Calculating Word Sense Probability Distributions for Semantic Web Applications

Researchers have found that Word Sense Disambiguation (WSD) is useful for tasks such as ontology alignment. Many other Semantic Web applications could also be enhanced with WSD results of Semantic Web documents. A system that can provide reusable intermediate WSD results is desirable. Compared to the top sense or a rank of senses, an output of meaningful scores of each possible sense informs subsequent processes of the certainty in results, and facilitates the application of other knowledge in choosing the correct sense. We propose that probabilistic models, which have proved successful in many other fields, can also be applied to WSD. Based on such observations, we focus on the problem of calculating probability distributions of senses for terms. In this paper we propose our novel WSD approach with our probability model, derive the problem formula into small computable pieces, and propose ways to estimate the values of these pieces.