Abstract
Measuring semantic relatedness is one of the important fundamental technical processes. In this thesis, we propose an approach to find the semantic primitives embedded in a common sense database (ConceptNet) and the algorithms to measure the concept semantic relatedness. We used the Random Walk Algorithm to analyze the common sense database first, and adopt the HITS, a well-known web rank algorithm, to find the semantic primitives in this database. Then we propose two algorithms to measure the semantic relatedness between different pairs of concepts. We adopted the Spearman’s correlation score as criteria of semantic relatedness and compared the performance of our methods against some benchmark data. Our performance in terms of Spearman’s correlation score ranging from 0.54 to 0.8.