Ontology Ranking: Finding the Right Ontologies on the Web

Ontology search, which is the process of finding ontologies or ontological terms for users’ defined queries from an ontology collection, is an important task to facilitate ontology reuse of ontology engineering. Ontology reuse is desired to avoid the tedious process of building an ontology from scratch and to limit the design of several competing ontologies that represent similar knowledge. Since many organisations in both the private and public sectors are publishing their data in RDF, they increasingly require to find or design ontologies for data annotation and/or integration. In general, there exist multiple ontologies representing a domain, therefore, finding the best matching ontologies or their terms is required to facilitate manual or dynamic ontology selection for both ontology design and data annotation. The ranking is a crucial component in the ontology retrieval process which aims at listing the ‘relevant0 ontologies or their terms as high as possible in the search results to reduce the human intervention. Most existing ontology ranking techniques inherit one or more information retrieval ranking parameter(s). They linearly combine the values of these parameters for each ontology to compute the relevance score against a user query and rank the results in descending order of the relevance score. A significant aspect of achieving an effective ontology ranking model is to develop novel metrics and dynamic techniques that can optimise the relevance score of the most relevant ontology for a user query. In this thesis, we present extensive research in ontology retrieval and ranking, where several research gaps in the existing literature are identified and addressed. First, we begin the thesis with a review of the literature and propose a taxonomy of Semantic Web data (i.e., ontologies and linked data) retrieval approaches. That allows us to identify potential research directions in the field. In the remainder of the thesis, we address several of the identified shortcomings in the ontology retrieval domain. We develop a framework for the empirical and comparative evaluation of different ontology ranking solutions, which has not been studied in the literature so far. Second, we propose an effective relationship-based concept retrieval framework and a concept ranking model through the use of learning to rank approach which addresses the limitation of the existing linear ranking models. Third, we propose RecOn, a framework that helps users in finding the best matching ontologies to a multi-keyword query. There the relevance score of an ontology to the query is computed by formulating and solving the ontology recommendation problem as a linear and an optimisation problem. Finally, the thesis also reports on an extensive comparative evaluation of our proposed solutions with several other state-of-the-art techniques using real-world ontologies. This thesis will be useful for researchers and practitioners interested in ontology search, for methods and performance benchmark on ranking approaches to ontology search

DWRank: Learning concept ranking for ontology search

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

Where to search top-K biomedical ontologies?

Motivation Searching for precise terms and terminological definitions in the biomedical data space is problematic, as researchers find overlapping, closely related and even equivalent concepts in a single or multiple ontologies. Search engines that retrieve ontological resources often suggest an extensive list of search results for a given input term, which leads to the tedious task of selecting the best-fit ontological resource (class or property) for the input term and reduces user confidence in the retrieval engines. A systematic evaluation of these search engines is necessary to understand their strengths and weaknesses in different search requirements. Result We have implemented seven comparable Information Retrieval ranking algorithms to search through ontologies and compared them against four search engines for ontologies. Free-text queries have been performed, the outcomes have been judged by experts and the ranking algorithms and search engines have been evaluated against the expert-based ground truth (GT). In addition, we propose a probabilistic GT that is developed automatically to provide deeper insights and confidence to the expert-based GT as well as evaluating a broader range of search queries. Conclusion The main outcome of this work is the identification of key search factors for biomedical ontologies together with search requirements and a set of recommendations that will help biomedical experts and ontology engineers to select the best-suited retrieval mechanism in their search scenarios. We expect that this evaluation will allow researchers and practitioners to apply the current search techniques more reliably and that it will help them to select the right solution for their daily work. Availability The source code (of seven ranking algorithms), ground truths and experimental results are available at https://github.com/danielapoliveira/bioont-search-benchmarkThis work has been supported by the Science Foundation Ireland (grant number SFI/12/RC/2289).peer-reviewed2019-03-2

Where to search top-K biomedical ontologies?

Motivation Searching for precise terms and terminological definitions in the biomedical data space is problematic, as researchers find overlapping, closely related and even equivalent concepts in a single or multiple ontologies. Search engines that retrieve ontological resources often suggest an extensive list of search results for a given input term, which leads to the tedious task of selecting the best-fit ontological resource (class or property) for the input term and reduces user confidence in the retrieval engines. A systematic evaluation of these search engines is necessary to understand their strengths and weaknesses in different search requirements. Result We have implemented seven comparable Information Retrieval ranking algorithms to search through ontologies and compared them against four search engines for ontologies. Free-text queries have been performed, the outcomes have been judged by experts and the ranking algorithms and search engines have been evaluated against the expert-based ground truth (GT). In addition, we propose a probabilistic GT that is developed automatically to provide deeper insights and confidence to the expert-based GT as well as evaluating a broader range of search queries. Conclusion The main outcome of this work is the identification of key search factors for biomedical ontologies together with search requirements and a set of recommendations that will help biomedical experts and ontology engineers to select the best-suited retrieval mechanism in their search scenarios. We expect that this evaluation will allow researchers and practitioners to apply the current search techniques more reliably and that it will help them to select the right solution for their daily work. Availability The source code (of seven ranking algorithms), ground truths and experimental results are available at https://github.com/danielapoliveira/bioont-search-benchmarkThis work has been supported by the Science Foundation Ireland (grant number SFI/12/RC/2289).peer-reviewed2019-03-2

Where to search top-K biomedical ontologies?

Motivation: Searching for precise terms and terminological definitions in the biomedical data space is problematic, as researchers find overlapping, closely related and even equivalent concepts in a single or multiple ontologies. Search engines that retrieve ontological resources often suggest an extensive list of search results for a given input term, which leads to the tedious task of selecting the best-fit ontological resource (class or property) for the input term and reduces user confidence in the retrieval engines. A systematic evaluation of these search engines is necessary to understand their strengths and weaknesses in different search requirements.This work has been supported by the Science Foundation Ireland (grant number SFI/12/RC/2289)