OntoSearch: An Ontology Search Engine

Zhang, Yi; Vasconcelos, Wamberto; Sleeman, Derek

doi:10.1007/1-84628-102-4_5

Cited by 48 publications

(31 citation statements)

References 2 publications

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“…Similarly, brute-force sampling, i.e., testing port 80 of reachable IP addresses [11], introduces prohibitive cost in validating millions of web documents. Meta-search based approaches [28] are limited by the inability to filter out conventional web documents from search engine results and the fact that some search engines intentionally ignore SWDs. Manual submission based approaches, such as that used for the DAML ontology library [9] scale poorly and are difficult to maintain.…”

Section: A Hybrid Semantic Web Harvesting Frameworkmentioning

confidence: 99%

Characterizing the Semantic Web on the Web

Ding

Finin

2006

Lecture Notes in Computer Science

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Abstract. Semantic Web languages are being used to represent, encode and exchange semantic data in many contexts beyond the Web -in databases, multiagent systems, mobile computing, and ad hoc networking environments. The core paradigm, however, remains what we call the Web aspect of the Semantic Web -its use by independent and distributed agents who publish and consume data on the World Wide Web. To better understand this central use case, we have harvested and analyzed a collection of Semantic Web documents from an estimated ten million available on the Web. Using a corpus of more than 1.7 million documents comprising over 300 million RDF triples, we describe a number of global metrics, properties and usage patterns. Most of the metrics, such as the size of Semantic Web documents and the use frequency of Semantic Web terms, were found to follow a power law distribution.

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Section: A Hybrid Semantic Web Harvesting Frameworkmentioning

confidence: 99%

Characterizing the Semantic Web on the Web

Ding

Finin

2006

Lecture Notes in Computer Science

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“…We think that in a large-scale mode like (i.e. in whole web) they will be very similar to a multi context based search engines [8] Swoogle [2,3,4], Ontokhoj [27] OWLIR [2], QuizRDF [6], InWiss [7], Corese [9], Infofox [12], SHOE [15], DOSE [18], SERSE [22], ALVIS [17], OntoWeb [23], Score [25], 20,21, 24 W3C Semantic Search [5], ABC [5] SemDis [10,14], 13, 16 for evaluating ontology search engines. For enterprise search engines on the semantic web two important issues arise.…”

Section: Future Workmentioning

confidence: 97%

“…According to [1] and [8], for the following reasons, it is not possible to use current search engines for SW documents:…”

Section: Ontology Search Enginesmentioning

confidence: 99%

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A Categorization Scheme for Semantic Web Search Engines

Esmaili

Abolhassani

2006

IEEE International Conference on Computer Systems and Applications, 2006.

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Abstract-Semantic web search engines are evolving and many prototype systems and some implementation have been developed. However, there are some different views on what a semantic search engine should do. In this paper, a categorization scheme for semantic web search engines are introduced and elaborated. For each category, its components are described according to a proposed general architecture and various approaches employed in these components are discussed. We also propose some factors to evaluate systems in each category.

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“…Some ontology search engines have been developed that can provide lists of ontology that contain specific search terms. Examples of such are Swoogle [3] and OntoSearch [4]. Swoogle is a search engine for Semantic Web ontologies, documents, terms and data present on the Web.…”

Section: Introductionmentioning

confidence: 99%

A Novel and Hybrid Ontology Ranking Framework using Semantic Closeness Measure

Sridevi¹,

Umarani²

2014

IJCA

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Semantic Web is a Web that adds more meaning to the Web documents in order to access knowledge instead of unstructured material and also allowing knowledge to be processed automatically. One of the methods to achieve this is of using Ontology. The Ontology defines the terms and the relations among the terms on a domain. There are number of Ontology repositories present. When this increases day by day, the need for getting relevant ontology for the search keyword also increases. Even though there are number of semantic web search engines, Swoogle is placed first, which ranks the ontologies using an adaptation of Google's Page Rank scoring method. A major drawback with this system is that many ontologies are poorly inter-referenced, which does not reflect the quality of the ontologies. This paper reviews the methodologies used in Swoogle for computing rank score and proposes Semantic Closeness Measure (SCM) which has not been employed in any other ontology ranking algorithms. This work develops a hybrid ranking system to rank the ontologies better than Swoogle and other ontology search engines. The results confirm that the proposed system places the highly relevant and quality ontologies on the top list by reranking the Swoogle's results. This ranking framework enables the searcher to get relevant results quickly and reduces time in searching the long list of results.

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OntoSearch: An Ontology Search Engine

Cited by 48 publications

References 2 publications

Characterizing the Semantic Web on the Web

Characterizing the Semantic Web on the Web

A Categorization Scheme for Semantic Web Search Engines

A Novel and Hybrid Ontology Ranking Framework using Semantic Closeness Measure

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