Learning to Refine Ontology for a New Web Site Using a Bayesian Approach
Tak-Lam Wong, Wai Chun Lam · 2005
We develop a probabilistic framework which can refine an existing ontology from a source Web site to new unseen sites. One characteristic of our framework is to consider several clues related to how an ontology influences the text content and the visual layout of the Web pages. The first clue is the text fragments regarding the content of the concepts previously collected or extracted from the source Web site. The second clue is the text fragments regarding the header labels of the concepts. The third clue is the visual layout of the text fragments regarding the content of the concepts and the header labels of the concepts in the unseen site. To harness the uncertainty involved in a rigorous manner, we formalize these clues by a generative model to represent the generation of text fragments regarding the concepts and the ontology corresponding to the Web page. Bayesian learning technique and expectation-maximization (EM) algorithm are employed to accomplish the task. Extensive experiments on several real-world Web sites from two different domains have been conducted to demonstrate the effectiveness of our framework.