Concept learning and information inferencing on a high dimensional semantic space.
Bruza, Peter D.
Cole, R. J.
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How to automatically capture a significant portion of relevant background knowledge and keep it up-to-date has been a challenging problem encountered in current research on logic based information retrieval. This paper addresses this problem by investigating various information inference mechanisms based on a high dimensional semantic space constructed from a text corpus using the Hyperspace Analogue to Language (HAL) model. Additionally, the Singular Value Decomposition (SVD) algorithm is considered as an alternative way to enhance the quality of the HAL matrix as well as a mechanism of infering implicit associations. The different characteristics of these inference mechanisms are demonstrated using examples from the Reuters-21578 collection. Our hope is that the techniques discussed in this paper provide a basis for logic based IR to progress to large scale applications.