Abstract
Deep Web database clustering is a key operation in organizing Deep Web resources. Cosine similarity in Vector Space Model (VSM) is used as the similarity computation in traditional ways. However it cannot denote the semantic similarity between the contents of two databases. In this paper how to cluster Deep Web databases semantically is discussed. Firstly, a fuzzy semantic measure, which integrates ontology and fuzzy set theory to compute semantic similarity between the visible features of two Deep Web forms, is proposed, and then a hybrid Particle Swarm Optimization (PSO) algorithm is provided for Deep Web databases clustering. Finally the clustering results are evaluated according to Average Similarity of Document to the Cluster Centroid (ASDC) and Rand Index (RI). Experiments show that: 1) the hybrid PSO approach has the higher ASDC values than those based on PSO and K-Means approaches. It means the hybrid PSO approach has the higher intra cluster similarity and lowest inter cluster similarity; 2) the clustering results based on fuzzy semantic similarity have higher ASDC values and higher RI values than those based on cosine similarity. It reflects the conclusion that the fuzzy semantic similarity approach can explore latent semantics.
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Song, L., Ma, J., Yan, P., Lian, L., Zhang, D. (2008). Clustering Deep Web Databases Semantically. In: Li, H., Liu, T., Ma, WY., Sakai, T., Wong, KF., Zhou, G. (eds) Information Retrieval Technology. AIRS 2008. Lecture Notes in Computer Science, vol 4993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68636-1_35
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DOI: https://doi.org/10.1007/978-3-540-68636-1_35
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