Ontology Search: An Empirical Evaluation

Butt, Anila Sahar; Haller, Armin; Xie, Lexing

doi:10.1007/978-3-319-11915-1_9

Cited by 18 publications

(37 citation statements)

References 15 publications

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“…The details of this work are presented in [2]. We are still looking into ways to extend the benchmark to evaluate search techniques efficiency and effectiveness in recommending an ontology covering user specified terms.…”

Section: Cbrbench: Canberra Ontology Ranking Benchmarkmentioning

confidence: 99%

“…• Effectiveness of the ranking model will be evaluated by comparing the results with the state-of-the-art techniques proposed by [2].…”

Section: Evaluation Planmentioning

confidence: 99%

“…More recently, a dedicated ontology search engine has emerged [14], however there are three major issues with existing approaches: (1) The ranking algorithms they use are based only on document-ranking algorithms. Moreover, most of the ranking techniques in existing ontology libraries and search engines only consider the popularity of terms in the ontology corpus, often using the PageRank algorithm, which although effective in some cases [2] hinders the visibility of newly emerged well defined ontologies. (2) Most of the ontology search systems retrieve ontological terms (concepts and relations) and some provide ontology search based on some keywords.…”

mentioning

confidence: 99%

“…Moreover, most of the ranking techniques in existing ontology libraries and search engines only consider the popularity of terms in the ontology corpus, often using the PageRank algorithm, which although effective in some cases [2] hinders the visibility of newly emerged well defined ontologies. (2) Most of the ontology search systems retrieve ontological terms (concepts and relations) and some provide ontology search based on some keywords. However, almost all ontology libraries and search engines do not facilitate the task of ontology retrieval for a use-case where a user looks for an ontology that models all or some of the concepts and relations.…”

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confidence: 99%

“…Moreover, in ontology search keyword queries are still the preferred method to find concepts and relations in the registered ontologies [2]. The search results are a match of a search term with more expressive concepts, relations or ontology descriptions.…”

mentioning

confidence: 99%

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Ontology Search

Butt

2015

Proceedings of the 24th International Conference on World Wide Web

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With the recent growth of Linked Data on the Web, there is an increased need for knowledge engineers to find ontologies to describe their data. Only limited work exists that addresses the problem of searching and ranking ontologies based on keyword queries. In this proposal we introduce the main challenges to find appropriate ontologies, and preliminary solutions to address these challenges. Our evaluation shows that the proposed solution performs significantly better than existing solutions on a benchmark ontology collection for the majority of the sample queries defined in the benchmark.

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Section: Cbrbench: Canberra Ontology Ranking Benchmarkmentioning

confidence: 99%

“…• Effectiveness of the ranking model will be evaluated by comparing the results with the state-of-the-art techniques proposed by [2].…”

Section: Evaluation Planmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Ontology Search

Butt

2015

Proceedings of the 24th International Conference on World Wide Web

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Popularity-Driven Ontology Ranking Using Qualitative Features

Kolbe¹,

Kubler²,

Traon³

2019

Lecture Notes in Computer Science

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Efficient ontology reuse is a key factor in the Semantic Web to enable and enhance the interoperability of computing systems. One important aspect of ontology reuse is concerned with ranking most relevant ontologies based on a keyword query. Apart from the semantic match of query and ontology, the state-of-the-art often relies on ontologies' occurrences in the Linked Open Data (LOD) cloud to determine relevance. We observe that ontologies of some application domains, in particular those related to Web of Things (WoT), often do not appear in the underlying LOD datasets used to define ontologies' popularity, resulting in ineffective ranking scores. This motivated us to investigate -based on the problematic WoT case -whether the scope of ranking models can be extended by relying on qualitative attributes instead of an explicit popularity feature. We propose a novel approach to ontology ranking by (i) selecting a range of relevant qualitative features, (ii) proposing a popularity measure for ontologies based on scholarly data, (iii) training a ranking model that uses ontologies' popularity as prediction target for the relevance degree, and (iv) confirming its validity by testing it on independent datasets derived from the state-of-the-art. We find that qualitative features help to improve the prediction of the relevance degree in terms of popularity. We further discuss the influence of these features on the ranking model.

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