Weighted Partonomy-taxonomy Trees with Local Similarity Measures for Semantic Buyer-seller Match-making

A semantically enhanced weighted tree similarity algorithm for buyer-seller match-making is presented. First, our earlier global, structural similarity measure over (product) partonomy trees is enriched by taxonomic semantics: Inner nodes can be labeled by classes whose partial subsumption order is represented as a taxonomy tree that is used for similarity computation. In particular, the similarity of any two classes can be defined via the weighted length of the shortest path connecting them in the taxonomy. Instead of a taxonomy separate from the partonomy, we encode the taxonomy tree into the partonomy tree (as a “Classification” branch) in a way that allows us to directly reuse our partonomy similarity algorithm and to permit weighted (or 'fuzzy') taxonomic subsumption with no added effort. Second, leaf nodes can be typed and each type is associated with a local, special-purpose similarity measure realising the semantics to be invoked when computing the similarity of any two of its instances. We illustrate local similarity measures with e-Business types such as “Currency”, “Address”, “Date”, and “Price”. For example, the similarity measure on “Date”-typed leaf node labels translates various notations for date instances into a normal form from which it linearly maps any two to their similarity value. Finally, previous adjustment functions, which prevent similarity degradation for our arbitrarily wide and deep trees, are enhanced by smoother functions that evenly compensate intermediate similarity values.