An approach to measuring semantic similarity and relatedness between concepts in an ontology
Semantic similarity and relatedness are applied more and more extensively in many fields, such as in Artificial Intelligence, Semantic Web and Knowledge Management. In this paper, we propose a comprehensive metric of similarity, a method of relatedness measure and a comprehensive degree measure that combines semantic similarity and relatedness between two concepts. Then we compare the proposed metrics with other classic similarity/relatedness metrics based on a benchmark data set and WordNet 3.0. The experimental results demonstrate that the proposed approach outperforms other classical computational approaches. The comprehensive degree metric gives the highest correlation value which is 0.913, and the semantic similarity measure obtains the second-highest correlation value which is 0.823 with a benchmark based on human similarity judgements.
We study the scalability problem of a priori-like algorithms that are used in mining association rules. We show how a priori suffers from performance deterioration for large-scale problems and propose alternative data structures and operations that can be used to apply the a priori-trick optimization method in large-scale problems. In the proposed method, the database is transformed into a more efficient structure that is used along with the intersection operation, to find the frequent itemsets in the database. The performance evaluation shows that, with a minor increase in the storage requirement, the proposed technique outperforms significantly the existing algorithms especially in large-scale problems.