Search

One of the major barriers to the deployment of Linked Data is the difficulty that data publishers have in determining which vocabularies to use to describe the semantics of data. This systematic report describes Linked Open Vocabularies (LOV), a high-quality catalogue of reusable vocabularies for the description of data on the Web. The LOV initiative gathers and makes visible indicators such as the interconnections between vocabularies and each vocabulary’s version history, along with past and current editor (individual or organization). The report details the various components of the system along with some innovations, such as the introduction of a property-level boost in the vocabulary search scoring that takes into account the property’s type (e.g., dc:comment) associated with a matching literal value. By providing an extensive range of data access methods (full-text search, SPARQL endpoint, API, data dump or UI), the project aims at facilitating the reuse of well-documented vocabularies in the Linked Data ecosystem. The adoption of LOV by many applications and methods shows the importance of such a set of vocabularies and related features for ontology design and the publication of data on the Web. [ABSTRACT FROM AUTHOR]

Ontology search is becoming increasingly important as the number of available ontologies on the Web steadily increases. Ontology recommendation is done by analyzing various properties of ontologies, such as syntax, structure, and usage, to find and recommend high-quality matches for a user defined query. Only a few ontology libraries and search engines facilitate this task for a user who wants to find an ontology that models all or some of the concepts she is looking for. In this paper, we introduce RecOn, a framework that helps users in finding the best matching ontologies to a multi-keyword query. Our approach recommends a ranked list of relevant ontologies using metrics that include the matching cost of a user query to an ontology, an ontology's informativeness, and its popularity. Based on these metrics two versions of RecOn are implemented: RecOnln, where the metrics are combined in a linear model to find the relevance score of an ontology to a query, and RecOnopt that formalizes ontology recommendation as an optimization problem to recommend ontologies to the user that are as informative and popular as possible while incurring the least matching costs. We compare both versions of RecOn with the state-of-the-art approach in ontology ranking by conducting a user study over the CBRBench ontology collection. Our experimental results show that both versions of the proposed approach are promising: they identify high-quality matches for keyword queries over real-life ontologies, and outperform the state-of-the-art ranking method significantly regarding effectiveness, while RecOnopt is more effective than RecOnln. We further test the scalability of our proposed approach, and results show RecOnopt is more efficient than RecOnln. [ABSTRACT FROM AUTHOR]

With the recent growth of Linked Data on theWeb 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. [ABSTRACT FROM AUTHOR]

Reuse of ontologies is important for achieving better interoperability among health systems and relieving knowledge engineers from the burden of developing ontologies from scratch. Most of the work that aims to facilitate ontology reuse has focused on building ontology libraries that are simple repositories of ontologies or has led to keyword-based search tools that search among ontologies. To our knowledge, there are no operational methodologies that allow users to evaluate ontologies and to compare them in order to choose the most appropriate ontology for their task. In this paper, we present, Knowledge Zone – a Web-based portal that allows users to submit their ontologies, to associate metadata with their ontologies, to search for existing ontologies, to find ontology rankings based on user reviews, to post their own reviews, and to rate reviews. [ABSTRACT FROM AUTHOR]