Abstract
Semantic search has a great potentiality in helping users to make choices, since it appears to outperform traditional keyword-based approaches. This paper presents an ontology-based semantic search method, referred to as influential SemSim (i-SemSim), which relies on the Bayesian probabilistic approach for weighting the reference ontology. The Bayesian approach seems promising when the reference ontology is organized according to a Directed Acyclic Graph (DAG). In particular, in the proposed method the similarity among a user request and semantically annotated resources is evaluated. The user request, as well as each annotated resource, is represented by a set of concepts of the reference ontology. The experimental results of this paper show that the adoption of the Bayesian method for weighting DAG-based reference ontologies allows i-SemSim to outperform the most representative methods selected in the literature.
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- 1.
The proposed OFV approach is based on the Term Vector (or Vector Space) Model approach, where terms are substituted by concepts [10].
- 2.
A conditional probability table is defined for a set of (non-independent) random variables to represent the marginal probability of a single variable w.r.t. the others.
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Formica, A., Missikoff, M., Pourabbas, E., Taglino, F. (2017). A Bayesian Approach for Semantic Search Based on DAG-Shaped Ontologies. In: Benslimane, D., Damiani, E., Grosky, W., Hameurlain, A., Sheth, A., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science(), vol 10439. Springer, Cham. https://doi.org/10.1007/978-3-319-64471-4_12
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DOI: https://doi.org/10.1007/978-3-319-64471-4_12
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