Search (30 results, page 1 of 2)

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Abstract

Indexing consists of both novel and more traditional techniques. Cutting-edge indexing techniques, such as automatic indexing, ontologies, and topic maps, were developed independently of older techniques such as thesauri, but it is now recognized that these older methods also hold expertise. Indexing describes various traditional and novel indexing techniques, giving information professionals and students of library and information sciences a broad and comprehensible introduction to indexing. This title consists of twelve chapters: an Introduction to subject readings and theasauri; Automatic indexing versus manual indexing; Techniques applied in automatic indexing of text material; Automatic indexing of images; The black art of indexing moving images; Automatic indexing of music; Taxonomies and ontologies; Metadata formats and indexing; Tagging; Topic maps; Indexing the web; and The Semantic Web.

Date

24. 8.2016 14:03:22

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Abstract

Metadata and semantics are integral to any information system and significant to the sphere of Web data. Research focusing on metadata and semantics is crucial for advancing our understanding and knowledge of metadata; and, more profoundly for being able to effectively discover, use, archive, and repurpose information. In response to this need, researchers are actively examining methods for generating, reusing, and interchanging metadata. Integrated with these developments is research on the application of computational methods, linked data, and data analytics. A growing body of work also targets conceptual and theoretical designs providing foundational frameworks for metadata and semantic applications. There is no doubt that metadata weaves its way into nearly every aspect of our information ecosystem, and there is great motivation for advancing the current state of metadata and semantics. To this end, it is vital that scholars and practitioners convene and share their work.
The MTSR 2013 program and the contents of these proceedings show a rich diversity of research and practices, drawing on problems from metadata and semantically focused tools and technologies, linked data, cross-language semantics, ontologies, metadata models, and semantic system and metadata standards. The general session of the conference included 18 papers covering a broad spectrum of topics, proving the interdisciplinary field of metadata, and was divided into three main themes: platforms for research data sets, system architecture and data management; metadata and ontology validation, evaluation, mapping and interoperability; and content management. Metadata as a research topic is maturing, and the conference also supported the following five tracks: Metadata and Semantics for Open Repositories, Research Information Systems and Data Infrastructures; Metadata and Semantics for Cultural Collections and Applications; Metadata and Semantics for Agriculture, Food and Environment; Big Data and Digital Libraries in Health, Science and Technology; and European and National Projects, and Project Networking. Each track had a rich selection of papers, giving broader diversity to MTSR, and enabling deeper exploration of significant topics.
All the papers underwent a thorough and rigorous peer-review process. The review and selection this year was highly competitive and only papers containing significant research results, innovative methods, or novel and best practices were accepted for publication. Only 29 of 89 submissions were accepted as full papers, representing 32.5% of the total number of submissions. Additional contributions covering noteworthy and important results in special tracks or project reports were accepted, totaling 42 accepted contributions. This year's conference included two outstanding keynote speakers. Dr. Stefan Gradmann, a professor arts department of KU Leuven (Belgium) and director of university library, addressed semantic research drawing from his work with Europeana. The title of his presentation was, "Towards a Semantic Research Library: Digital Humanities Research, Europeana and the Linked Data Paradigm". Dr. Michail Salampasis, associate professor from our conference host institution, the Department of Informatics of the Alexander TEI of Thessaloniki, presented new potential, intersecting search and linked data. The title of his talk was, "Rethinking the Search Experience: What Could Professional Search Systems Do Better?"

Date

17.12.2013 12:51:22

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Abstract

Peter and Alice are at home, it is a calm winter night, snow is falling, and it is too cold to go outside. "Why don't we just order a pizza and watch a movie?" says Alice wrapped in her favorite blanket. "Why not?"-Peter replies-"Which movie do you wanna watch?" "Well, what about some comedy, romance-like one? Com'on Pete, look on Facebook, there is that nice application Kara suggested me some days ago!" answers Alice. "Oh yes, MORE, here we go, tell me a movie you like a lot," says Peter excited. "Uhm, I wanna see something like the Bridget Jones's Diary or Four Weddings and a Funeral, humour, romance, good actors..." replies his beloved, rubbing her hands. Peter is a bit concerned, he is more into fantasy genre, but he wants to please Alice, so he looks on MORE for movies similar to the Bridget Jones's Diary and Four Weddings and a Funeral: "Here we are my dear, MORE suggests the sequel or, if you prefer, Love Actually," I would prefer the second." "Great! Let's rent it!" nods Peter in agreement. The scenario just presented highlights an interesting and useful feature of a modern Web application. There are tasks where the users look for items similar to the ones they already know. Hence, we need systems that recommend items based on user preferences. In other words, systems should allow an easy and friendly exploration of the information/data related to a particular domain of interest. Such characteristics are well known in the literature and in common applications such as recommender systems. Nevertheless, new challenges in this field arise whenthe information used by these systems exploits the huge amount of interlinked data coming from the Semantic Web. In this chapter, we present MORE, a system for 'movie recommendation' in the Web of Data.

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Abstract

The World Wide Web has enabled the creation of a global information space comprising linked documents. As the Web becomes ever more enmeshed with our daily lives, there is a growing desire for direct access to raw data not currently available on the Web or bound up in hypertext documents. Linked Data provides a publishing paradigm in which not only documents, but also data, can be a first class citizen of the Web, thereby enabling the extension of the Web with a global data space based on open standards - the Web of Data. In this Synthesis lecture we provide readers with a detailed technical introduction to Linked Data. We begin by outlining the basic principles of Linked Data, including coverage of relevant aspects of Web architecture. The remainder of the text is based around two main themes - the publication and consumption of Linked Data. Drawing on a practical Linked Data scenario, we provide guidance and best practices on: architectural approaches to publishing Linked Data; choosing URIs and vocabularies to identify and describe resources; deciding what data to return in a description of a resource on the Web; methods and frameworks for automated linking of data sets; and testing and debugging approaches for Linked Data deployments. We give an overview of existing Linked Data applications and then examine the architectures that are used to consume Linked Data from the Web, alongside existing tools and frameworks that enable these. Readers can expect to gain a rich technical understanding of Linked Data fundamentals, as the basis for application development, research or further study.

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Abstract

We are witnessing a smooth evolution of the Web from a worldwide information space of linked documents to a global knowledge base, where resources are identified by means of uniform resource identifiers (URIs, essentially string identifiers) and are semantically described and correlated through resource description framework (RDF, a metadata data model) statements. With the size and availability of data constantly increasing (currently around 7 billion RDF triples and 150 million RDF links), a fundamental problem lies in the difficulty users face to find and retrieve the information they are interested in. In general, to access semantic data, users need to know the organization of data and the syntax of a specific query language (e.g., SPARQL or variants thereof). Clearly, this represents an obstacle to information access for nonexpert users. For this reason, keyword search-based systems are increasingly capturing the attention of researchers. Recently, many approaches to keyword-based search over structured and semistructured data have been proposed]. These approaches usually implement IR strategies on top of traditional database management systems with the goal of freeing the users from having to know data organization and query languages.

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Abstract

With the advance of the semantic Web, increasing amounts of data are available in a structured and machine-understandable form. This opens opportunities for users to employ semantic queries instead of simple keyword-based ones to accurately express the information need. However, constructing semantic queries is a demanding task for human users [11]. To compose a valid semantic query, a user has to (1) master a query language (e.g., SPARQL) and (2) acquire sufficient knowledge about the ontology or the schema of the data source. While there are systems which support this task with visual tools [21, 26] or natural language interfaces [3, 13, 14, 18], the process of query construction can still be complex and time consuming. According to [24], users prefer keyword search, and struggle with the construction of semantic queries although being supported with a natural language interface. Several keyword search approaches have already been proposed to ease information seeking on semantic data [16, 32, 35] or databases [1, 31]. However, keyword queries lack the expressivity to precisely describe the user's intent. As a result, ranking can at best put query intentions of the majority on top, making it impossible to take the intentions of all users into consideration.

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Abstract

For a long while, the creation of Web content required at least basic knowledge of Web technologies, meaning that for many Web users, the Web was de facto a read-only medium. This changed with the arrival of the "social Web," when Web applications started to allow users to publish Web content without technological expertise. Here, content creation is often an inclusive, iterative, and interactive process. Examples of social Web applications include blogs, social networking sites, as well as many specialized applications, for example, for saving and sharing bookmarks and publishing photos. Social semantic Web applications are social Web applications in which knowledge is expressed not only in the form of text and multimedia but also through informal to formal annotations that describe, reflect, and enhance the content. These annotations often take the shape of RDF graphs backed by ontologies, but less formal annotations such as free-form tags or tags from a controlled vocabulary may also be available. Wikis are one example of social Web applications for collecting and sharing knowledge. They allow users to easily create and edit documents, so-called wiki pages, using a Web browser. The pages in a wiki are often heavily interlinked, which makes it easy to find related information and browse the content.

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Abstract

The modern Web has grown from a publishing place of well-structured data and HTML pages for companies and experienced users into a vivid publishing and data exchange community in which everyone can participate, both as a data consumer and as a data producer. Unavoidably, the data available on the Web became highly heterogeneous, ranging from highly structured and semistructured to highly unstructured user-generated content, reflecting different perspectives and structuring principles. The full potential of such data can only be realized by combining information from multiple sources. For instance, the knowledge that is typically embedded in monolithic applications can be outsourced and, thus, used also in other applications. Numerous systems nowadays are already actively utilizing existing content from various sources such as WordNet or Wikipedia. Some well-known examples of such systems include DBpedia, Freebase, Spock, and DBLife. A major challenge during combining and querying information from multiple heterogeneous sources is entity linkage, i.e., the ability to detect whether two pieces of information correspond to the same real-world object. This chapter introduces a novel approach for addressing the entity linkage problem for heterogeneous, uncertain, and volatile data.

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Abstract

The Entity-Relationship (ER) model is a fundamental formalism for conceptual modeling in database design; it was introduced by Chen in his milestone paper, and it is now widely used, being flexible and easily understood by practitioners. With the rise of the Semantic Web, conceptual modeling formalisms have gained importance again as ontology formalisms, in the Semantic Web parlance. Ontologies and conceptual models are aimed at representing, rather than the structure of data, the domain of interest, that is, the fragment of the real world that is being represented by the data and the schema. A prominent formalism for modeling ontologies are Description Logics (DLs), which are decidable fragments of first-order logic, particularly suitable for ontological modeling and querying. In particular, DL ontologies are sets of assertions describing sets of objects and (usually binary) relations among such sets, exactly in the same fashion as the ER model. Recently, research on DLs has been focusing on the problem of answering queries under ontologies, that is, given a query q, an instance B, and an ontology X, answering q under B and amounts to compute the answers that are logically entailed from B by using the assertions of X. In this context, where data size is usually large, a central issue the data complexity of query answering, i.e., the computational complexity with respect to the data set B only, while the ontology X and the query q are fixed.

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Abstract

The book covers multimedia ontology in heritage preservation with intellectual explorations of various themes of Indian cultural heritage. The result of more than 15 years of collective research, Multimedia Ontology: Representation and Applications provides a theoretical foundation for understanding the nature of media data and the principles involved in its interpretation. The book presents a unified approach to recent advances in multimedia and explains how a multimedia ontology can fill the semantic gap between concepts and the media world. It relays real-life examples of implementations in different domains to illustrate how this gap can be filled. The book contains information that helps with building semantic, content-based search and retrieval engines and also with developing vertical application-specific search applications. It guides you in designing multimedia tools that aid in logical and conceptual organization of large amounts of multimedia data. As a practical demonstration, it showcases multimedia applications in cultural heritage preservation efforts and the creation of virtual museums. The book describes the limitations of existing ontology techniques in semantic multimedia data processing, as well as some open problems in the representations and applications of multimedia ontology. As an antidote, it introduces new ontology representation and reasoning schemes that overcome these limitations. The long, compiled efforts reflected in Multimedia Ontology: Representation and Applications are a signpost for new achievements and developments in efficiency and accessibility in the field.

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Abstract

This text introduces the standardized knowledge representation languages for modeling ontologies operating at the core of the semantic web. It covers RDF schema, Web Ontology Language (OWL), rules, query languages, the OWL 2 revision, and the forthcoming Rule Interchange Format (RIF). A 2010 CHOICE Outstanding Academic Title ! The nine chapters of the book guide the reader through the major foundational languages for the semantic Web and highlight the formal semantics. ! the book has very interesting supporting material and exercises, is oriented to W3C standards, and provides the necessary foundations for the semantic Web. It will be easy to follow by the computer scientist who already has a basic background on semantic Web issues; it will also be helpful for both self-study and teaching purposes. I recommend this book primarily as a complementary textbook for a graduate or undergraduate course in a computer science or a Web science academic program. --Computing Reviews, February 2010 This book is unique in several respects. It contains an in-depth treatment of all the major foundational languages for the Semantic Web and provides a full treatment of the underlying formal semantics, which is central to the Semantic Web effort. It is also the very first textbook that addresses the forthcoming W3C recommended standards OWL 2 and RIF. Furthermore, the covered topics and underlying concepts are easily accessible for the reader due to a clear separation of syntax and semantics ! I am confident this book will be well received and play an important role in training a larger number of students who will seek to become proficient in this growing discipline.

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Abstract

Web search engines such as Google, Yahoo! MSN/Bing, and Ask are far from the consummate Web search solution: they do not typically produce direct answers to queries but instead typically recommend a selection of related documents from the Web. We note that in more recent years, search engines have begun to provide direct answers to prose queries matching certain common templates-for example, "population of china" or "12 euro in dollars"-but again, such functionality is limited to a small subset of popular user queries. Furthermore, search engines now provide individual and focused search interfaces over images, videos, locations, news articles, books, research papers, blogs, and real-time social media-although these tools are inarguably powerful, they are limited to their respective domains. In the general case, search engines are not suitable for complex information gathering tasks requiring aggregation from multiple indexed documents: for such tasks, users must manually aggregate tidbits of pertinent information from various pages. In effect, such limitations are predicated on the lack of machine-interpretable structure in HTML-documents, which is often limited to generic markup tags mainly concerned with document renderign and linking. Most of the real content is contained in prose text which is inherently difficult for machines to interpret.

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Abstract

The Web is a surprisingly extensive source of information: it offers a huge number of sites containing data about a disparate range of topics. Although Web pages are built for human fruition, not for automatic processing of the data, we observe that an increasing number of Web sites deliver pages containing structured information about recognizable concepts, relevant to specific application domains, such as movies, finance, sport, products, etc. The development of scalable techniques to discover, extract, and integrate data from fairly structured large corpora available on the Web is a challenging issue, because to face the Web scale, these activities should be accomplished automatically by domain-independent techniques. To cope with the complexity and the heterogeneity of Web data, state-of-the-art approaches focus on information organized according to specific patterns that frequently occur on the Web. Meaningful examples are WebTables, which focuses on data published in HTML tables, and information extraction systems, such as TextRunner, which exploits lexical-syntactic patterns. As noticed by Cafarella et al., even if a small fraction of the Web is organized according to these patterns, due to the Web scale, the amount of data involved is impressive. In this chapter, we focus on methods and techniques to wring out value from the data delivered by large data-intensive Web sites.

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Abstract

The resource description framework (RDF for short) provides a flexible method for modeling information on the Web [34,40]. All data items in RDF are uniformly represented as triples of the form (subject, predicate, object), sometimes also referred to as (subject, property, value) triples. As a running example for this chapter, a small fragment of an RDF dataset concerning music and music fans is given in Fig. 2.1. Spurred by efforts like the Linking Open Data project, increasingly large volumes of data are being published in RDF. Notable contributors in this respect include areas as diverse as the government, the life sciences, Web 2.0 communities, and so on. To give an idea of the volumes of RDF data concerned, as of September 2012, there are 31,634,213,770 triples in total published by data sources participating in the Linking Open Data project. Many individual data sources (like, e.g., PubMed, DBpedia, MusicBrainz) contain hundreds of millions of triples (797, 672, and 179 millions, respectively). These large volumes of RDF data motivate the need for scalable native RDF data management solutions capabable of efficiently storing, indexing, and querying RDF data. In this chapter, we present a general and up-to-date survey of the current state of the art in RDF storage and indexing.

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Abstract

The Web of Linked Data can be seen as a global database, where resources are identified through URIs, are self-described (by means of the URI dereferencing mechanism), and are globally connected through RDF links. According to the Linked Data perspective, research attention is progressively shifting from data organization and representation to linkage and composition of the huge amount of data available on the Web. For example, at the time of this writing, the DBpedia knowledge base describes more than 3.5 million things, conceptualized through 672 million RDF triples, with 6.5 million external links into other RDF datasets. Useful applications have been provided for enabling people to browse this wealth of data, like Tabulator. Other systems have been implemented to collect, index, and provide advanced searching facilities over the Web of Linked Data, such as Watson and Sindice. Besides these applications, domain-specific systems to gather and mash up Linked Data have been proposed, like DBpedia Mobile and Revyu . corn. DBpedia Mobile is a location-aware client for the semantic Web that can be used on an iPhone and other mobile devices. Based on the current GPS position of a mobile device, DBpedia Mobile renders a map indicating nearby locations from the DBpedia dataset. Starting from this map, the user can explore background information about his or her surroundings. Revyu . corn is a Web site where you can review and rate whatever is possible to identify (through a URI) on the Web. Nevertheless, the potential advantages implicit in the Web of Linked Data are far from being fully exploited. Current applications hardly go beyond presenting together data gathered from different sources. Recently, research on the Web of Linked Data has been devoted to the study of models and languages to add functionalities to the Web of Linked Data by means of Linked Data services.

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Abstract

Search is the preferred method to access information in today's computing systems. The Web, accessed through search engines, is universally recognized as the source for answering users' information needs. However, offering a link to a Web page does not cover all information needs. Even simple problems, such as "Which theater offers an at least three-stars action movie in London close to a good Italian restaurant," can only be solved by searching the Web multiple times, e.g., by extracting a list of the recent action movies filtered by ranking, then looking for movie theaters, then looking for Italian restaurants close to them. While search engines hint to useful information, the user's brain is the fundamental platform for information integration. An important trend is the availability of new, specialized data sources-the so-called "long tail" of the Web of data. Such carefully collected and curated data sources can be much more valuable than information currently available in Web pages; however, many sources remain hidden or insulated, in the lack of software solutions for bringing them to surface and making them usable in the search context. A new class of tailor-made systems, designed to satisfy the needs of users with specific aims, will support the publishing and integration of data sources for vertical domains; the user will be able to select sources based on individual or collective trust, and systems will be able to route queries to such sources and to provide easyto-use interfaces for combining them within search strategies, at the same time, rewarding the data source owners for each contribution to effective search. Efforts such as Google's Fusion Tables show that the technology for bringing hidden data sources to surface is feasible.

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Abstract

New technologies will underpin the future generation of library catalogues. To facilitate their role providing information, serving users, and fulfilling their mission as cultural heritage and memory institutions, libraries must take a technological leap; their standards and services must be transformed to those of the Semantic Web. Bibliographic Information Organization in the Semantic Web explores the technologies that may power future library catalogues, and argues the necessity of such a leap. The text introduces international bibliographic standards and models, and fundamental concepts in their representation in the context of the Semantic Web. Subsequent chapters cover bibliographic information organization, linked open data, methodologies for publishing library metadata, discussion of the wider environment (museum, archival and publishing communities) and users, followed by a conclusion.

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Content

Inhalt: 1. Einleitung (A. Dengel, A. Bernardi) 2. Wissensrepräsentation (A. Dengel, A. Bernardi, L. van Elst) 3. Semantische Netze, Thesauri und Topic Maps (O. Rostanin, G. Weber) 4. Das Ressource Description Framework (T. Roth-Berghofer) 5. Ontologien und Ontologie-Abgleich in verteilten Informationssystemen (L. van Elst) 6. Anfragesprachen und Reasoning (M. Sintek) 7. Linked Open Data, Semantic Web Datensätze (G.A. Grimnes, O. Hartig, M. Kiesel, M. Liwicki) 8. Semantik in der Informationsextraktion (B. Adrian, B. Endres-Niggemeyer) 9. Semantische Suche (K. Schumacher, B. Forcher, T. Tran) 10. Erklärungsfähigkeit semantischer Systeme (B. Forcher, T. Roth-Berghofer, S. Agne) 11. Semantische Webservices zur Steuerung von Prooduktionsprozessen (M. Loskyll, J. Schlick, S. Hodeck, L. Ollinger, C. Maxeiner) 12. Wissensarbeit am Desktop (S. Schwarz, H. Maus, M. Kiesel, L. Sauermann) 13. Semantische Suche für medizinische Bilder (MEDICO) (M. Möller, M. Sintek) 14. Semantische Musikempfehlungen (S. Baumann, A. Passant) 15. Optimierung von Instandhaltungsprozessen durch Semantische Technologien (P. Stephan, M. Loskyll, C. Stahl, J. Schlick)

Editor

Dengel, A.

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Abstract

This book constitutes the refereed proceedings of the 12th Extended Semantic Web Conference, ESWC 2014, held in Anissaras, Portoroz, Slovenia, in May/June 2015. The 43 revised full papers presented together with three invited talks were carefully reviewed and selected from 164 submissions. This program was completed by a demonstration and poster session, in which researchers had the chance to present their latest results and advances in the form of live demos. In addition, the PhD Symposium program included 12 contributions, selected out of 16 submissions. The core tracks of the research conference were complemented with new tracks focusing on linking machine and human computation at web scale (cognition and Semantic Web, Human Computation and Crowdsourcing) beside the following subjects Vocabularies, Schemas, Ontologies, Reasoning, Linked Data, Semantic Web and Web Science, Semantic Data Management, Big data, Scalability, Natural Language Processing and Information Retrieval, Machine Learning, Mobile Web, Internet of Things and Semantic Streams, Services, Web APIs and the Web of Things, Cognition and Semantic Web, Human Computation and Crowdsourcing and In-Use Industrial Track as well

Content

Inhalt (Auszug) Vocabularies, Schemas, Ontologies: Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment / Valentina Ivanova, Patrick Lambrix, and Johan Åberg -- RODI: A Benchmark for Automatic Mapping Generation in Relational-to-Ontology Data Integration / Christoph Pinkel, Carsten Binnig, Ernesto Jiménez-Ruiz, Wolfgang May, Dominique Ritze, Martin G. Skjæveland, Alessandro Solimando, and Evgeny Kharlamov -- VocBench: A Web Application for Collaborative Development of Multilingual Thesauri. / Armando Stellato, Sachit Rajbhandari, Andrea Turbati, Manuel Fiorelli, Caterina Caracciolo, Tiziano Lorenzetti, Johannes Keizer, and Maria Teresa Pazienza -- Leveraging and Balancing Heterogeneous Sources of Evidence in Ontology Learning / Gerhard Wohlgenannt Natural Language Processing and Information Retrieval Learning a Cross-Lingual Semantic Representation of Relations Expressed in Text / Achim Rettinger, Artem Schumilin, Steffen Thoma, and Basil Ell