (1990) 31-56. The theoretical foundations of the logical approach to artificial intelligence are presented. Logical languages are widely used for expressing the declarative knowledge needed in artificial intelligence systems. Symbolic logic also provides a clear semantics for knowledge representation languages and a methodology for analyzing and comparing deductive inference techniques. Several observations gained from experience with the approach are discussed. Finally, we confront some challenging problems for artificial intelligence and describe what is being done in an attempt to solve them.

In the present paper, we present an automated tagging approach aimed at enhancing a well-known resource, the ACL Anthology Reference Corpus, with semantic class labels for more than 20,000 technical terms that are relevant to the domain of computational linguistics. We use state-of-the-art classification techniques to assign semantic class labels to technical terms extracted from several reference term lists. We also sketch a set of research questions and approaches directed towards the integrated analysis of scientific corpora. To this end, we query the data set resulting from our annotation effort on both the term and the semantic class level level.