Search (20 results, page 1 of 1)

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Content

Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.

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Abstract

One vision of the Semantic Web is that it will be much like the Web we know today, except that documents will be enriched by annotations in machine understandable markup. These annotations will provide metadata about the documents as well as machine interpretable statements capturing some of the meaning of document content. We discuss how the information retrieval paradigm might be recast in such an environment. We suggest that retrieval can be tightly bound to inference. Doing so makes today's Web search engines useful to Semantic Web inference engines, and causes improvements in either retrieval or inference to lead directly to improvements in the other.

Date

12. 2.2011 17:35:22

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Abstract

A new model is proposed to retrieve information by building automatically a semantic metatext structure for texts that allow searching and extracting discourse and semantic information according to certain linguistic categorizations. This paper presents approaches for searching and mining full text with semantic categories. The model is built up from two engines: The first one, called EXCOM (Djioua et al., 2006; Alrahabi, 2010), is an automatic system for text annotation, related to discourse and semantic maps, which are specification of general linguistic ontologies founded on the Applicative and Cognitive Grammar. The annotation layer uses a linguistic method called Contextual Exploration, which handles the polysemic values of a term in texts. Several 'semantic maps' underlying 'point of views' for text mining guide this automatic annotation process. The second engine uses semantic annotated texts, produced previously in order to create a semantic inverted index, which is able to retrieve relevant documents for queries associated with discourse and semantic categories such as definition, quotation, causality, relations between concepts, etc. (Djioua & Desclés, 2007). This semantic indexation process builds a metatext layer for textual contents. Some data and linguistic rules sets as well as the general architecture that extend third-party software are expressed as supplementary information.

Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64423.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

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Footnote

Vgl.: http://www.igi-global.com/book/next-generation-search-engines/64424.

Source

Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

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Abstract

As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we briefy describe how Semplore is used for searching Wikipedia and an IBM customer's product information.

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Abstract

The Web of Data keeps growing rapidly. However, the full exploitation of this large amount of structured data faces numerous challenges like usability, scalability, imprecise information needs and data change. We present Semplore, an IR-based system that aims at addressing these issues. Semplore supports intuitive faceted search and complex queries both on text and structured data. It combines imprecise keyword search and precise structured query in a unified ranking scheme. Scalable query processing is supported by leveraging inverted indexes traditionally used in IR systems. This is combined with a novel block-based index structure to support efficient index update when data changes. The experimental results show that Semplore is an efficient and effective system for searching the Web of Data and can be used as a basic infrastructure for Web-scale Semantic Web search engines.

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Abstract

The power of search is with no doubt one of the main aspects for the success of the Web. Currently available search engines on the Web allow to return results with a high precision. Nevertheless, if we limit our attention only to lookup search we are missing another important search task. In exploratory search, the user is willing not only to find documents relevant with respect to her query but she is also interested in learning, discovering and understanding novel knowledge on complex and sometimes unknown topics. In the paper we address this issue presenting LED, a web based system that aims to improve (lookup) Web search by enabling users to properly explore knowledge associated to her query. We rely on DBpedia to explore the semantics of keywords within the query thus suggesting potentially interesting related topics/keywords to the user.

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Abstract

This volume contains the lecture notes of the 13th Reasoning Web Summer School, RW 2017, held in London, UK, in July 2017. In 2017, the theme of the school was "Semantic Interoperability on the Web", which encompasses subjects such as data integration, open data management, reasoning over linked data, database to ontology mapping, query answering over ontologies, hybrid reasoning with rules and ontologies, and ontology-based dynamic systems. The papers of this volume focus on these topics and also address foundational reasoning techniques used in answer set programming and ontologies.

Content

Neumaier, Sebastian (et al.): Data Integration for Open Data on the Web - Stamou, Giorgos (et al.): Ontological Query Answering over Semantic Data - Calì, Andrea: Ontology Querying: Datalog Strikes Back - Sequeda, Juan F.: Integrating Relational Databases with the Semantic Web: A Reflection - Rousset, Marie-Christine (et al.): Datalog Revisited for Reasoning in Linked Data - Kaminski, Roland (et al.): A Tutorial on Hybrid Answer Set Solving with clingo - Eiter, Thomas (et al.): Answer Set Programming with External Source Access - Lukasiewicz, Thomas: Uncertainty Reasoning for the Semantic Web - Calvanese, Diego (et al.): OBDA for Log Extraction in Process Mining

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Abstract

Using semantic web search engines, such as Watson, Swoogle or Sindice, to find ontologies is a complex exploratory activity. It generally requires formulating multiple queries, browsing pages of results, and assessing the returned ontologies against each other to obtain a relevant and adequate subset of ontologies for the intended use. Our hypothesis is that at least some of the difficulties related to searching ontologies stem from the lack of structure in the search results, where ontologies that are implicitly related to each other are presented as disconnected and shown on different result pages. In earlier publications we presented a software framework, Kannel, which is able to automatically detect and make explicit relationships between ontologies in large ontology repositories. In this paper, we present a study that compares the use of the Watson ontology search engine with an extension,Watson+Kannel, which provides information regarding the various relationships occurring between the result ontologies. We evaluate Watson+Kannel by demonstrating through various indicators that explicit relationships between ontologies improve users' efficiency in ontology search, thus validating our hypothesis.

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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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Content

"Textual content-based search engines for the web have a number of limitations. Firstly, many web resources have little or no textual content (images, audio or video streams etc.) Secondly, precision is low where natural language terms have overloaded meaning (e.g. 'bank', 'watch', 'chip' etc.) Thirdly, recall is incomplete where the search does not take account of synonyms or quasi-synonyms. Fourthly, there is no basis for assisting a user in modifying (expanding, refining, translating) a search based on the meaning of the original search. Fifthly, there is no basis for searching across natural languages, or framing search queries in terms of symbolic languages. The Semantic Web is a framework for creating, managing, publishing and searching semantically rich metadata for web resources. Annotating web resources with precise and meaningful statements about conceptual aspects of their content provides a basis for overcoming all of the limitations of textual content-based search engines listed above. Creating this type of metadata requires that metadata generators are able to refer to shared repositories of meaning: 'vocabularies' of concepts that are common to a community, and describe the domain of interest for that community.

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Abstract

Important information is often scattered across Web and/or intranet resources. Traditional search engines return ranked retrieval lists that offer little or no information on the semantic relationships among documents. Knowledge workers spend a substantial amount of their time browsing and reading to find out how documents are related to one another and where each falls into the overall structure of the problem domain. Yet only when knowledge workers begin to locate the similarities and differences among pieces of information do they move into an essential part of their work: building relationships to create new knowledge. Information retrieval traditionally focuses on the relationship between a given query (or user profile) and the information store. On the other hand, exploitation of interrelationships between selected pieces of information (which can be facilitated by the use of ontologies) can put otherwise isolated information into a meaningful context. The implicit structures so revealed help users use and manage information more efficiently. Knowledge management tools are needed that integrate the resources dispersed across Web resources into a coherent corpus of interrelated information. Previous research in information integration has largely focused on integrating heterogeneous databases and knowledge bases, which represent information in a highly structured way, often by means of formal languages. In contrast, the Web consists to a large extent of unstructured or semi-structured natural language texts. As we have seen, ontologies offer an alternative way to cope with heterogeneous representations of Web resources. The domain model implicit in an ontology can be taken as a unifying structure for giving information a common representation and semantics. Once such a unifying structure exists, it can be exploited to improve browsing and retrieval performance in information access tools. QuizRDF is an example of such a tool.

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Date

31. 7.2010 16:58:22

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Date

14. 8.2011 13:33:22

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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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Date

26.12.2011 13:40:22

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Source

Bulletin of the Association for Information Science and Technology. 41(2015) no.4, S.18-22

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Abstract

In order to transfer the Chinese Classified Thesaurus (CCT) into a machine-processable format and provide CCT-based Web services, a pilot study has been conducted in which a variety of selected CCT classes and mapped thesaurus entries are encoded with SKOS. OWL and RDFS are also used to encode the same contents for the purposes of feasibility and cost-benefit comparison. CCT is a collected effort led by the National Library of China. It is an integration of the national standards Chinese Library Classification (CLC) 4th edition and Chinese Thesaurus (CT). As a manually created mapping product, CCT provides for each of the classes the corresponding thesaurus terms, and vice versa. The coverage of CCT includes four major clusters: philosophy, social sciences and humanities, natural sciences and technologies, and general works. There are 22 main-classes, 52,992 sub-classes and divisions, 110,837 preferred thesaurus terms, 35,690 entry terms (non-preferred terms), and 59,738 pre-coordinated headings (Chinese Classified Thesaurus, 2005) Major challenges of encoding this large vocabulary comes from its integrated structure. CCT is a result of the combination of two structures (illustrated in Figure 1): a thesaurus that uses ISO-2788 standardized structure and a classification scheme that is basically enumerative, but provides some flexibility for several kinds of synthetic mechanisms Other challenges include the complex relationships caused by differences of granularities of two original schemes and their presentation with various levels of SKOS elements; as well as the diverse coordination of entries due to the use of auxiliary tables and pre-coordinated headings derived from combining classes, subdivisions, and thesaurus terms, which do not correspond to existing unique identifiers. The poster reports the progress, shares the sample SKOS entries, and summarizes problems identified during the SKOS encoding process. Although OWL Lite and OWL Full provide richer expressiveness, the cost-benefit issues and the final purposes of encoding CCT raise questions of using such approaches.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

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Date

16.11.2018 14:22:01

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Date

20. 1.2015 18:30:22