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The shortest path between two concepts in a taxonomic ontology is commonlyused to represent the semantic distance between concepts in the edge-basedsemantic similarity measures. In the past, the edge counting is considered tobe the default method for the path computation, which is simple, intuitive andhas low computational complexity. However, a large lexical taxonomy of such asWordNet has the irregular densities of links between concepts due to its broaddomain but. The edge counting-based path computation is powerless for thisnon-uniformity problem. In this paper, we advocate that the path computation isable to be separated from the edge-based similarity measures and form variousgeneral computing models. Therefore, in order to solve the problem ofnon-uniformity of concept density in a large taxonomic ontology, we propose anew path computing model based on the compensation of local area density ofconcepts, which is equal to the number of direct hyponyms of the subsumers ofconcepts in their shortest path. This path model considers the local areadensity of concepts as an extension of the edge-based path and converts thelocal area density divided by their depth into the compensation for edge-basedpath with an adjustable parameter, which idea has been proven to be consistentwith the information theory. This model is a general path computing model andcan be applied in various edge-based similarity algorithms. The experimentresults show that the proposed path model improves the average correlationbetween edge-based measures with human judgments on Miller and Charlesbenchmark from less than 0.8 to more than 0.85, and has a big advantage inefficiency than information content (IC) computation in a dynamic ontology,thereby successfully solving the non-uniformity problem of taxonomic ontology.

A density compensation-based path computing model for measuring semantic similarity