Paper Title
Set Similarity Analysis using Semantic Web Mining

A subgraph matching with set similarity (SMS2) query extracts subgraphs that are structurally isomorphic to the query, and satisfies the condition of vertex pair matching with weighted set similarity. To process the SMS2 query, a novel lattice-based index for data graph and signatures for both query vertices and data vertices are designed. The vertices that do not exceed the specified threshold are pruned using set similarity pruning and structure-based pruning. Vertices are arranged in decreasing order of their signatures. A dominating set selection algorithm is used to achieve better query performance. Index terms - Pattern Matching, Query Processing, Subgraph Matching with Set Similarity Query (SMS2 query), Structurally Isomorphic Subgraphs, Structure-based Pruning.