DWRank: Learning concept ranking for ontology search

Issue title: Selected papers from the combined EKAW 2014 and Semantic Web journal track

Guest editors: Stefan Schlobach and Krzysztof Janowicz

Article type: Research Article

Authors: Butt, Anila Sahar^{a; b; *} | Haller, Armin^a | Xie, Lexing^a

Affiliations: [a] Australian National University, Canberra, Australia. E-mails: [email protected], [email protected], [email protected] | [b] CSIRO Digital Productivity, Canberra, Australia

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: 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 a given query term. In this paper we introduce DWRank, a two-staged bi-directional graph walk ranking algorithm for concepts in ontologies. DWRank characterises two features of a concept in an ontology to determine its rank in a corpus, the centrality of the concept to the ontology within which it is defined (HubScore) and the authoritativeness of the ontology in which it is defined (AuthorityScore). DWRank then uses a Learning to Rank approach to learn the feature weights for the two aforementioned ranking strategies. We compare DWRank with state-of-the-art ontology ranking models and traditional information retrieval algorithms. This evaluation shows that DWRank significantly outperforms the best ranking models on a benchmark ontology collection for the majority of the sample queries defined in the benchmark. In addition, we compare the effectiveness of the HubScore part of our algorithm with the state-of-the-art ranking model to determine a concept centrality and show the improved performance of DWRank in this aspect. Finally, we evaluate the effectiveness of the design decisions made for the AuthorityScore method in DWRank to find missing inter-ontology links and present a graph-based analysis of the ontology corpus that shows the increased connectivity of the ontology corpus after extraction of the implicit inter-ontology links.

Keywords: Ontology search, learning to rank, ontology ranking

DOI: 10.3233/SW-150185

Journal: Semantic Web, vol. 7, no. 4, pp. 447-461, 2016