Differential Evolutionary Algorithm Based on Multiple Vector Metrics for Semantic Similarity Assessment in Continuous Vector Space

Automatic service discovery in heterogeneous environment is becoming one of the challenging problems for applications in semantic web, wireless sensor networks, etc. It is mainly due to the lack of accurate semantic similarity assessment between profile attributes of user request and web services. Generally, lexical semantic resources consist of corpus and domain knowledge. To improve similarity measures in terms of accuracy, various hybrid methods have been proposed to either integrate different semantic resources or combine various similarity methods based on a single resource. In this work, we propose a novel approach which combines vector similarity metrics in a continuous vector space to evaluate semantic similarity between concepts. This approach takes advantage of both corpus and knowledge base by constructing diverse vector space models. Specifically, we use differential evolutionary (DE) algorithm which is an powerful population-based stochastic search strategy for obtaining optimal value of the combination. Our approach has been validated against a variety of vector-based similarity approaches on multiple benchmark datasets. The empirical results demonstrate that our approach outperforms the state-of-the-art approaches. The results also indicate the continuous vectors are efficient for evaluating semantic similarity, since they have outstanding expressiveness to latent semantic features of words. Moreover, the robustness of our approach is presented by the steady measure results under different hyper-parameters of neural network. Keywords-differential evolutionary; semantic similarity; continuous vector space; vector similarity metrics