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Author: search.filters.author.Gavankar, ChetanaSubject: search.filters.subject.Web Ontologies

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    Enriching Concept Search across Semantic Web Ontologies
    (CEUR, 2013) Gavankar, Chetana
    Semantic Web ontologies are fast-growing knowledge sources on the Web. Searching relevant concepts from this large repository is a challenging problem. The current Semantic Web search engines provide either (1) coarse-grained search over ontologies or (2) very ne-grained search over individuals.We believe searching and ranking concepts across ontologies provides an ideal granularity for certain tasks such as ontology population and web page annotation. Towards this objective, we propose a novel approach of indexing concepts using ontology axioms in an inverted le structure and ranking them using a dynamic ranking algorithm. Our proposed method is generic and domain-independent. A preliminary evaluation indicates that our proposed method is e ective, outperforming the search function of BioPortal, a large and widely-used ontology repository.
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    Context-driven Concept Search across Web Ontologies using Keyword Queries
    (ACM Digital Library, 2015-10) Gavankar, Chetana
    Concepts in ontologies can be used in many scenarios, including annotation of online resources, automatic ontology population, and document classification to improve web search results. Collectively, tens of millions of concepts have been defined in a large number of ontologies that cover many overlapping domains. The scale, duplication and ambiguity makes concept search a challenging problem. We present a novel concept search approach that exploits structures present in ontologies and constructs contexts to effectively filter the noise in concept search results. The three key components of our approach are (1) a context for each concept extracted from relevant properties and axioms, (2) query interpretation based on the extracted context and (3) result ranking using learning to rank algorithms. We evaluate our approach on a large dataset from BioPortal. Our comprehensive evaluation is performed on 2,062,080 concepts and more than 2,000 queries, using two widely-employed performance metrics: normalized discounted cumulative gain (NDCG) and mean reciprocal rank (MRR). Our approach outperforms BioPortal significantly for multitoken queries that make up a large percentage of total queries.