Angermann, H ., Pervez, Z., Ramzan, N.
Decision Support Systems, Elsevier, 98, 10-25 p.
Publication year: 2017

Taxonomies, also named directories, are utilized in e-catalogs to classify goods in a hierarchical manner with the help of concepts. If there is a need to create new concepts when modifying the taxonomy, the semantic similarity between the provided concepts has to be assessed properly. Existing semantic similarity assessment techniques lack in a comprehensive support for e-commerce, as those are not supporting multi-word expressions, multilingualism, the import/export to relational databases, and supervised user-involvement. This paper proposes Taxo-Semantics, a decision support system that is based on the progress in taxonomy matching to match each expression against various sources of background knowledge. The similarity assessment is based on providing three different matching strategies: a lexical-based strategy named Taxo-Semantics-Label, the strategy Taxo-Semantics-Bk, which is using different sources of background knowledge, and the strategy Taxo-Semantics-User that is providing user-involvement. The proposed system includes a translating service to analyze non-English concepts with the help of the WordNet lexicon, can parse taxonomies of relational databases, supports user-involvement to match single sequences with WordNet, and is capable to analyze each sequence as (sub)-taxonomy. The three proposed matching strategies significantly outperformed existing techniques. Taxo-Semantics-Label could improve the accuracy result by more than 7% as compared to state-of-the-art lexical techniques. Taxo-Semantics-Bk could improve the accuracy compared to structure-based techniques by more than 8%. And, Taxo-Semantics-User could additionally increase the accuracy by on average 23%.

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