  The shortest path between two concepts in a taxonomic ontology is commonly
used to represent the semantic distance between concepts in the edge-based
semantic similarity measures. In the past, the edge counting is considered to
be the default method for the path computation, which is simple, intuitive and
has low computational complexity. However, a large lexical taxonomy of such as
WordNet has the irregular densities of links between concepts due to its broad
domain but. The edge counting-based path computation is powerless for this
non-uniformity problem. In this paper, we advocate that the path computation is
able to be separated from the edge-based similarity measures and form various
general computing models. Therefore, in order to solve the problem of
non-uniformity of concept density in a large taxonomic ontology, we propose a
new path computing model based on the compensation of local area density of
concepts, which is equal to the number of direct hyponyms of the subsumers of
concepts in their shortest path. This path model considers the local area
density of concepts as an extension of the edge-based path and converts the
local area density divided by their depth into the compensation for edge-based
path with an adjustable parameter, which idea has been proven to be consistent
with the information theory. This model is a general path computing model and
can be applied in various edge-based similarity algorithms. The experiment
results show that the proposed path model improves the average correlation
between edge-based measures with human judgments on Miller and Charles
benchmark from less than 0.8 to more than 0.85, and has a big advantage in
efficiency than information content (IC) computation in a dynamic ontology,
thereby successfully solving the non-uniformity problem of taxonomic ontology.
