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Semantic Web

About: Semantic Web is a research topic. Over the lifetime, 26987 publications have been published within this topic receiving 534275 citations. The topic is also known as: Sem Web & SemWeb.


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Book
31 Aug 2000
TL;DR: This chapter discusses the structure and Dynamics of Organizational Knowledge, and the role of the Intranet as Infrastructure for Knowledge Work, in the context of knowledge work on the World Wide Web.
Abstract: Section I: Information Seeking and Knowledge Work. 1. Information Seeking. 2. The Structure and Dynamics of Organizational Knowledge. Section II: Knowledge Work on Intranets. 3. The Intranet as Infrastructure for Knowledge Work. 4. Designing Intranets to Support Knowledge Work. Section III: Information Seeking on the World Wide Web. 5. Models of Information Seeking on the World Wide Web. 6. Understanding Organizational Web Use. Coda. References. Index.

291 citations

Proceedings ArticleDOI
10 May 2005
TL;DR: An approach that ranks results based on how predictable a result might be for users is presented, based on a relevance model SemRank, which is a rich blend of semantic and information-theoretic techniques with heuristics that supports the novel idea of modulative searches, where users may vary their search modes to effect changes in the ordering of results depending on their need.
Abstract: While the idea that querying mechanisms for complex relationships (otherwise known as Semantic Associations) should be integral to Semantic Web search technologies has recently gained some ground, the issue of how search results will be ranked remains largely unaddressed Since it is expected that the number of relationships between entities in a knowledge base will be much larger than the number of entities themselves, the likelihood that Semantic Association searches would result in an overwhelming number of results for users is increased, therefore elevating the need for appropriate ranking schemes Furthermore, it is unlikely that ranking schemes for ranking entities (documents, resources, etc) may be applied to complex structures such as Semantic AssociationsIn this paper, we present an approach that ranks results based on how predictable a result might be for users It is based on a relevance model SemRank, which is a rich blend of semantic and information-theoretic techniques with heuristics that supports the novel idea of modulative searches, where users may vary their search modes to effect changes in the ordering of results depending on their need We also present the infrastructure used in the SSARK system to support the computation of SemRank values for resulting Semantic Associations and their ordering

291 citations

Book ChapterDOI
20 Oct 2003
TL;DR: The KIM platform allows KIM-based applications to use it for automatic semantic annotation, content retrieval based on semantic restrictions, and querying and modifying the underlying ontologies and knowledge bases.
Abstract: The KIM platform provides a novel Knowledge and Information Management infrastructure and services for automatic semantic annotation, indexing, and retrieval of documents. It provides mature infrastructure for scaleable and customizable information extraction (IE) as well as annotation and document management, based on GATE. In order to provide basic level of performance and allow easy bootstrapping of applications, KIM is equipped with an upper-level ontology and a knowledge base providing extensive coverage of entities of general importance. The ontologies and knowledge bases involved are handled using cutting edge Semantic Web technology and standards, including RDF(S) repositories, ontology middleware and reasoning. From technical point of view, the platform allows KIM-based applications to use it for automatic semantic annotation, content retrieval based on semantic restrictions, and querying and modifying the underlying ontologies and knowledge bases. This paper presents the KIM platform, with emphasize on its architecture, interfaces, tools, and other technical issues.

291 citations

Proceedings Article
30 Jul 2001
TL;DR: The trials and tribulations of building such an ontology represented in RDF Schema are described and it is demonstrated how this ontology can be exploited and reused by other communities on the semantic web to enable the inclusion and exchange of multimedia content through a common understanding of the associated MPEG-7 multimedia content descriptions.
Abstract: For the past two years the Moving Pictures Expert Group (MPEG), a working group of ISO/IEC, have been developing MPEG-7 [1], the "Multimedia Content Description Interface", a standard for describing multimedia content. The goal of this standard is to develop a rich set of standardized tools to enable both humans and machines to generate and understand audiovisual descriptions which can be used to enable fast efficient retrieval from digital archives (pull applications) as well as filtering of streamed audiovisual broadcasts on the Internet (push applications). MPEG-7 is intended to describe audiovisual information regardless of storage, coding, display, transmission, medium, or technology. It will address a wide variety of media types including: still pictures, graphics, 3D models, audio, speech, video, and combinations of these (e.g., multimedia presentations). MPEG-7 is due for completion in October 2001. At this stage MPEG-7 definitions (description schemes and descriptors) are expressed solely in XML Schema [2-4]. XML Schema has been ideal for expressing the syntax, structural, cardinality and datatyping constraints required by MPEG-7. However it has become increasingly clear that in order to make MPEG-7 accessible, re-usable and interoperable with other domains then the semantics of the MPEG-7 metadata terms also need to be expressed in an ontology using a machine-understandable language. This paper describes the trials and tribulations of building such an ontology represented in RDF Schema [5] and demonstrates how this ontology can be exploited and reused by other communities on the semantic web (such as TV-Anytime [6], MPEG-21 [7], NewsML [8], museum, educational and geospatial domains) to enable the inclusion and exchange of multimedia content through a common understanding of the associated MPEG-7 multimedia content descriptions.

290 citations

Proceedings ArticleDOI
01 Apr 2001
TL;DR: This paper proposes the novel concept of intelligent crawling which actually learns characteristics of the linkage structure of the world wide web while performing the crawling, and refers to this technique as intelligent crawling because of its adaptive nature in adjusting to the web page linkage structure.
Abstract: The enormous growth of the world wide web in recent years has made it important to perform resource discovery e ciently. Consequently, several new ideas have been proposed in recent years; among them a key technique is focused crawling which is able to crawl particular topical portions of the world wide web quickly without having to explore all web pages. In this paper, we propose the novel concept of intelligent crawling which actually learns characteristics of the linkage structure of the world wide web while performing the crawling. Speci cally, the intelligent crawler uses the inlinking web page content, candidate URL structure, or other behaviors of the inlinking web pages or siblings in order to estimate the probability that a candidate is useful for a given crawl. This is a much more general framework than the focused crawling technique which is based on a pre-de ned understanding of the topical structure of the web. The techniques discussed in this paper are applicable for crawling web pages which satisfy arbitrary user-de ned predicates such as topical queries, keyword queries or any combinations of the above. Unlike focused crawling, it is not necessary to provide representative topical examples, since the crawler can learn its way into the appropriate topic. We refer to this technique as intelligent crawling because of its adaptive nature in adjusting to the web page linkage structure. The learning crawler is capable of reusing the knowledge gained in a given crawl in order to provide more e cient crawling for closely related predicates.

289 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023116
2022348
2021412
2020612
2019782
2018881