Automatic learning of text-to-concept mappings exploiting WordNet-like lexical networks
Proceedings of the 2005 ACM symposium on Applied computing
Price prediction and insurance for online auctions
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
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Current Data Mining techniques usually do not have amechanism to automatically infer semantic features inherentin the data being "mined". The semantics are eitherinjected in the initial stages (by feature construction) or byinterpreting the results produced by the algorithms. Bothof these techniques have proved effective but require a lotof human effort. In many domains, semantic informationis implicitly available and can be extracted automaticallyto improve data mining systems. In this paper, we present acase study of a system that is trained to extract semantic featuresfor apparel products and populate a knowledge basewith these products and features. We show that semanticfeatures of these items can be successfully extracted by applyingtext learning techniques to the descriptions obtainedfrom websites of retailers. We also describe several applicationsof such a knowledge base of product semantics that wehave built including recommender systems and competitiveintelligence tools and provide evidence that our approachcan successfully build a knowledge base with accurate factswhich can then be used to create profiles of individual customers,groups of customers, or entire retail stores.