Question: An overview of various file system methodologies.

This paper introduces an innovative indexing technique, Multi-Probe Locality Sensitive Hashing (LSH), designed to enhance the efficiency of similarity search in high-dimensional data environments. High-dimensional similarity indices are critical for the development of content-based search systems tailored to rich media data such as audio, images, videos, and sensor outputs. Existing methods, including LSH and its variants, have faced a significant challenge in requiring a substantial number of hash tables to ensure high search quality. The proposed Multi-Probe LSH scheme overcomes this limitation by selectively probing multiple buckets within a hash table, where query results are likely to reside, thereby improving search accuracy without excessive resource consumption. This approach builds upon the foundational LSH technique but introduces a more intelligent probing mechanism. It is influenced by and represents an advancement over recent theoretical work on entropy-based LSH, which aimed to minimize the space requirements of traditional LSH. The Multi-Probe LSH method has been implemented and rigorously tested using two distinct high-dimensional datasets. The results demonstrate a significant enhancement in both space and time efficiency compared to previous approaches. To achieve equivalent search quality, Multi-Probe LSH matches the time efficiency of basic LSH while remarkably reducing the number of required hash tables by a factor of ten. In contrast to entropy-based LSH, Multi-Probe LSH achieves the same search quality with lower query time and a reduction of 5 to 8 times in the number of hash tables.

Disk-oriented approaches to online storage are becoming increasingly problematic: they do not scale gracefully to meet the needs of large-scale Web applications, and improvements in disk capacity have far outstripped improvements in access latency and bandwidth. This paper argues for a new approach to datacenter storage called RAMCloud, where information is kept entirely in DRAM and large-scale systems are created by aggregating the main memories of thousands of commodity servers. We believe that RAMClouds can provide durable and available storage with 100-1000x the throughput of disk-based systems and 100-1000x lower access latency. The combination of low latency and large scale will enable a new breed of dataintensive applications.

This paper reviews some of the difficulties that arise in the verification of kernelized secure systems and suggests new techniques for their resolution.
 It is proposed that secure systems should be conceived as distributed systems in which security is achieved partly through the physical separation of its individual components and partly through the mediation of trusted functions performed within some of those components. The purpose of a security kernel is simply to allow such a 'distributed' system to actually run within a single processor; policy enforcement is not the concern of a security kernel.
 This approach decouples verification of components which perform trusted functions from verification of the security kernel. This latter task may be accomplished by a new verification technique called 'proof of separability' which explicitly addresses the security relevant aspects of interrupt handling and other issues ignored by present methods.

Many organizations collect vast amounts of customer, scientific, sales, and other data for future analysis. Traditionally, most of these organizations have stored structured data in relational databases for subsequent access and analysis. However, a growing number of developers and users have begun turning to various types of nonrelational, now frequently called NoSQL-databases. Nonrelational databases, including hierarchical, graph, and object-oriented databases-have been around since the late 1960s. However, new types of NoSQL databases are being developed. And only now are they beginning to gain market traction. Different NoSQL databases take different approaches. What they have in common is that they're not relational. Their primary advantage is that, unlike relational databases, they handle unstructured data such as word-processing files, e-mail, multimedia, and social media efficiently. This paper discuss issues such as limitations, advantages, concerns and doubts regarding NoSQL databases.

As the world moves to digital storage for archival purposes, there is an increasing demand for systems that can provide secure data storage in a cost-effective manner. By identifying common chunks of data both within and between files and storing them only once, deduplication can yield cost savings by increasing the utility of a given amount of storage. Unfortunately, deduplication exploits identical content, while encryption attempts to make all content appear random; the same content encrypted with two different keys results in very different ciphertext. Thus, combining the space efficiency of deduplication with the secrecy aspects of encryption is problematic.
 We have developed a solution that provides both data security and space efficiency in single-server storage and distributed storage systems. Encryption keys are generated in a consistent manner from the chunk data; thus, identical chunks will always encrypt to the same ciphertext. Furthermore, the keys cannot be deduced from the encrypted chunk data. Since the information each user needs to access and decrypt the chunks that make up a file is encrypted using a key known only to the user, even a full compromise of the system cannot reveal which chunks are used by which users.

Traditional database systems offer rich query interfaces (SQL) and efficient query execution for data that they store. Recent years have seen the rise of Big Data analytics platforms offering query-based access to "raw" external data, e.g., file-resident data (often in HDFS). In this paper, we describe techniques to achieve the qualities offered by DBMSs when accessing external data. This work has been built into Apache AsterixDB, an open source Big Data Management System. We describe how we build distributed indexes over external data, partition external indexes, provide query consistency across access paths, and manage external indexes amidst concurrent activities. We compare the performance of this new AsterixDB capability to an external-only solution (Hive) and to its internally managed data and indexes.

This paper introduces an efficient technique called “stack processing” that can be used in the cost-performance evaluation of a large class of storage hierarchies. The technique depends on a classification of page replacement algorithms as “stack algorithms” for which various properties are derived. These properties may be of use in the general areas of program modeling and system analysis, as well as in the evaluation of storage hierarchies. For a better understanding of storage hierarchies, we briefly review some basic concepts of their design.

The chunk-lookup disk bottleneck and the read amplification problems are two great challenges for deduplication-based storage systems and restrict the applicability of data deduplication for large-scale data volumes. Previous studies and our experimental evaluations have shown that the amount of redundant data shared among different types of applications is negligible. Based on the observations, we propose AA-Plus which effectively groups the hash index of the same application together and divides the whole hash index into different groups based on the application types. Moreover, it groups the data chunks of the same application together on the disks. The extensive trace-driven experiments conducted on our lightweight prototype implementation of AA-Plus show that compared with AA-Dedupe, AA-Plus significantly speeds up the write throughput by a factor of up to 6.9 and with an average of 3.1, and speeds up the read throughput by a factor of up to 3.3 and with an average of 1.9.

Cloud storage services commonly use deduplication, which eliminates redundant data by storing only a single copy of each file or block. Deduplication reduces the space and bandwidth requirements of data storage services, and is most effective when applied across multiple users, a common practice by cloud storage offerings. We study the privacy implications of cross-user deduplication. We demonstrate how deduplication can be used as a side channel which reveals information about the contents of files of other users. In a different scenario, deduplication can be used as a covert channel by which malicious software can communicate with its control center, regardless of any firewall settings at the attacked machine. Due to the high savings offered by cross-user deduplication, cloud storage providers are unlikely to stop using this technology. We therefore propose simple mechanisms that enable cross-user deduplication while greatly reducing the risk of data leakage.

Designations used by companies to distinguish their products are often claimed as trademarks or registered trademarks. In all instances in which Morgan Kaufmann Publishers is aware of a claim, the product names appear in initial capital or all capital letters. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopying, scanning, or otherwise—without prior written permission of the publisher. may also complete your request on-line via the Elsevier homepage (http://elsevier.com), by selecting " Support & Contact " then " Copyright and Permission " and then " Obtaining Permissions. " Page 197: " Make a new plan Stan. .. and get yourself free. Physical database design : the database professional's guide to exploiting indexes, views, storage, and more / Sam Lightstone, Toby Teorey, and Tom Nadeau. p. cm.-(The Morgan Kaufmann series in database management systems) Includes bibliographical references and index.


Question: An overview of various file system methodologies.

This paper presents SmartStore, a novel metadata organization paradigm that addresses the scalability and functionality challenges faced by traditional hierarchical directory tree storage systems in managing vast datasets and sophisticated queries in Exabyte-scale environments with billions of files. SmartStore introduces semantic-aware organization, where metadata semantics of files are leveraged to intelligently aggregate related files into groups with semantic coherence, facilitated by information retrieval techniques. This decentralized approach not only enhances system scalability but also significantly reduces query latency for intricate queries, such as range and top-k queries. SmartStore is designed to support semantic-aware caching and conventional filename-based queries efficiently. By confining the search scope for complex queries to a single or a minimal number of semantically related groups, SmartStore effectively eliminates or minimizes the need for exhaustive searches across the entire system. Our comprehensive experimental evaluation, conducted using real-world datasets, demonstrates that SmartStore achieves a thousand-fold improvement in system scalability and query latency compared to fundamental database methods. To our knowledge, SmartStore represents the pioneering effort in implementing complex queries within large-scale file systems.

Description: The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "Cluster analysis is the increasingly important and practical subject of finding groupings in data. The authors set out to write a book for the user who does not necessarily have an extensive background in mathematics. They succeed very well." —Mathematical Reviews "Finding Groups in Data [is] a clear, readable, and interesting presentation of a small number of clustering methods. In addition, the book introduced some interesting innovations of applied value to clustering literature." —Journal of Classification "This is a very good, easy-to-read, and practical book. It has many nice features and is highly recommended for students and practitioners in various fields of study." —Technometrics An introduction to the practical application of cluster analysis, this text presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability. This book discusses various types of data, including interval-scaled and binary variables as well as similarity data, and explains how these can be transformed prior to clustering.

Increasingly advances in file carving, memory analysis and network forensics requires the ability to identify the underlying type of a file given only a file fragment. Work to date on this problem has relied on identification of specific byte sequences in file headers and footers, and the use of statistical analysis and machine learning algorithms taken from the middle of the file. We argue that these approaches are fundamentally flawed because they fail to consider the inherent internal structure in widely used file types such as PDF, DOC, and ZIP. We support our argument with a bottom-up examination of some popular formats and an analysis of TK PDF files. Based on our analysis, we argue that specialized methods targeted to each specific file type will be necessary to make progress in this area.

B-trees have been ubiquitous in database management systems for several decades, and they are used in other storage systems as well. Their basic structure and basic operations are well and widely understood including search, insertion, and deletion. Concurrency control of operations in B-trees, however, is perceived as a difficult subject with many subtleties and special cases. The purpose of this survey is to clarify, simplify, and structure the topic of concurrency control in B-trees by dividing it into two subtopics and exploring each of them in depth.

There are many interface schemes that allow users to work at, and move between, focused and contextual views of a dataset. We review and categorize these schemes according to the interface mechanisms used to separate and blend views. The four approaches are overview+detail, which uses a spatial separation between focused and contextual views; zooming, which uses a temporal separation; focus+context, which minimizes the seam between views by displaying the focus within the context; and cue-based techniques which selectively highlight or suppress items within the information space. Critical features of these categories, and empirical evidence of their success, are discussed. The aim is to provide a succinct summary of the state-of-the-art, to illuminate both successful and unsuccessful interface strategies, and to identify potentially fruitful areas for further work.

As hard disk encryption, RAM disks, persistent data avoidance technology and memory resident malware become morewidespread, memory analysis becomes more important. In order to provide more virtual memory than is actually physicalpresent on a system, an operating system may transfer frames of memory to a pagefile on persistent storage. Current memoryanalysis software does not incorporate such pagefiles and thus misses important information. We therefore present a detailedanalysis of Windows NT paging. We use dynamic gray-box analysis, in which we place known data into virtual memory andexamine where it is mapped to, in either the physical memory or the pagefile, and cross-reference these findings with theWindows NT Research Kernel source code. We demonstrate how to decode the non-present page table entries, and accuratelyreconstruct the complete virtual memory space, including non-present memory pages on Windows NT systems using 32-bit,PAE or IA32e paging. Our analysis approach can be used to analyze other operating systems as well.

Numerous systems have been designed which use virtualization to subdivide the ample resources of a modern computer. Some require specialized hardware, or cannot support commodity operating systems. Some target 100% binary compatibility at the expense of performance. Others sacrifice security or functionality for speed. Few offer resource isolation or performance guarantees; most provide only best-effort provisioning, risking denial of service.This paper presents Xen, an x86 virtual machine monitor which allows multiple commodity operating systems to share conventional hardware in a safe and resource managed fashion, but without sacrificing either performance or functionality. This is achieved by providing an idealized virtual machine abstraction to which operating systems such as Linux, BSD and Windows XP, can be ported with minimal effort.Our design is targeted at hosting up to 100 virtual machine instances simultaneously on a modern server. The virtualization approach taken by Xen is extremely efficient: we allow operating systems such as Linux and Windows XP to be hosted simultaneously for a negligible performance overhead --- at most a few percent compared with the unvirtualized case. We considerably outperform competing commercial and freely available solutions in a range of microbenchmarks and system-wide tests.

Traditional database systems offer rich query interfaces (SQL) and efficient query execution for data that they store. Recent years have seen the rise of Big Data analytics platforms offering query-based access to "raw" external data, e.g., file-resident data (often in HDFS). In this paper, we describe techniques to achieve the qualities offered by DBMSs when accessing external data. This work has been built into Apache AsterixDB, an open source Big Data Management System. We describe how we build distributed indexes over external data, partition external indexes, provide query consistency across access paths, and manage external indexes amidst concurrent activities. We compare the performance of this new AsterixDB capability to an external-only solution (Hive) and to its internally managed data and indexes.

ii Acknowledgements The research and writing of this thesis has been one of the most significant academic challenges I ever had to face. Without the support and guidance of the following people, this study would not have been completed. It is to them that I owe my deepest gratitude. I would sincerely like to thank my advisor, Dr. Golden G. Richard III for giving me an opportunity to conduct this thesis research under his excellent guidance. His exceptional knowledge, wisdom and understanding have inspired and motivated me. My professors, Dr. Shengru Tu and Dr. Adlai DePano for being on my thesis committee and mentoring me during my Master's degree in Computer Science at University of New Orleans. I am thankful to my friend Thomas Sires, for initiating my interest in Linux, making sure I keep it up and help me solve technical issues. I appreciate the help from Aisha Ali-Gombe, Joe Sylve and Andrew Case who are on our Information Assurance group, for their contributions in the Information Assurance field and answering my questions. Finally, I would like to thank my Parents and each member in my family and friends for their unconditional love and support. I specially want to thank my husband Mr. Naveen Singh, who has been my core source of strength through every phase of this effort and constantly encouraged me to do my best. His systematic approach and kind feedback towards my thesis has helped me improve. Words cannot express how grateful I am to you. My heartfelt gratitude to my precious daughter Navya Singh, who was born during my Master's program and has spent many days without my complete attention. With her never give up attitude she has taught me perseverance to work around a problem until I succeed. Thank you for all your love my little girl, it kept me going. Abstract Android forensics has evolved over time offering significant opportunities and exciting challenges. On one hand, being an open source platform Android is giving developers the freedom to contribute to the rapid growth of the Android market whereas on the other hand Android users may not be aware of the security and privacy implications of installing these applications on their phones. Users may assume that a password-locked device protects their personal information, but applications may retain private information on devices, in ways that users might not anticipate. In this thesis we will be …

One measure of usefulness of a general-purpose distributed computing system is the system’s ability to provide a level of performance commensurate to the degree of multiplicity of resources present in the system. Many different approaches and metrics of performance have been proposed in an attempt to achieve this goal in existing systems. In addition, analogous problem formulations exist in other fields such as control theory, operations research, and production management. However, due to the wide variety of approaches to this problem, it is difficult to meaningfully compare different systems since there is no uniform means for qualitatively or quantitatively evaluating them. It is difficult to successfully build upon existing work or identify areas worthy of additional effort without some understanding of the relationships between past efforts. In this paper, a taxonomy of approaches to the resource management problem is presented in an attempt to provide a common terminology and classification mechanism necessary in addressing this problem. The taxonomy, while presented and discussed in terms of distributed scheduling, is also applicable to most types of resource management. As an illustration of the usefulness of the taxonomy an annotated bibliography is given which classifies a large number of distributed scheduling approaches according to the taxonomy.

Personal cloud storage services are data-intensive applications already producing a significant share of Internet traffic. Several solutions offered by different companies attract more and more people. However, little is known about each service capabilities, architecture and -- most of all -- performance implications of design choices. This paper presents a methodology to study cloud storage services. We apply our methodology to compare 5 popular offers, revealing different system architectures and capabilities. The implications on performance of different designs are assessed executing a series of benchmarks. Our results show no clear winner, with all services suffering from some limitations or having potential for improvement. In some scenarios, the upload of the same file set can take seven times more, wasting twice as much capacity. Our methodology and results are useful thus as both benchmark and guideline for system design.


Question: An overview of various file system methodologies.

Addressing the Storage Conundrum: Facebook's Innovative Photo Management System

In response to the challenge of efficiently storing and retrieving metadata for photos on storage machines, Facebook developed a novel architecture that optimizes memory utilization and performance. The traditional approach of storing each photo in a separate file proved inadequate for their scale, leading to the decision to bundle multiple photos within a single file. This strategy significantly reduced metadata overhead, enabling more efficient in-memory storage of metadata. The resulting architecture, termed Haystack, comprises three key components: the Haystack Store, Haystack Directory, and Haystack Cache.

The Haystack Store serves as the persistent storage backbone, while the Haystack Directory facilitates photo location and storage management. The Haystack Cache, a crucial element for rapid photo access, especially for those not readily available in the Content Delivery Network (CDN), handles high-demand requests from users. The system's design allowed Facebook the flexibility to integrate a CDN or bypass it entirely, always leveraging the Cache to tackle the "long tail" problem, where less frequently accessed photos still incur substantial user requests. Any user request, whether for a photo in the Cache or fetched from a write-enabled machine, is promptly stored in the Cache.

To safeguard against the loss of in-memory data structures due to failures, Haystack employed a strategy of storing these structures in index files, which are asynchronously updated relative to writes, thus preventing performance degradation. To manage file modifications efficiently, Haystack adopted an append-only approach for modified files at the end of the file, alongside a record of deletions for the old file version. Files marked for deletion, including previous versions of modified files, are systematically removed through a process known as compaction. This operation consolidates files not marked for deletion or only the latest versions of modified files into a new disk file, ensuring that deletions and modifications are handled asynchronously from writes, thereby maintaining efficiency.

Facebook's Haystack architecture exemplifies an innovative solution to the complex problem of managing vast photo collections, demonstrating the effectiveness of a tailored approach to file system design.

As the world moves to digital storage for archival purposes, there is an increasing demand for systems that can provide secure data storage in a cost-effective manner. By identifying common chunks of data both within and between files and storing them only once, deduplication can yield cost savings by increasing the utility of a given amount of storage. Unfortunately, deduplication exploits identical content, while encryption attempts to make all content appear random; the same content encrypted with two different keys results in very different ciphertext. Thus, combining the space efficiency of deduplication with the secrecy aspects of encryption is problematic.
 We have developed a solution that provides both data security and space efficiency in single-server storage and distributed storage systems. Encryption keys are generated in a consistent manner from the chunk data; thus, identical chunks will always encrypt to the same ciphertext. Furthermore, the keys cannot be deduced from the encrypted chunk data. Since the information each user needs to access and decrypt the chunks that make up a file is encrypted using a key known only to the user, even a full compromise of the system cannot reveal which chunks are used by which users.

Sam H. Nohs Sang Lyul Mint t Department of Computer Engineering Seoul National University Seoul 151-742, Korea http://ssrnet.snu.ac.kr http://archi.snu.ac.kr We show that there exists a spectrum of block replacement policies that subsumes both the Least Recently Used (LRU) and the Least Frequently Used (LFU) policies. The spectrum is formed according to how much more weight we give to the recent history than to the older history, and is referred to as the LRFU (Least Recently/Frequently Used) policy. Unlike many previous policies that use limited history to make block replacement decisions, the LRFU policy uses the complete reference history of blocks recorded during their cache residency. Nevertheless, the LRFU requires only a few words for each block to maintain such history. This paper also describes an implementation of the LRFU that again subsumes the LRU and LFU implementations. The LRFU policy is applied to buffer caching, and results from trace-driven simulations show that the LRFU performs better than previously known policies for the workloads we considered. This point is reinforced by results from our integration of the LRFU into the FreeBSD operating system.

Value of software log file analysis has been constantly increasing with the value of information to organizations. Log management tools still have a lot to deliver in order to empower their customers with the true strength of log information. In addition to the traditional uses such as testing software functional conformance, troubleshooting and performance benchmarking, log analysis has proven its capabilities in fields like intrusion detection and compliance evaluation. This is verified by the emphasis on log analysis in regulations like PCI DSS, FISMA, HIPAA and frameworks such as ISO 27001 and COBIT. In this paper we present an in depth analysis into current log analysis domains and common problems. A practical guide to the use of few popular log analysis tools is also included. Lack of proper support for structured analysis is identified as one major flaw in existing tools. After that, we describe a framework we developed for structured log analysis with the view of providing a solution to open problems in the domain. The core strength of the framework is its ability to handle many log file formats that are not well served by existing tools and providing sophisticated infrastructure for automating recurring log analysis procedures. We prove the usefulness of the framework with a simple experiment.

We have designed and implemented a new operating system facility for I/O buffer management and data transferacross protection domain boundaries on shared memory machines. This facility, called fast buffers (fbufs), combines virtual page remapping with shared virtual memory, and exploits locality in I/O traffic to achieve high throughput without compromising protection, security, or modularity. goal is to help deliver the high bandwidth afforded by emerging high-speed networks to user-level processes, both in monolithic and microkernel-based operating systems.This paper outlines the requirements for a cross-domain transfer facility, describes the design of the fbuf mechanism that meets these requirements, and experimentally quantifies the impact of fbufs on network performance.

There are many interface schemes that allow users to work at, and move between, focused and contextual views of a dataset. We review and categorize these schemes according to the interface mechanisms used to separate and blend views. The four approaches are overview+detail, which uses a spatial separation between focused and contextual views; zooming, which uses a temporal separation; focus+context, which minimizes the seam between views by displaying the focus within the context; and cue-based techniques which selectively highlight or suppress items within the information space. Critical features of these categories, and empirical evidence of their success, are discussed. The aim is to provide a succinct summary of the state-of-the-art, to illuminate both successful and unsuccessful interface strategies, and to identify potentially fruitful areas for further work.

Now a day’s Peta-Bytes of data becomes the norm in industries. Handling, analyzing such big data is challenging task. Even frameworks like Hadoop (Open Source Implementation of MapReduce Paradigm) and NoSQL databases like Cassandra, HBase can be used to analyze and store such large data; heterogeneity of data is still an issue. Data centers usually have clusters formed using heterogeneous nodes. Ecosystem like Hadoop can be used to manage such types of cluster but it can not schedule jobs (Application) efficiently on this heterogeneous cluster when data is itself heterogeneous. Heterogeneity of data may be because of data format or because of the complexity. This paper is review of systems and algorithms for distributed management of huge size data, efficiency of these approaches.

VMware ESX Server is a thin software layer designed to multiplex hardware resources efficiently among virtual machines running unmodified commodity operating systems. This paper introduces several novel ESX Server mechanisms and policies for managing memory. A ballooning technique reclaims the pages considered least valuable by the operating system running in a virtual machine. An idle memory tax achieves efficient memory utilization while maintaining performance isolation guarantees. Content-based page sharing and hot I/O page remapping exploit transparent page remapping to eliminate redundancy and reduce copying overheads. These techniques are combined to efficiently support virtual machine workloads that overcommit memory.

Within computer science, the term provenance has multiple meanings, due to different motivations, perspectives, and assumptions prevalent in the respective communities. This chapter provides a high-level “sightseeing tour” of some of those different notions and uses of provenance in scientific workflows and databases.

The Source Code Control System (SCCS) is a software tool designed to help programming projects control changes to source code. It provides facilities for storing, updating, and retrieving all versions of modules, for controlling updating privileges for identifying load modules by version number, and for recording who made each software change, when and where it was made, and why. This paper discusses the SCCS approach to source code control, shows how it is used and explains how it is implemented.

NAND flash storage is widely adopted in all classes of computing devices. However, random write performance and lifetime issues remain to be addressed. In this paper, we propose a novel buffer replacement algorithm called TS-CLOCK that effectively resolves the remaining problems. Our experimental results show that TS-CLOCK outperforms state-of-the-art algorithms in terms of performance and lifetime.


Question: Entity set augmentation through the utilization of knowledge graphs.

This paper explores the enhancement of RDF (Resource Description Framework) data through the application of association rule mining, a technique that holds promise for the integration and utilization of the vast and diverse datasets comprising Linked Open Data. RDF datasets, characterized by their subject-predicate-object triple structure, present challenges due to their heterogeneity, necessitating sophisticated integration methods for effective application. Association rule mining offers a novel approach to navigating these datasets, facilitated by what we term "mining configurations." These configurations enable the exploration of RDF data in multiple dimensions, facilitating the discovery of schema and value dependencies that, when combined, lead to a range of practical applications. We demonstrate rule-based methods for predicate suggestion, data enrichment, ontology refinement, and query relaxation. By suggesting appropriate predicates, enriching datasets with omitted facts, and aligning ontologies, we address inconsistencies within the data. Additionally, we assist users in managing inconsistencies during query formulation through predicate expansion techniques. Our findings illustrate the significant benefits of association rule mining in enhancing the integration and usability of RDF data, underscoring its potential in optimizing the handling of Linked Open Data.

Analyzing interconnection structures among underlying entities or objects in a dataset through the use of graph analytics can provide tremendous value in many application domains. However, graphs are not the primary representation choice for storing most data today, and in order to have access to these analyses, users are forced to manually extract data from their data stores, construct the requisite graphs, and then load them into some graph engine in order to execute their graph analysis task. Moreover, in many cases (especially when the graphs are dense), these graphs can be significantly larger than the initial input stored in the database, making it infeasible to construct or analyze such graphs in memory. In this paper we address both of these challenges by building a system that enables users to declaratively specify graph extraction tasks over a relational database schema and then execute graph algorithms on the extracted graphs. We propose a declarative domain specific language for this purpose, and pair it up with a novel condensed, in-memory representation that significantly reduces the memory footprint of these graphs, permitting analysis of larger-than-memory graphs. We present a general algorithm for creating such a condensed representation for a large class of graph extraction queries against arbitrary schemas. We observe that the condensed representation suffers from a duplication issue, that results in inaccuracies for most graph algorithms. We then present a suite of in-memory representations that handle this duplication in different ways and allow trading off the memory required and the computational cost for executing different graph algorithms. We also introduce several novel deduplication algorithms for removing this duplication in the graph, which are of independent interest for graph compression, and provide a comprehensive experimental evaluation over several real-world and synthetic datasets illustrating these trade-offs.

RDF question/answering (Q/A) allows users to ask questions in natural languages over a knowledge base represented by RDF. To answer a national language question, the existing work takes a two-stage approach: question understanding and query evaluation. Their focus is on question understanding to deal with the disambiguation of the natural language phrases. The most common technique is the joint disambiguation, which has the exponential search space. In this paper, we propose a systematic framework to answer natural language questions over RDF repository (RDF Q/A) from a graph data-driven perspective. We propose a semantic query graph to model the query intention in the natural language question in a structural way, based on which, RDF Q/A is reduced to subgraph matching problem. More importantly, we resolve the ambiguity of natural language questions at the time when matches of query are found. The cost of disambiguation is saved if there are no matching found. We compare our method with some state-of-the-art RDF Q/A systems in the benchmark dataset. Extensive experiments confirm that our method not only improves the precision but also speeds up query performance greatly.

The need for the establishment of evaluation methods that can measure respective improvements or degradations of ontological models e.g. yielded by a precursory ontology population stage is undisputed. We propose an evaluation scheme that allows to employ a number of different ontologies and to measure their performance on specific tasks. In this paper we present the resulting task-based approach for quantitative ontology evaluation, which also allows for a bootstrapping approach to ontology population. Benchmark tasks commonly feature a so-called gold-standard defining perfect performance. By selecting ontology-based approaches for the respective tasks, the ontology-dependent part of the performance can be measured. Following this scheme, we present the results of an experiment for testing and incrementally augmenting ontologies using a well-defined benchmark problem based on a evaluation goldstandard.

Research on question answering with knowledge base has recently seen an increasing use of deep architectures. In this extended abstract, we study the application of the neural machine translation paradigm for question parsing. We employ a sequence-to-sequence model to learn graph patterns in the SPARQL graph query language and their compositions. Instead of inducing the programs through question-answer pairs, we expect a semi-supervised approach, where alignments between questions and queries are built through templates. We argue that the coverage of language utterances can be expanded using late notable works in natural language generation.

This research proposes an expanded semantic graph definition that serves as a basis for an expanded semantic graph representation and graph matching approach. This representation separates the content and context and adds a number of semantic structures that encapsulate inferred information. The expanded semantic graph approach facilitates finding additional matches, identifying and eliminating poor matches, and prioritizing matches based on how much new knowledge is provided. By focusing on information of interest, doing pre-processing, and reducing processing requirements, the approach is applicable to problems where related information of interest is sought across a massive body of free text documents. Key aspects of the approach include (1) expanding the nodes and edges through inference using DL-Safe rules, abductive hypotheses, and syntactic patterns, (2) separating semantic content into nodes and semantic context into edges, and (3) applying relatedness measures on a node, edge, and sub graph basis. Results from tests using a ground-truthed subset of a large dataset of law enforcement investigator emails illustrate the benefits of these approaches.

Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus. Email addresses: fatemeh.lashkari@unb.ca (Fatemeh Lashkari), bagheri@ryerson.ca (Ebrahim Bagheri), ghorbani@unb.ca (Ali A. Ghorbani) Preprint submitted to Information Processing and Management September 11, 2018

We present a neural model for question generation from knowledge base triples in a “ZeroShot” setup, that is generating questions for triples containing predicates, subject types or object types that were not seen at training time. Our model leverages triples occurrences in the natural language corpus in an encoderdecoder architecture, paired with an original part-of-speech copy action mechanism to generate questions. Benchmark and human evaluation show that our model sets a new state-ofthe-art for zero-shot QG.

In recent years patents have become increasingly important for businesses to protect their intellectual capital and as a valuable source of information. Patent information is, however, not employed to its full potential and the interpretation of structured and unstructured patent information in large volumes remains a challenge. We address this by proposing an integrated interdisciplinary approach that uses natural language processing and machine learning techniques to formalize multilingual patent information in an ontology. The ontology further contains patent and domain specific knowledge, which allows for aligning patents with technological fields of interest and other business-related artifacts. Our empirical evaluation shows that for categorizing patents according to well-known technological fields of interest, the approach achieves high accuracy with selected feature sets compared to related work focussing on monolingual patents. We further show that combining OWL RL reasoning with SPARQL querying over the patent knowledge base allows for answering complex business queries and illustrate this with real-world use cases from the automotive domain.

Extracting entities and relations for types of interest from text is important for understanding massive text corpora. Traditionally, systems of entity relation extraction have relied on human-annotated corpora for training and adopted an incremental pipeline. Such systems require additional human expertise to be ported to a new domain, and are vulnerable to errors cascading down the pipeline. In this paper, we investigate joint extraction of typed entities and relations with labeled data heuristically obtained from knowledge bases (i.e., distant supervision). As our algorithm for type labeling via distant supervision is context-agnostic, noisy training data poses unique challenges for the task. We propose a novel domainindependent framework, called COTYPE, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations. COTYPE, then using these learned embeddings, estimates the types of test (unlinkable) mentions. We formulate a joint optimization problem to learn embeddings from text corpora and knowledge bases, adopting a novel partial-label loss function for noisy labeled data and introducing an object "translation" function to capture the cross-constraints of entities and relations on each other. Experiments on three public datasets demonstrate the effectiveness of COTYPE across different domains (e.g., news, biomedical), with an average of 25% improvement in F1 score compared to the next best method.

Populating large-scale structured databases from unstructured sources is a critical and challenging task in data analytics. As automated feature engineering methods grow increasingly prevalent, constructing sufficiently large labeled training sets has become the primary hurdle in building machine learning information extraction systems. In light of this, we have taken a new approach called data programming [7]. Rather than hand-labeling data, in the data programming paradigm, users generate large amounts of noisy training labels by programmatically encoding domain heuristics as simple rules. Using this approach over more traditional distant supervision methods and fully supervised approaches using labeled data, we have been able to construct knowledge base systems more rapidly and with higher quality. Since the ability to quickly prototype, evaluate, and debug these rules is a key component of this paradigm, we introduce DDLite, an interactive development framework for data programming. This paper reports feedback collected from DDLite users across a diverse set of entity extraction tasks. We share observations from several DDLite hackathons in which 10 biomedical researchers prototyped information extraction pipelines for chemicals, diseases, and anatomical named entities. Initial results were promising, with the disease tagging team obtaining an F1 score within 10 points of the state-of-the-art in only a single day-long hackathon's work. Our key insights concern the challenges of writing diverse rule sets for generating labels, and exploring training data. These findings motivate several areas of active data programming research.


Question: Expanding Entity Sets through the Utilization of Knowledge Graphs

This paper introduces PathSim, a novel top-K similarity search approach tailored for heterogeneous information networks, which are comprised of multi-typed, interconnected objects. Given the rise of large-scale networks like bibliographic and social media networks, exploring similarity search within such complex structures has become paramount. Traditionally, two objects are deemed similar if they share numerous connecting paths within the network. However, existing similarity measures are primarily designed for homogeneous networks and do not account for the distinct semantic meanings that different paths can carry. This limitation renders them unsuitable for heterogeneous networks.

To address this gap, the paper proposes a similarity search method that is specifically targeted at objects of the same type within heterogeneous networks. By considering various linkage paths, one can derive a multitude of similarity semantics. To unify these paths under a common framework, the concept of meta paths is introduced. A meta path is defined as a path made up of a series of relations between different object types, existing at the meta level. Whether a user explicitly specifies a path combination with domain knowledge, selects the best path through experimentation, or uses training examples for learning, meta paths serve as the foundational element for a network-based similarity search engine.

Under the meta path framework, the paper defines PathSim, a new similarity measure designed to identify peer objects in the network. This measure is particularly adept at finding authors in similar fields with comparable reputations, offering a more contextually relevant approach compared to random-walk based measures in numerous scenarios. To facilitate rapid online query processing for PathSim queries, an efficient solution is developed that partially precomputes short meta paths and concatenates them on-demand to compute the top-K results. Experimental evaluations on real datasets substantiate the effectiveness and efficiency of the proposed methodology.

Link Prediction using Knowledge graph embedding projects symbolic entities and relations into low dimensional vector space, thereby learning the semantic relations between entities. Among various embedding models, there is a series of translation-based models such as TransE [1], TransH [2], and TransR[3]. This paper proposes modifications in the TransR model to address the issue of skewed data which is common in real-world knowledge graphs. The enhancements enable the model to smartly generate corrupted triplets during negative sampling, which significantly improves the training time and performance of TransR. The proposed approach can be applied to other translationbased models.

Extracting keyphrases from documents automatically is an important and interesting task since keyphrases provide a quick summarization for documents. Although lots of efforts have been made on keyphrase extraction, most of the existing methods (the co-occurrence-based methods and the statistic-based methods) do not take semantics into full consideration. The co-occurrence-based methods heavily depend on the co-occurrence relations between two words in the input document, which may ignore many semantic relations. The statistic-based methods exploit the external text corpus to enrich the document, which introduce more unrelated relations inevitably. In this paper, we propose a novel approach to extract keyphrases using knowledge graphs, based on which we could detect the latent relations of two keyterms (i.e., noun words and named entities) without introducing many noises. Extensive experiments over real data show that our method outperforms the state-of-the-art methods including the graph-based co-occurrence methods and statistic-based clustering methods.

List-only entity linking (EL) is the task of mapping ambiguous mentions in texts to target entities in a group of entity lists. Different from traditional EL task, which leverages rich semantic relatedness in knowledge bases to improve linking accuracy, the list-only EL can merely take advantage of co-occurrences information in entity lists. State-of-the-art work utilizes co-occurrences information to enrich entity descriptions which are further used to calculate local compatibility between mentions and entities to determine results. Nonetheless, entity coherence is also deemed to play an important part in EL, which is yet currently neglected. In this paper, in addition to local compatibility, we take into account global coherence among entities. Specifically, we propose to harness co-occurrences in entity lists for mining both explicit and implicit entity relations. The relations are then integrated into an entity graph, on which personalized PageRank is incorporated to compute entity coherence. The final results are derived by combining local mention-entity similarity and global entity coherence. The experimental studies validate the superiority of our method. Our proposal not only improves the performance of the list-only EL, but also opens up the bridge between the list-only EL and conventional EL solutions.

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.

The Semantic Web has recently seen a rise of large knowledge bases (such as DBpedia) that are freely accessible via SPARQL endpoints. The structured representation of the contained information opens up new possibilities in the way it can be accessed and queried. In this paper, we present an approach that extracts a graph covering relationships between two objects of interest. We show an interactive visualization of this graph that supports the systematic analysis of the found relationships by providing highlighting, previewing, and filtering features.

Open Information Extraction (Open IE) has gained increasing research interest in recent years. The first step in Open IE is to extract raw subject--predicate--object triples from the data. These raw triples are rarely usable per se, and need additional post-processing. To that end, we proposed the use of Boolean Tucker tensor decomposition to simultaneously find the entity and relation synonyms and the facts connecting them from the raw triples. Our method represents the synonym sets and facts using (sparse) binary matrices and tensor that can be efficiently stored and manipulated. We consider the presentation of the problem as a Boolean tensor decomposition as one of this paper's main contributions. To study the validity of this approach, we use a recent algorithm for scalable Boolean Tucker decomposition. We validate the results with empirical evaluation on a new semi-synthetic data set, generated to faithfully reproduce real-world data features, as well as with real-world data from existing Open IE extractor. We show that our method obtains high precision while the low recall can easily be remedied by considering the original data together with the decomposition.

In this work we present a novel approach for the utilization of observed relations between entity pairs in the task of triple argument prediction. The approach is based on representing observations in a shared, continuous vector space of structured relations and text. Results on a recent benchmark dataset demonstrate that the new model is superior to existing sparse feature models. In combination with state-of-the-art models, we achieve substantial improvements when observed relations are available.

Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large sets of documents into a small number of meaningful clusters. The bag of words representation used for these clustering methods is often unsatisfactory as it ignores relationships between important terms that do not cooccur literally. In order to deal with the problem, we integrate core ontologies as background knowledge into the process of clustering text documents. Our experimental evaluations compare clustering techniques based on precategorizations of texts from Reuters newsfeeds and on a smaller domain of an eLearning course about Java. In the experiments, improvements of results by background knowledge compared to a baseline without background knowledge can be shown in many interesting combinations.

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.

A key challenge in entity linking is making effective use of contextual information to disambiguate mentions that might refer to different entities in different contexts. We present a model that uses convolutional neural networks to capture semantic correspondence between a mention’s context and a proposed target entity. These convolutional networks operate at multiple granularities to exploit various kinds of topic information, and their rich parameterization gives them the capacity to learn which n-grams characterize different topics. We combine these networks with a sparse linear model to achieve state-of-the-art performance on multiple entity linking datasets, outperforming the prior systems of Durrett and Klein (2014) and Nguyen et al. (2014).1


Question: Expanding Entity Sets through the Utilization of Knowledge Graphs.

This paper examines the evaluation of question answering systems over linked data, a critical area in the realm of semantic search and natural language processing. Given the unprecedented volume of open, distributed, and structured semantic data available on the web, there has been significant progress in developing interfaces that facilitate semantic search and question answering over RDF (Resource Description Framework) datasets. Notably, natural language interfaces offer a user-friendly approach to accessing the rich semantics of the web without exposing them to the complexities of query languages and data models. Despite the growing interest in this field, a comprehensive evaluation framework for such systems has been lacking, contrasting starkly with the extensive evaluations conducted for traditional question answering and document search interfaces.

To bridge this gap, a series of evaluation challenges have been organized to assess the performance of question answering systems as interfaces for querying linked data sources. The primary objective was to understand the systems' strengths, capabilities, and limitations, as well as to gauge their effectiveness in handling the vast and heterogeneous nature of RDF data on the web, characterized by diverse vocabularies and schemas. The paper presents the outcomes from the first two evaluation campaigns, highlighting the insights gained and the issues addressed in the second evaluation, which were identified in the initial round. It also outlines the ongoing challenges that future competitions must tackle to advance the field of question answering over linked data.

In essence, the evaluation challenges serve as a benchmark for question answering systems, providing a critical assessment of their ability to navigate and extract information from the complex landscape of linked data. By systematically evaluating these systems, researchers and developers can identify areas for improvement, refine existing approaches, and foster the development of more sophisticated and user-friendly interfaces for accessing semantic web data.

A wealth of knowledge is encoded in the form of tables on the World Wide Web. We propose a classification algorithm and a rich feature set for automatically recognizing layout tables and attribute/value tables. We report the frequencies of these table types over a large analysis of the Web and propose open challenges for extracting from attribute/value tables semantic triples (knowledge). We then describe a solution to a key problem in extracting semantic triples: protagonist detection, i.e., finding the subject of the table that often is not present in the table itself. In 79% of our Web tables, our method finds the correct protagonist in its top three returned candidates.

Freebase is a practical, scalable, graph-shaped database of structured general human knowledge, inspired by Semantic Web research and collaborative data communities such as the Wikipedia. Freebase allows public read and write access through an HTTP-based graph-query API for research, the creation and maintenance of structured data, and application building. Access is free and all data in Freebase has a very open (e.g. Creative Commons, GFDL) license.

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.

Representation learning (RL) of knowledge graphs aims to project both entities and relations into a continuous lowdimensional space. Most methods concentrate on learning representations with knowledge triples indicating relations between entities. In fact, in most knowledge graphs there are usually concise descriptions for entities, which cannot be well utilized by existing methods. In this paper, we propose a novel RL method for knowledge graphs taking advantages of entity descriptions. More specifically, we explore two encoders, including continuous bag-of-words and deep convolutional neural models to encode semantics of entity descriptions. We further learn knowledge representations with both triples and descriptions. We evaluate our method on two tasks, including knowledge graph completion and entity classification. Experimental results on real-world datasets show that, our method outperforms other baselines on the two tasks, especially under the zero-shot setting, which indicates that our method is capable of building representations for novel entities according to their descriptions. The source code of this paper can be obtained from https://github.com/xrb92/DKRL.

When humans approach the task of text categorization, they interpret the specific wording of the document in the much larger context of their background knowledge and experience. On the other hand, state-of-the-art information retrieval systems are quite brittle—they traditionally represent documents as bags of words, and are restricted to learning from individual word occurrences in the (necessarily limited) training set. For instance, given the sentence “Wal-Mart supply chain goes real time”, how can a text categorization system know that Wal-Mart manages its stock with RFID technology? And having read that “Ciprofloxacin belongs to the quinolones group”, how on earth can a machine know that the drug mentioned is an antibiotic produced by Bayer? In this paper we present algorithms that can do just that. We propose to enrich document representation through automatic use of a vast compendium of human knowledge—an encyclopedia. We apply machine learning techniques to Wikipedia, the largest encyclopedia to date, which surpasses in scope many conventional encyclopedias and provides a cornucopia of world knowledge. Each Wikipedia article represents a concept, and documents to be categorized are represented in the rich feature space of words and relevant Wikipedia concepts. Empirical results confirm that this knowledge-intensive representation brings text categorization to a qualitatively new level of performance across a diverse collection of datasets.

Distant supervision has attracted recent interest for training information extraction systems because it does not require any human annotation but rather employs existing knowledge bases to heuristically label a training corpus. However, previous work has failed to address the problem of false negative training examples mislabeled due to the incompleteness of knowledge bases. To tackle this problem, we propose a simple yet novel framework that combines a passage retrieval model using coarse features into a state-of-the-art relation extractor using multi-instance learning with fine features. We adapt the information retrieval technique of pseudorelevance feedback to expand knowledge bases, assuming entity pairs in top-ranked passages are more likely to express a relation. Our proposed technique significantly improves the quality of distantly supervised relation extraction, boosting recall from 47.7% to 61.2% with a consistently high level of precision of around 93% in the experiments.

Most knowledge sources on the Data Web were extracted from structured or semi-structured data. Thus, they encompass solely a small fraction of the information available on the document-oriented Web. In this paper, we present BOA, an iterative bootstrapping strategy for extracting RDF from unstructured data. The idea behind BOA is to use the Data Web as background knowledge for the extraction of natural language patterns that represent predicates found on the Data Web. These patterns are used to extract instance knowledge from natural language text. This knowledge is nally fed back into the Data Web, therewith closing the loop. We evaluate our approach on two data sets using DBpedia as background knowledge. Our results show that we can extract several thousand new facts in one iteration with very high accuracy. Moreover, we provide the rst repository of natural language representations of predicates found on the Data Web.

An explosion in the use of RDF for representing information about resources has driven the requirements for Webscale server systems that can store and process huge quantities of data, and furthermore provide powerful data access and mining functionalities. This paper describes OWLIM, a family of semantic repositories that provide storage, inference and novel data-access features delivered in a scalable, resilient, industrial-strength platform. Ontotext AD, 135 Tsarigradsko Chaussee, Sofia 1784, Bulgaria

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.

In recent years we have seen the emergence of a variety of scholarly datasets. Typically these capture ‘standard’ scholarly entities and their connections, such as authors, affiliations, venues, publications, citations, and others. However, as the repositories grow and the technology improves, researchers are adding new entities to these repositories to develop a richer model of the scholarly domain. In this paper, we introduce TechMiner, a new approach, which combines NLP, machine learning and semantic technologies, for mining technologies from research publications and generating an OWL ontology describing their relationships with other research entities. The resulting knowledge base can support a number of tasks, such as: richer semantic search, which can exploit the technology dimension to support better retrieval of publications; richer expert search; monitoring the emergence and impact of new technologies, both within and across scientific fields; studying the scholarly dynamics associated with the emergence of new technologies; and others. TechMiner was evaluated on a manually annotated gold standard and the results indicate that it significantly outperforms alternative NLP approaches and that its semantic features improve performance significantly with respect to both recall and precision.


Question: Expanding entity sets through the utilization of knowledge graphs.

Introducing DBpediaRec: A Music Recommendation Engine Leveraging Knowledge Graphs
This paper delves into the conceptual framework and practical deployment of DBpediaRec, a music recommendation engine that harnesses the extensive DBpedia knowledge graph. The system is designed to offer personalized music suggestions for over 39,000 musical acts, including bands and solo artists. We explore the intricate challenges encountered and the valuable lessons gleaned during the development phase, offering critical insights for developers crafting applications that utilize Linked Data. Additionally, we conduct a user-focused evaluation of DBpediaRec, contrasting its performance with that of last.fm, a leading music streaming and recommendation platform. Through this comparative analysis, we highlight the unique advantages and capabilities of DBpediaRec in the realm of music recommendation systems.

One of the main obstacles to highperformance Word Sense Disambiguation (WSD) is the knowledge acquisition bottleneck. In this paper, we present a methodology to automatically extend WordNet with large amounts of semantic relations from an encyclopedic resource, namely Wikipedia. We show that, when provided with a vast amount of high-quality semantic relations, simple knowledge-lean disambiguation algorithms compete with state-of-the-art supervised WSD systems in a coarse-grained all-words setting and outperform them on gold-standard domain-specific datasets.

Linking entities with knowledge base (entity linking) is a key issue in bridging the textual data with the structural knowledge base. Due to the name variation problem and the name ambiguity problem, the entity linking decisions are critically depending on the heterogenous knowledge of entities. In this paper, we propose a generative probabilistic model, called entitymention model, which can leverage heterogenous entity knowledge (including popularity knowledge, name knowledge and context knowledge) for the entity linking task. In our model, each name mention to be linked is modeled as a sample generated through a three-step generative story, and the entity knowledge is encoded in the distribution of entities in document P(e), the distribution of possible names of a specific entity P(s|e), and the distribution of possible contexts of a specific entity P(c|e). To find the referent entity of a name mention, our method combines the evidences from all the three distributions P(e), P(s|e) and P(c|e). Experimental results show that our method can significantly outperform the traditional methods.

An explosion in the use of RDF for representing information about resources has driven the requirements for Webscale server systems that can store and process huge quantities of data, and furthermore provide powerful data access and mining functionalities. This paper describes OWLIM, a family of semantic repositories that provide storage, inference and novel data-access features delivered in a scalable, resilient, industrial-strength platform. Ontotext AD, 135 Tsarigradsko Chaussee, Sofia 1784, Bulgaria

Knowledge Graph (KG) embedding projects entities and relations into low dimensional vector space, which has been successfully applied in KG completion task. The previous embedding approaches only model entities and their relations, ignoring a large number of entities’ numeric attributes in KGs. In this paper, we propose a new KG embedding model which jointly model entity relations and numeric attributes. Our approach combines an attribute embedding model with a translation-based structure embedding model, which learns the embeddings of entities, relations, and attributes simultaneously. Experiments of link prediction on YAGO and Freebase show that the performance is effectively improved by adding entities’ numeric attributes in the embedding model.

HTML tables on web pages (“web tables”) cover a wide variety of topics. Data from web tables can thus be useful for tasks such as knowledge base completion or ad hoc table extension. Before table data can be used for these tasks, the tables must be matched to the respective knowledge base or base table. The challenges of web table matching are the high heterogeneity and the small size of the tables. Though it is known that the majority of web tables are very small, the gold standards that are used to compare web table matching systems mostly consist of larger tables. In this experimental paper, we evaluate T2K Match, a web table to knowledge base matching system, and COMA, a standard schema matching tool, using a sample of web tables that is more realistic than the gold standards that were previously used. We find that both systems fail to produce correct results for many of the very small tables in the sample. As a remedy, we propose to stitch (combine) the tables from each web site into larger ones and match these enlarged tables to the knowledge base or base table afterwards. For this stitching process, we evaluate different schema matching methods in combination with holistic correspondence refinement. Limiting the stitching procedure to web tables from the same web site decreases the heterogeneity and allows us to stitch tables with very high precision. Our experiments show that applying table stitching before running the actual matching method improves the matching results by 0.38 in F1-measure for T2K Match and by 0.14 for COMA. Also, stitching the tables allows us to reduce the amount of tables in our corpus from 5 million original web tables to as few as 100,000 stitched tables.

Entity recognition is an important but challenging research problem. In reality, many text collections are from specific, dynamic, or emerging domains, which poses significant new challenges for entity recognition with increase in name ambiguity and context sparsity, requiring entity detection without domain restriction. In this paper, we investigate entity recognition (ER) with distant-supervision and propose a novel relation phrase-based ER framework, called ClusType, that runs data-driven phrase mining to generate entity mention candidates and relation phrases, and enforces the principle that relation phrases should be softly clustered when propagating type information between their argument entities. Then we predict the type of each entity mention based on the type signatures of its co-occurring relation phrases and the type indicators of its surface name, as computed over the corpus. Specifically, we formulate a joint optimization problem for two tasks, type propagation with relation phrases and multi-view relation phrase clustering. Our experiments on multiple genres---news, Yelp reviews and tweets---demonstrate the effectiveness and robustness of ClusType, with an average of 37% improvement in F1 score over the best compared method.

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

The combined efforts of human volunteers have recently extracted numerous facts from Wikipedia, storing them as machine-harvestable object-attribute-value triples in Wikipedia infoboxes. Machine learning systems, such as Kylin, use these infoboxes as training data, accurately extracting even more semantic knowledge from natural language text. But in order to realize the full power of this information, it must be situated in a cleanly-structured ontology. This paper introduces KOG, an autonomous system for refining Wikipedia's infobox-class ontology towards this end. We cast the problem of ontology refinement as a machine learning problem and solve it using both SVMs and a more powerful joint-inference approach expressed in Markov Logic Networks. We present experiments demonstrating the superiority of the joint-inference approach and evaluating other aspects of our system. Using these techniques, we build a rich ontology, integrating Wikipedia's infobox-class schemata with WordNet. We demonstrate how the resulting ontology may be used to enhance Wikipedia with improved query processing and other features.

Semantic Space is a pervasive computing infrastructure that exploits semantic Web technologies to support explicit representation, expressive querying, and flexible reasoning of contexts in smart spaces.

List-only entity linking (EL) is the task of mapping ambiguous mentions in texts to target entities in a group of entity lists. Different from traditional EL task, which leverages rich semantic relatedness in knowledge bases to improve linking accuracy, the list-only EL can merely take advantage of co-occurrences information in entity lists. State-of-the-art work utilizes co-occurrences information to enrich entity descriptions which are further used to calculate local compatibility between mentions and entities to determine results. Nonetheless, entity coherence is also deemed to play an important part in EL, which is yet currently neglected. In this paper, in addition to local compatibility, we take into account global coherence among entities. Specifically, we propose to harness co-occurrences in entity lists for mining both explicit and implicit entity relations. The relations are then integrated into an entity graph, on which personalized PageRank is incorporated to compute entity coherence. The final results are derived by combining local mention-entity similarity and global entity coherence. The experimental studies validate the superiority of our method. Our proposal not only improves the performance of the list-only EL, but also opens up the bridge between the list-only EL and conventional EL solutions.


Question: Expanding Entity Sets through the Utilization of Knowledge Graphs

This paper presents a comprehensive probabilistic framework for entity search that leverages not only query terms but also additional user-provided inputs, including category information and example entities. In the context of entity search, users often seek specific entities rather than documents, and are thus inclined to offer supplementary information to refine their queries. Our framework is designed to explore and illuminate various methods of incorporating these inputs into query modeling. Specifically, we highlight the benefits of utilizing category information, illustrating how a category-based representation outperforms a term-based representation. Moreover, we showcase the efficacy of category-based expansion techniques when example entities are employed. Our top-performing model achieves remarkable results, demonstrating significant competitive advantage in the INEX-XER entity ranking and list completion tasks, thereby underscoring the power of our approach in enhancing entity search effectiveness.

Entity Linking (EL) and Word Sense Disambiguation (WSD) both address the lexical ambiguity of language. But while the two tasks are pretty similar, they differ in a fundamental respect: in EL the textual mention can be linked to a named entity which may or may not contain the exact mention, while in WSD there is a perfect match between the word form (better, its lemma) and a suitable word sense. In this paper we present Babelfy, a unified graph-based approach to EL and WSD based on a loose identification of candidate meanings coupled with a densest subgraph heuristic which selects high-coherence semantic interpretations. Our experiments show state-of-the-art performances on both tasks on 6 different datasets, including a multilingual setting. Babelfy is online at http://babelfy.org

Machine learning has become pervasive in multiple domains, impacting a wide variety of applications, such as knowledge discovery and data mining, natural language processing, information retrieval, computer vision, social and health informatics, ubiquitous computing, etc. Two essential problems of machine learning are how to generate features and how to acquire labels for machines to learn. Particularly, labeling large amount of data for each domain-specific problem can be very time consuming and costly. It has become a key obstacle in making learning protocols realistic in applications. In this paper, we will discuss how to use the existing general-purpose world knowledge to enhance machine learning processes, by enriching the features or reducing the labeling work. We start from the comparison of world knowledge with domain-specific knowledge, and then introduce three key problems in using world knowledge in learning processes, i.e., explicit and implicit feature representation, inference for knowledge linking and disambiguation, and learning with direct or indirect supervision. Finally we discuss the future directions of this research topic.

Graph Database Management Systems (GDBMs) provide an effective and efficient solution to data storage in current scenarios where data are more and more connected, graph models are widely used, and systems need to scale to large data sets. In particular, the conversion of the persistent layer of an application from a RDF to a graph data store can be convenient but it is usually an hard task for database administrators. In this paper we propose a methodology to convert a RDF data store to a graph database by exploiting the ontology and the constraints of the source. We provide experimental results that show the feasibility of our solution and the efficiency of query answering over the target database.

Knowledge graphs are typical large-scale multi-relational structures, which comprise a large amount of fact triplets. Nonetheless, existing knowledge graphs are still sparse and far from being complete. To refine the knowledge graphs, representation learning is widely used to embed fact triplets into low-dimensional spaces. Many existing knowledge graph embedding models either focus on learning rich features from entities but fail to extract good features of relations, or employ sophisticated models that have rather high time and memory-space complexities. In this paper, we propose a novel knowledge graph embedding model, CombinE. It exploits entity features from two complementary perspectives via the plus and minus combinations. We start with the plus combination, where we use shared features of entity pairs participating in a relation to convey its relation features. To also allow differences of each pairs of entities participating in a relation, we also use the minus combination, where we concentrate on individual entity features, and regard relations as a channel to offset the divergence and preserve the prominence between head and tail entities. Compared with the state-of-the-art models, our experimental results demonstrate that CombinE outperforms existing ones and has low time and memory-space complexities.

Traditional relation extraction predicts relations within some fixed and finite target schema. Machine learning approaches to this task require either manual annotation or, in the case of distant supervision, existing structured sources of the same schema. The need for existing datasets can be avoided by using a universal schema: the union of all involved schemas (surface form predicates as in OpenIE, and relations in the schemas of preexisting databases). This schema has an almost unlimited set of relations (due to surface forms), and supports integration with existing structured data (through the relation types of existing databases). To populate a database of such schema we present matrix factorization models that learn latent feature vectors for entity tuples and relations. We show that such latent models achieve substantially higher accuracy than a traditional classification approach. More importantly, by operating simultaneously on relations observed in text and in pre-existing structured DBs such as Freebase, we are able to reason about unstructured and structured data in mutually-supporting ways. By doing so our approach outperforms stateof-the-art distant supervision.

The availability of large amounts of open, distributed and structured semantic data on the web has no precedent in the history of computer science. In recent years, there have been important advances in semantic search and question answering over RDF data. In particular, natural language interfaces to online semantic data have the advantage that they can exploit the expressive power of Semantic Web data models and query languages, while at the same time hiding their complexity from the user. However, despite the increasing interest in this area, there are no evaluations so far that systematically evaluate this kind of systems, in contrast to traditional question answering and search interfaces to document spaces. To address this gap, we have set up a series of evaluation challenges for question answering over linked data. The main goal of the challenge was to get insight into the strengths, capabilities and current shortcomings of question answering systems as interfaces to query linked data sources, as well as benchmarking how these interaction paradigms can deal with the fact that the amount of RDF data available on the web is very large and heterogeneous with respect to the vocabularies and schemas used. Here we report on the results from the first and second of such evaluation campaigns. We also discuss how the second evaluation addressed some of the issues and limitations which arose from the first one, as well as the open issues to be addressed in future competitions.

Several ontologies have been created in the last years for the semantic annotation of scholarly publications and scientific documents. This rich variety of ontologies makes it difficult for those willing to annotate their documents to know which ones they should select for such activity. This paper presents a classification and description of these state-of-the-art ontologies, together with the rationale behind the different approaches. Finally, we provide an example of how some of these ontologies can be used for the annotation of a scientific document.

Recently, knowledge graph embedding, which projects symbolic entities and relations into continuous vector space, has become a new, hot topic in artificial intelligence. This paper proposes a novel generative model (TransG) to address the issue of multiple relation semantics that a relation may have multiple meanings revealed by the entity pairs associated with the corresponding triples. The new model can discover latent semantics for a relation and leverage a mixture of relationspecific component vectors to embed a fact triple. To the best of our knowledge, this is the first generative model for knowledge graph embedding, and at the first time, the issue of multiple relation semantics is formally discussed. Extensive experiments show that the proposed model achieves substantial improvements against the state-of-the-art baselines.

In this paper we propose the use of WordNet as a knowledge base in an information retrieval task. The application areas range from information filtering and document retrieval to multimedia retrieval and data sharing in large scale distributed database systems. The WordNet derived knowledge base makes semantic knowledge available which can be used in overcoming many problems associated with the richness of natural language. A semantic similarity measure is also proposed which can be used as an alternative to pattern matching in the comparison process.

In this paper we give an overview of the Knowledge Base Population (KBP) track at the 2010 Text Analysis Conference. The main goal of KBP is to promote research in discovering facts about entities and augmenting a knowledge base (KB) with these facts. This is done through two tasks, Entity Linking – linking names in context to entities in the KB – and Slot Filling – adding information about an entity to the KB. A large source collection of newswire and web documents is provided from which systems are to discover information. Attributes (“slots”) derived from Wikipedia infoboxes are used to create the reference KB. In this paper we provide an overview of the techniques which can serve as a basis for a good KBP system, lay out the remaining challenges by comparison with traditional Information Extraction (IE) and Question Answering (QA) tasks, and provide some suggestions to address these challenges.