Search (25 results, page 1 of 2)

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Abstract

By the explosion of possibilities for a ubiquitous content production, the information overload problem reaches the level of complexity which cannot be managed by traditional modelling approaches anymore. Due to their pure syntactical nature traditional information retrieval approaches did not succeed in treating content itself (i.e. its meaning, and not its representation). This leads to a very low usefulness of the results of a retrieval process for a user's task at hand. In the last ten years ontologies have been emerged from an interesting conceptualisation paradigm to a very promising (semantic) modelling technology, especially in the context of the Semantic Web. From the information retrieval point of view, ontologies enable a machine-understandable form of content description, such that the retrieval process can be driven by the meaning of the content. However, the very ambiguous nature of the retrieval process in which a user, due to the unfamiliarity with the underlying repository and/or query syntax, just approximates his information need in a query, implies a necessity to include the user in the retrieval process more actively in order to close the gap between the meaning of the content and the meaning of a user's query (i.e. his information need). This thesis lays foundation for such an ontology-based interactive retrieval process, in which the retrieval system interacts with a user in order to conceptually interpret the meaning of his query, whereas the underlying domain ontology drives the conceptualisation process. In that way the retrieval process evolves from a query evaluation process into a highly interactive cooperation between a user and the retrieval system, in which the system tries to anticipate the user's information need and to deliver the relevant content proactively. Moreover, the notion of content relevance for a user's query evolves from a content dependent artefact to the multidimensional context-dependent structure, strongly influenced by the user's preferences. This cooperation process is realized as the so-called Librarian Agent Query Refinement Process. In order to clarify the impact of an ontology on the retrieval process (regarding its complexity and quality), a set of methods and tools for different levels of content and query formalisation is developed, ranging from pure ontology-based inferencing to keyword-based querying in which semantics automatically emerges from the results. Our evaluation studies have shown that the possibilities to conceptualize a user's information need in the right manner and to interpret the retrieval results accordingly are key issues for realizing much more meaningful information retrieval systems.

Content

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Source

Research and advanced technology for digital libraries : 10th European conference, proceedings / ECDL 2006, Alicante, Spain, September 17 - 22, 2006

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Abstract

Ontologies will play a central role in the development of the Semantic Web. It is unrealistic to assume that such ontologies will be developed from scratch. Rather, we assume that existing resources such as thesauri and lexical data bases will be reused in the development of ontologies for the Semantic Web. In this paper we describe a method for converting existing source material to a representation that is compatible with Semantic Web languages such as RDF(S) and OWL. The method is illustrated with three case studies: converting Wordnet, AAT and MeSH to RDF(S) and OWL.

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Date

31. 7.2010 16:58:22

Source

Semantic digital libraries. Eds.: S.R. Kruk, B. McDaniel

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Abstract

How do we test Semantic Web tools? How can we know that they perform better than current technologies for knowledge management? What does 'better' precisely mean? How can we operationalize and measure this? Some of these questions may be partially answered by simulations in lab experiments that for example look at the speed or scalability of algorithms. However, it is not clear in advance to what extent such laboratory results carry over to the real world. Quality is in the eye of the beholder, and so the quality of Semantic Web methods will very much depend on the perception of their usefulness as seen by tool users. This can only be tested by carefully designed field experiments. In this chapter, we discuss the design considerations and set-up of field experiments with Semantic Web tools, and illustrate these with case examples from a virtual organization in industrial research.

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Abstract

Plenty of contemporary attempts to search exist that are associated with the area of Semantic Web. But which of them qualify as information retrieval for the Semantic Web? Do such approaches exist? To answer these questions we take a look at the nature of the Semantic Web and Semantic Desktop and at definitions for information and data retrieval. We survey current approaches referred to by their authors as information retrieval for the Semantic Web or that use Semantic Web technology for search.

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Abstract

This paper provides an analysis of the knowledge structure underlying DBpedia, one of the largest and most heavily used datasets in the current Linked Data landscape. The study reveals an evolving knowledge representation environment where different descriptive and classification approaches are employed concurrently. This analysis opens up a new area of research to which the knowledge organization community can make a significant contribution.

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Footnote

Rez. in: Annals of Library and Information Studies 62(2015) no.4, S.299-300 (A.K. Das)

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Abstract

Developing ontologies is central to our vision of Semantic Web-based knowledge management. The methodology described in Chapter 3 guides the development of ontologies for different applications. However, because of the size of ontologies, their complexity, their formal underpinnings and the necessity to come towards a shared understanding within a group of people when defining an ontology, ontology construction is still far from being a well-understood process. Concerning the methodology, OntoEdit focuses on three of the main steps for ontology development (the methodology is described in Chapter 3), viz. the kick off, refinement, and evaluation. We describe the steps supported by OntoEdit and focus on collaborative aspects that occur during each of the step. First, all requirements of the envisaged ontology are collected during the kick off phase. Typically for ontology engineering, ontology engineers and domain experts are joined in a team that works together on a description of the domain and the goal of the ontology, design guidelines, available knowledge sources (e.g. re-usable ontologies and thesauri, etc.), potential users and use cases and applications supported by the ontology. The output of this phase is a semiformal description of the ontology. Second, during the refinement phase, the team extends the semi-formal description in several iterations and formalizes it in an appropriate representation language like RDF(S) or, more advanced, DAML1OIL. The output of this phase is a mature ontology (the 'target ontology'). Third, the target ontology needs to be evaluated according to the requirement specifications. Typically this phase serves as a proof for the usefulness of ontologies (and ontology-based applications) and may involve the engineering team as well as end users of the targeted application. The output of this phase is an evaluated ontology, ready for roll-out into a productive environment. Support for these collaborative development steps within the ontology development methodology is crucial in order to meet the conflicting needs for ease of use and construction of complex ontology structures. We now illustrate OntoEdit's support for each of the supported steps. The examples shown are taken from the Swiss Life case study on skills management (cf. Chapter 12).

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Abstract

This article introduces the Simple Knowledge Organisation System (SKOS), a Semantic Web language for representing controlled structured vocabularies, including thesauri, classification schemes, subject heading systems and taxonomies. SKOS provides a framework for publishing thesauri, classification schemes, and subject indexes on the Web, and for applying these systems to resource collections that are part of the SemanticWeb. SemanticWeb applications may harvest and merge SKOS data, to integrate and enhances retrieval service across multiple collections (e.g. libraries). This article also describes some alternatives for integrating Semantic Web services based on the Resource Description Framework (RDF) and SKOS into a distributed enterprise architecture.

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Abstract

An information-seeking system is described which combines traditional keyword querying of WWW resources with the ability to browse and query against RDF annotations of those resources. RDF(S) and RDF are used to specify and populate an ontology and the resultant RDF annotations are then indexed along with the full text of the annotated resources. The resultant index allows both keyword querying against the full text of the document and the literal values occurring in the RDF annotations, along with the ability to browse and query the ontology. We motivate our approach as a key enabler for fully exploiting the Semantic Web in the area of knowledge management and argue that the ability to combine searching and browsing behaviours more fully supports a typical information-seeking task. The approach is characterised as "low threshold, high ceiling" in the sense that where RDF annotations exist they are exploited for an improved information-seeking experience but where they do not yet exist, a search capability is still available.

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Abstract

While much of a company's knowledge can be found in text repositories, current content management systems have limited capabilities for structuring and interpreting documents. In the emerging Semantic Web, search, interpretation and aggregation can be addressed by ontology-based semantic mark-up. In this paper, we examine semantic annotation, identify a number of requirements, and review the current generation of semantic annotation systems. This analysis shows that, while there is still some way to go before semantic annotation tools will be able to address fully all the knowledge management needs, research in the area is active and making good progress.

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Abstract

The main purpose of this book is to sum up the vital and highly topical research issue of knowledge representation on the Web and to discuss novel solutions by combining benefits of folksonomies and Web 2.0 approaches with ontologies and semantic technologies. This book contains an overview of knowledge representation approaches in past, present and future, introduction to ontologies, Web indexing and in first case the novel approaches of developing ontologies. This title combines aspects of knowledge representation for both the Semantic Web (ontologies) and the Web 2.0 (folksonomies). Currently there is no monographic book which provides a combined overview over these topics. focus on the topic of using knowledge representation methods for document indexing purposes. For this purpose, considerations from classical librarian interests in knowledge representation (thesauri, classification schemes etc.) are included, which are not part of most other books which have a stronger background in computer science.

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Abstract

An information-seeking system is described which combines traditional keyword querying of WWW resources with the ability to browse and query against RD annotations of those resources. RDF(S) and RDF are used to specify and populate an ontology and the resultant RDF annotations are then indexed along with the full text of the annotated resources. The resultant index allows both keyword querying against the full text of the document and the literal values occurring in the RDF annotations, along with the ability to browse and query the ontology. We motivate our approach as a key enabler for fully exploiting the Semantic Web in the area of knowledge management and argue that the ability to combine searching and browsing behaviours more fully supports a typical information-seeking task. The approach is characterised as "low threshold, high ceiling" in the sense that where RDF annotations exist they are exploited for an improved information-seeking experience but where they do not yet exist, a search capability is still available.

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Source

Semantic digital libraries. Eds.: S.R. Kruk, B. McDaniel

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Date

14. 8.2011 13:33:22

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Abstract

Currently, techniques for content description and query processing in Information Retrieval (IR) are based on keywords, and therefore provide limited capabilities to capture the conceptualizations associated with user needs and contents. Aiming to solve the limitations of keyword-based models, the idea of conceptual search, understood as searching by meanings rather than literal strings, has been the focus of a wide body of research in the IR field. More recently, it has been used as a prototypical scenario (or even envisioned as a potential "killer app") in the Semantic Web (SW) vision, since its emergence in the late nineties. However, current approaches to semantic search developed in the SW area have not yet taken full advantage of the acquired knowledge, accumulated experience, and technological sophistication achieved through several decades of work in the IR field. Starting from this position, this work investigates the definition of an ontology-based IR model, oriented to the exploitation of domain Knowledge Bases to support semantic search capabilities in large document repositories, stressing on the one hand the use of fully fledged ontologies in the semantic-based perspective, and on the other hand the consideration of unstructured content as the target search space. The major contribution of this work is an innovative, comprehensive semantic search model, which extends the classic IR model, addresses the challenges of the massive and heterogeneous Web environment, and integrates the benefits of both keyword and semantic-based search. Additional contributions include: an innovative rank fusion technique that minimizes the undesired effects of knowledge sparseness on the yet juvenile SW, and the creation of a large-scale evaluation benchmark, based on TREC IR evaluation standards, which allows a rigorous comparison between IR and SW approaches. Conducted experiments show that our semantic search model obtained comparable and better performance results (in terms of MAP and P@10 values) than the best TREC automatic system.

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Date

12. 2.2011 17:35:22

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Abstract

18 August 2009 -- Today W3C announces a new standard that builds a bridge between the world of knowledge organization systems - including thesauri, classifications, subject headings, taxonomies, and folksonomies - and the linked data community, bringing benefits to both. Libraries, museums, newspapers, government portals, enterprises, social networking applications, and other communities that manage large collections of books, historical artifacts, news reports, business glossaries, blog entries, and other items can now use Simple Knowledge Organization System (SKOS) to leverage the power of linked data. As different communities with expertise and established vocabularies use SKOS to integrate them into the Semantic Web, they increase the value of the information for everyone.