  Various applications in the areas of computational linguistics and artificial
intelligence employ semantic similarity to solve challenging tasks, such as
word sense disambiguation, text classification, information retrieval, machine
translation, and document clustering. Previous work on semantic similarity
followed a mono-relational approach using mostly the taxonomic relation "ISA".
This paper explores the benefits of using all types of non-taxonomic relations
in large linked data, such as WordNet knowledge graph, to enhance existing
semantic similarity and relatedness measures. We propose a holistic
poly-relational approach based on a new relation-based information content and
non-taxonomic-based weighted paths to devise a comprehensive semantic
similarity and relatedness measure. To demonstrate the benefits of exploiting
non-taxonomic relations in a knowledge graph, we used three strategies to
deploy non-taxonomic relations at different granularity levels. We conducted
experiments on four well-known gold standard datasets, and the results
demonstrated the robustness and scalability of the proposed semantic similarity
and relatedness measure, which significantly improves existing similarity
measures.
