Search (136 results, page 7 of 7)

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Abstract

The article presents a method for automatic semantic indexing of archaeological grey-literature reports using empirical (rule-based) Information Extraction techniques in combination with domain-specific knowledge organization systems. The semantic annotation system (OPTIMA) performs the tasks of Named Entity Recognition, Relation Extraction, Negation Detection, and Word-Sense Disambiguation using hand-crafted rules and terminological resources for associating contextual abstractions with classes of the standard ontology CIDOC Conceptual Reference Model (CRM) for cultural heritage and its archaeological extension, CRM-EH. Relation Extraction (RE) performance benefits from a syntactic-based definition of RE patterns derived from domain oriented corpus analysis. The evaluation also shows clear benefit in the use of assistive natural language processing (NLP) modules relating to Word-Sense Disambiguation, Negation Detection, and Noun Phrase Validation, together with controlled thesaurus expansion. The semantic indexing results demonstrate the capacity of rule-based Information Extraction techniques to deliver interoperable semantic abstractions (semantic annotations) with respect to the CIDOC CRM and archaeological thesauri. Major contributions include recognition of relevant entities using shallow parsing NLP techniques driven by a complimentary use of ontological and terminological domain resources and empirical derivation of context-driven RE rules for the recognition of semantic relationships from phrases of unstructured text.

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Source

Proceedings of the 15th European Networked Knowledge Organization Systems Workshop (NKOS 2016) co-located with the 20th International Conference on Theory and Practice of Digital Libraries 2016 (TPDL 2016), Hannover, Germany, September 9, 2016. Edi. by Philipp Mayr et al. [http://ceur-ws.org/Vol-1676/=urn:nbn:de:0074-1676-5]

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Abstract

Data from multiple knowledge organization systems are combined to provide a global overview of the content holdings of a small personal library. Subject headings and classification data are used to effectively map the combined book and topic space of the library. While harvested and manipulated by hand, the work reveals issues and potential solutions when using automated techniques to produce topic maps of much larger libraries. The small library visualized consists of the thirty-nine, digital, English language books found in the Osama Bin Laden (OBL) compound in Abbottabad, Pakistan upon his death. As this list of books has garnered considerable media attention, it is worth providing a visual overview of the subject content of these books - some of which is not readily apparent from the titles. Metadata from subject headings and classification numbers was combined to create book-subject maps. Tree maps of the classification data were also produced. The books contain 328 subject headings. In order to enhance the base map with meaningful thematic overlay, library holding count data was also harvested (and aggregated from duplicates). This additional data revealed the relative scarcity or popularity of individual books.

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Series

Lecture Notes in Computer Scienc;10370) (Information Systems and Applications, incl. Internet/Web, and HCI

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Footnote

Beitrag eines Special Issue: Research Information Systems and Science Classifications; including papers from "Trajectories for Research: Fathoming the Promise of the NARCIS Classification," 27-28 September 2018, The Hague, The Netherlands.

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Abstract

Current retrieval and recommendation approaches rely on hard-wired data models. This hinders personalized cus-tomizations to meet information needs of users in a more flexible manner. Therefore, the paper investigates how similarity-basedretrieval strategies can be combined with graph queries to enable users or system providers to explore repositories in the LinkedOpen Data (LOD) cloud more thoroughly. For this purpose, we developed novel content-based recommendation approaches.They rely on concept annotations of Simple Knowledge Organization System (SKOS) vocabularies and a SPARQL-based querylanguage that facilitates advanced and personalized requests for openly available knowledge graphs. We have comprehensivelyevaluated the novel search strategies in several test cases and example application domains (i.e., travel search and multimediaretrieval). The results of the web-based online experiments showed that our approaches increase the recall and diversity of rec-ommendations or at least provide a competitive alternative strategy of resource access when conventional methods do not providehelpful suggestions. The findings may be of use for Linked Data-enabled recommender systems (LDRS) as well as for semanticsearch engines that can consume LOD resources. (PDF) Similarity-based knowledge graph queries for recommendation retrieval. Available from: https://www.researchgate.net/publication/333358714_Similarity-based_knowledge_graph_queries_for_recommendation_retrieval [accessed May 21 2020].

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Abstract

Purpose The Integrative Levels Classification (ILC) is a comprehensive "freely faceted" knowledge organization system not previously expressed as SKOS (Simple Knowledge Organization System). This paper reports and reflects on work converting the ILC to SKOS representation. Design/methodology/approach The design of the ILC representation and the various steps in the conversion to SKOS are described and located within the context of previous work considering the representation of complex classification schemes in SKOS. Various issues and trade-offs emerging from the conversion are discussed. The conversion implementation employed the STELETO transformation tool. Findings The ILC conversion captures some of the ILC facet structure by a limited extension beyond the SKOS standard. SPARQL examples illustrate how this extension could be used to create faceted, compound descriptors when indexing or cataloguing. Basic query patterns are provided that might underpin search systems. Possible routes for reducing complexity are discussed. Originality/value Complex classification schemes, such as the ILC, have features which are not straight forward to represent in SKOS and which extend beyond the functionality of the SKOS standard. The ILC's facet indicators are modelled as rdf:Property sub-hierarchies that accompany the SKOS RDF statements. The ILC's top-level fundamental facet relationships are modelled by extensions of the associative relationship - specialised sub-properties of skos:related. An approach for representing faceted compound descriptions in ILC and other faceted classification schemes is proposed.

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Abstract

The purpose of this study is to provide a systematic literature review on taxonomy alignment methods in information science to explore the common research pipeline and characteristics. Design/methodology/approach The authors implement a five-step systematic literature review process relating to taxonomy alignment. They take on a knowledge organization system (KOS) perspective, and specifically examining the level of KOS on "taxonomies." Findings They synthesize the matching dimensions of 28 taxonomy alignment studies in terms of the taxonomy input, approach and output. In the input dimension, they develop three characteristics: tree shapes, variable names and symmetry; for approach: methodology, unit of matching, comparison type and relation type; for output: the number of merged solutions and whether original taxonomies are preserved in the solutions. Research limitations/implications The main research implications of this study are threefold: (1) to enhance the understanding of the characteristics of a taxonomy alignment work; (2) to provide a novel categorization of taxonomy alignment approaches into natural language processing approach, logic-based approach and heuristic-based approach; (3) to provide a methodological guideline on the must-include characteristics for future taxonomy alignment research. Originality/value There is no existing comprehensive review on the alignment of "taxonomies". Further, no other mapping survey research has discussed the comparison from a KOS perspective. Using a KOS lens is critical in understanding the broader picture of what other similar systems of organizations are, and enables us to define taxonomies more precisely.

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Abstract

Things change. Words change, meaning changes and use changes both words and meaning. In information access systems this means concept schemes such as thesauri or classification schemes change. They always have. Concept schemes that have survived have evolved over time, moving from one version, often called an edition, to the next. If we want to manage how words and meanings - and as a consequence use - change in an effective manner, and if we want to be able to search across versions of concept schemes, we have to track these changes. This paper explores how we might expand SKOS, a World Wide Web Consortium (W3C) draft recommendation in order to do that kind of tracking. The Simple Knowledge Organization System (SKOS) Core Guide is sponsored by the Semantic Web Best Practices and Deployment Working Group. The second draft, edited by Alistair Miles and Dan Brickley, was issued in November 2005. SKOS is a "model for expressing the basic structure and content of concept schemes such as thesauri, classification schemes, subject heading lists, taxonomies, folksonomies, other types of controlled vocabulary and also concept schemes embedded in glossaries and terminologies" in RDF. How SKOS handles version in concept schemes is an open issue. The current draft guide suggests using OWL and DCTERMS as mechanisms for concept scheme revision. As it stands an editor of a concept scheme can make notes or declare in OWL that more than one version exists. This paper adds to the SKOS Core by introducing a tracking system for changes in concept schemes. We call this tracking system vocabulary ontogeny. Ontogeny is a biological term for the development of an organism during its lifetime. Here we use the ontogeny metaphor to describe how vocabularies change over their lifetime. Our purpose here is to create a conceptual mechanism that will track these changes and in so doing enhance information retrieval and prevent document loss through versioning, thereby enabling persistent retrieval.

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Abstract

The paper discusses the importance of these initiatives in releasing as linked data the very large quantities of rich, professionally-generated metadata stored in formats based on these standards, such as UNIMARC and MARC21, addressing such issues as critical mass for semantic and statistical inferencing, integration with user- and machine-generated metadata, and authenticity, veracity and trust. The paper also discusses related initiatives to release controlled vocabularies, including the Dewey Decimal Classification (DDC), ISBD, Library of Congress Name Authority File (LCNAF), Library of Congress Subject Headings (LCSH), Rameau (French subject headings), Universal Decimal Classification (UDC), and the Virtual International Authority File (VIAF) as linked data. Finally, the paper discusses the potential collective impact of these initiatives on metadata workflows and management systems.

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Abstract

This dissertation studies ontology mapping: the problem of finding semantic correspondences between similar elements of different ontologies. In the dissertation, elements denote classes or properties of ontologies. The goal of this research is to use ontology mapping to make heterogeneous information more accessible. The World Wide Web (WWW) now is widely used as a universal medium for information exchange. Semantic interoperability among different information systems in the WWW is limited due to information heterogeneity, and the non semantic nature of HTML and URLs. Ontologies have been suggested as a way to solve the problem of information heterogeneity by providing formal, explicit definitions of data and reasoning ability over related concepts. Given that no universal ontology exists for the WWW, work has focused on finding semantic correspondences between similar elements of different ontologies, i.e., ontology mapping. Ontology mapping can be done either by hand or using automated tools. Manual mapping becomes impractical as the size and complexity of ontologies increases. Full or semi-automated mapping approaches have been examined by several research studies. Previous full or semiautomated mapping approaches include analyzing linguistic information of elements in ontologies, treating ontologies as structural graphs, applying heuristic rules and machine learning techniques, and using probabilistic and reasoning methods etc. In this paper, two generic ontology mapping approaches are proposed. One is the PRIOR+ approach, which utilizes both information retrieval and artificial intelligence techniques in the context of ontology mapping. The other is the non-instance learning based approach, which experimentally explores machine learning algorithms to solve ontology mapping problem without requesting any instance. The results of the PRIOR+ on different tests at OAEI ontology matching campaign 2007 are encouraging. The non-instance learning based approach has shown potential for solving ontology mapping problem on OAEI benchmark tests.

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Abstract

Metadata related to cultural items such as literature, music and movies is a valuable resource that is currently exploited in many applications and services based on semantic web technologies. A vast amount of such information has been created by memory institutions in the last decades using different standard or ad hoc schemas, and a main challenge is to make this legacy data accessible as reusable semantic data. On one hand, this is a syntactic problem that can be solved by transforming to formats that are compatible with the tools and services used for semantic aware services. On the other hand, this is a semantic problem. Simply transforming from one format to another does not automatically enable semantic interoperability and legacy data often needs to be reinterpreted as well as transformed. The conceptual model in the Functional Requirements for Bibliographic Records, initially developed as a conceptual framework for library standards and systems, is a major step towards a shared semantic model of the products of artistic and intellectual endeavor of mankind. The model is generally accepted as sufficiently generic to serve as a conceptual framework for a broad range of cultural heritage metadata. Unfortunately, the existing large body of legacy data makes a transition to this model difficult. For instance, most bibliographic data is still only available in various MARC-based formats which is hard to render into reusable and meaningful semantic data. Making legacy bibliographic data accessible as semantic data is a complex problem that includes interpreting and transforming the information. In this article, we present our work on transforming and enhancing legacy bibliographic information into a representation where the structure and semantics of the FRBR model is explicit.

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Abstract

This highly practical handbook teaches you how to unlock the value of your existing metadata through cleaning, reconciliation, enrichment and linking and how to streamline the process of new metadata creation. Libraries, archives and museums are facing up to the challenge of providing access to fast growing collections whilst managing cuts to budgets. Key to this is the creation, linking and publishing of good quality metadata as Linked Data that will allow their collections to be discovered, accessed and disseminated in a sustainable manner. This highly practical handbook teaches you how to unlock the value of your existing metadata through cleaning, reconciliation, enrichment and linking and how to streamline the process of new metadata creation. Metadata experts Seth van Hooland and Ruben Verborgh introduce the key concepts of metadata standards and Linked Data and how they can be practically applied to existing metadata, giving readers the tools and understanding to achieve maximum results with limited resources. Readers will learn how to critically assess and use (semi-)automated methods of managing metadata through hands-on exercises within the book and on the accompanying website. Each chapter is built around a case study from institutions around the world, demonstrating how freely available tools are being successfully used in different metadata contexts. This handbook delivers the necessary conceptual and practical understanding to empower practitioners to make the right decisions when making their organisations resources accessible on the Web. Key topics include, the value of metadata; metadata creation - architecture, data models and standards; metadata cleaning; metadata reconciliation; metadata enrichment through Linked Data and named-entity recognition; importing and exporting metadata; ensuring a sustainable publishing model. This will be an invaluable guide for metadata practitioners and researchers within all cultural heritage contexts, from library cataloguers and archivists to museum curatorial staff. It will also be of interest to students and academics within information science and digital humanities fields. IT managers with responsibility for information systems, as well as strategy heads and budget holders, at cultural heritage organisations, will find this a valuable decision-making aid.

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Content

"Last week, I received an email from Yulia Skora in Ukraine who is working on the mapping between UDC Summary and BBK (Bibliographic Library Classification) Summary. It reminded me of yet another challenging area of work. When responding to Yulia I realised that the issues with mapping, for instance, UDC Summary to Dewey Summaries [pdf] are often made more difficult because we have to deal with classification summaries in both systems and we cannot use a known exactMatch in many situations. In 2008, following advice received from colleagues in the HILT project, two of our colleagues quickly mapped 1000 classes of Dewey Summaries to UDC Master Reference File as a whole. This appeared to be relatively simple. The mapping in this case is simply an answer to a question "and how would you say e.g. Art metal work in UDC?" But when in 2009 we realised that we were going to release 2000 classes of UDC Summary as linked data, we decided to wait until we had our UDC Summary set defined and completed to be able to publish it mapped to the Dewey Summaries. As we arrived at this stage, little did we realise how much more complex the reversed mapping of UDC Summary to Dewey Summaries would turn out to be. Mapping the Dewey Summaries to UDC highlighted situations in which the logic and structure of two systems do not agree. Especially because Dewey tends to enumerate combinations of subject and attributes that do not always logically belong together. For instance, 850 Literatures of Italian, Sardinian, Dalmatian, Romanian, Rhaeto-Romanic languages Italian literature. This class mixes languages from three different subgroups of Romance languages. Italian and Sardinian belong to Italo Romance sub-family; Romanian and Dalmatian are Balkan Romance languages and Rhaeto Romance is the third subgroup that includes Friulian Ladin and Romanch. As UDC literature is based on a strict classification of language families, Dewey class 850 has to be mapped to 3 narrower UDC classes 821.131 Literature of Italo-Romance Languages , 821.132 Literature of Rhaeto-Romance languages and 821.135 Literature of Balkan-Romance Languages, or to a broader class 821.13 Literature of Romance languages. Hence we have to be sure that we have all these classes listed in the UDC Summary to be able to express UDC-DDC many-to-one, specific-to-broader relationships.

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Content

Im Fall von Ontologien, Terminologien und Synonymringen ist das Mapping in oder aus einem Thesaurus nicht immer nützlich. Hier sind andere Formen sich gegenseitig befruchtender Nutzung zu empfehlen. Dies gilt besonders für Ontologien, die im Kontext des Semantischen Web kombiniert mit Thesauri eingesetzt werden können. ISO 25964-2 zeigt die Unterschiede zwischen Thesauri und Ontologien auf und gibt Beispiele für die Möglichkeiten interoperabler Funktionen. Praktische Implementation und weiterführende Arbeiten Und was bedeutet dies für SKOS, dem W3C-Standard für die Veröffentlichung von Simple Knowledge Organization Systems im Web? Die Arbeitsgruppen von SKOS und ISO 25964 haben glücklicherweise eng zusammengearbeitet, was sich in einer guten Kompatibilität der beiden Standards äußert. Gemeinsam haben sie eine Tabelle erarbeitet, die die Übereinstimmungen zwischen den Datenmodellen von ISO 25964 und SKOS/SKOS-XL aufzeigt und nun beim ISO 25964-Sekretariat unter http://www.niso.org/schemas/iso25964/frei zugänglich ist. Die Tabelle ist formal kein Bestandteil der beiden Standards, aber von beiden abhängig. Das macht sie zu einem Beispiel für die Entwicklung praktischer Tools für das sich erweiternde Semantische Web durch die Gemeinschaft seiner Nutzer."

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Abstract

After the launch of the World Wide Web, it became clear that searching documentson the Web would not be trivial. Well-known engines to search the web, like Google, focus on search in web documents using keywords. The documents are structured and indexed to ensure keywords match documents as accurately as possible. However, searching by keywords does not always suice. It is oen the case that users do not know exactly how to formulate the search query or which keywords guarantee retrieving the most relevant documents. Besides that, it occurs that users rather want to browse information than looking up something specific. It turned out that there is need for systems that enable more interactivity and facilitate the gradual refinement of search queries to explore the Web. Users expect more from the Web because the short keyword-based queries they pose during search, do not suffice for all cases. On top of that, the Web is changing structurally. The Web comprises, apart from a collection of documents, more and more linked data, pieces of information structured so they can be processed by machines. The consequently applied semantics allow users to exactly indicate machines their search intentions. This is made possible by describing data following controlled vocabularies, concept lists composed by experts, published uniquely identifiable on the Web. Even so, it is still not trivial to explore data on the Web. There is a large variety of vocabularies and various data sources use different terms to identify the same concepts.