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Semantics for the Semantic Web

The Implicit, the Formal and the Powerful

descriptionPublicationkeyboard_double_arrow_right Article , Part of book or chapter of book 01 Jan 2005 United States Publisher:IGI GlobalJournal:International Journal on Semantic Web and Information Systems, volume 1, pages 1-18 (issn: 1552-6283, eissn: 1552-6291,

Authors: Sheth, Amit P.; Ramakrishnan, Cartic; Thomas, Christopher;

doi: 10.4018/jswis.2005010101 , 10.4018/9781599044262.ch001 , 10.4018/978-1-59904-426-2.ch001

Semantics for the Semantic Web

- Summary
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Abstract

Enabling applications that exploit heterogeneous data in the Semantic Web will require us to harness a broad variety of semantics. Considering the role of semantics in a number of research areas in computer science, we organize semantics in three forms — implicit, formal, and powerful — and explore their roles in enabling some of the key capabilities related to the Semantic Web. The central message of this article is that building the Semantic Web purely on description logics will artificially limit its potential, and that we will need to both exploit well-known techniques that support implicit semantics, and develop more powerful semantic techniques.

Country

United States

Related Organizations

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Keywords

Databases and Information Systems, Relationship Discovery, Bioinformatics, OS and Networks, computer science, Implicit Semantics, Semantic Integration, Social and Behavioral Sciences, Science and Technology Studies, Semantic Analytics, semantic web, Physical Sciences and Mathematics, Informal Semantics, Computer Engineering, Document Management and Retrieval, Semantic Web, Semantic Search, Metadata, Analytical Processing, Computer Sciences, Communication, Life Sciences, Electrical and Computer Engineering, Knowledge Discovery, 004, Semantic Matching, Semantic Technology, Communication Technology and New Media, semantic, Soft Computing, Formal Semantics

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	131
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 1%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 1%