An Information Retrieval Approach for Automatically Constructing Software Libraries
IEEE Transactions on Software Engineering
Conceptual schema analysis: techniques and applications
ACM Transactions on Database Systems (TODS)
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Algorithmic detection of semantic similarity
WWW '05 Proceedings of the 14th international conference on World Wide Web
Ontology ranking based on the analysis of concept structures
Proceedings of the 3rd international conference on Knowledge capture
CP/CV: concept similarity mining without frequency information from domain describing taxonomies
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Ontology Matching
Composing mappings among data sources
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Concept similarity by evaluating information contents and feature vectors: a combined approach
Communications of the ACM - Being Human in the Digital Age
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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Semantic search is an important approach that promises significant improvements for customers to identify products of their interest. To perform semantic search, enterprises need to publish semantically enriched descriptions of their offered goods and services; then a customer expresses his/her request, in an easy Google like fashion, by providing a list of desired features. If enterprise offerings and customer requests are based on the same vocabulary (i.e., ontology), they can be semantically matched by using advanced semantic methods. In this paper, we propose an ontology-based method aimed at finding the best matches between a user request and the services offered by different enterprises. We assume that in a given business ecosystem (in the paper, as an example, the tourism sector) a group of SMEs agree on the adoption of a reference ontology, used to build the company profiles on the basis of the offered services. Accordingly, a user request, represented by a set of desired features, is expressed in terms of the reference ontology terminology (i.e., concepts). In this paper, we illustrate SemSim, a method used to collectively search the SME profiles to identify the services that match at best the user request. SemSim is based on the well-known information content approach used to evaluate the semantic similarity between concepts. The experimental results show that our proposal performs better than some of the most representative similarity search methods proposed in the literature.