Concept learning and information inferencing on a high-dimensional semantic space.

Song, Dawei; Bruza, Peter; Cole, Richard

Abstract

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.

Citation

SONG, D., BRUZA, P. and COLE, R. 2004. Concept learning and information inferencing on a high-dimensional semantic space. Presented at the 2004 Association of Computing Machinery Special Interest Group on Information Retrieval (ACM SIGIR) workshop on mathematical/formal methods in information retrieval (MF/IR 2004), 25-29 July 2004, Sheffield, UK.

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: 2004 Association of Computing Machinery Special Interest Group on Information Retrieval (ACM SIGIR) workshop on mathematical/formal methods in information retrieval (MF/IR 2004)
• Conference Location: Sheffield, UK
• Start Date: Jul 25, 2004
• End Date: Jul 29, 2004
• Deposit Date: Oct 2, 2009
• Publicly Available Date: Oct 2, 2009
• Keywords: Logic based information retrieval; Information inference
• Public URL: http://hdl.handle.net/10059/426