Ontology based algorithms for indexing and search of semantically close natural language phrases

Abstract

Free text constitutes a overwhelming fraction of information available on the World Wide Web. Specifically, consider small chunks of natural language phrases frequently used by Web users to describe stuff relevant to them. For example, consider the following two posts on a classifieds site (which serves a small locality, say, a university campus) - "2 Tickets for the prom tonight" and "Trade 2 extra passes for tonight's Ball for $25". For a human looking at these two posts, its trivial to conclude that he has found what he wanted. But when there are thousands of such posts and in the absence of any common keywords or any additional information from the user it is unlikely that naive keyword based matching will be of any help in reflecting the glaring similarity between these descriptions. This problem is very relevant and challenging because users tend to describe the same item in several dif ferent ways. Humans frequently use their commonsense and background knowledge to infer that these relate to the same item. However the enormous sizes of most datasets prohibit manual classification. To automate this, we present intuitive and scalable algorithms which use existing Ontologies like WordNet to correctly relate semantically close descriptions.