The Web has been rapidly "deepened" with the prevalence of databases online. On this "deep Web," numerous sources are structured, providing schemarich data. Their schemas define the object domain and its query capabilities. This paper proposes clustering sources by their query schemas, which is critical for enabling both source selection and query mediation, by organizing sources of with similar query capabilities. In abstraction, this problem is essentially clustering categorical data (by viewing each query schema as a transaction). Our approach hypothesizes that "homogeneous sources" are characterized by the same hidden generative models for their schemas. To find clusters governed by such statistical distributions, we propose a novel objective function, model-differentiation, which employs principled hypothesis testing to maximize statistical heterogeneity among clusters. Our evaluation shows that, on clustering the Web query schemas, the model-differentiation function outperforms existing ones with the hierarchical agglomerative clustering algorithm.
|Original language||English (US)|
|Number of pages||11|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|State||Published - Jan 1 2004|
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)