Discovering information flow using a high dimensional conceptual space.
Song, Dawei; Bruza, Peter
This paper presents an informational inference mechanism realized via the use of a high dimensional conceptual space. More specifically, we claim to have operationalized important aspects of GÃ¤rdenforss recent three-level cognitive model. The connectionist level is primed with the Hyperspace Analogue to Language (HAL) algorithm which produces vector representations for use at the conceptual level. We show how inference at the symbolic level can be implemented by employing Barwise and Seligmans theory of information flow. This article also features heuristics for enhancing HAL-based representations via the use of quality properties, determining concept inclusion and computing concept composition. The worth of these heuristics in underpinning informational inference are demonstrated via a series of experiments. These experiments, though small in scale, show that informational inference proposed in this article has a very different character to the semantic associations produced by the Minkowski distance metric and concept similarity computed via the cosine coefficient. In short, informational inference generally uncovers concepts that are carried, or, in some cases, implied by another concept, (or combination of concepts).
|Start Date||Sep 9, 2001|
|Publication Date||Dec 31, 2001|
|Publisher||Association for Computing Machinery|
|Institution Citation||SONG, D. and BRUZA, P. 2001. Discovering information flow using a high dimensional conceptual space. In Proceedings of the 24th Annual international Association of Computing Machinery Special Interest Group on Information Retrieval (ACM SIGIR) conference on research and development in information retrieval (SIGIR'01), 9-13 September 2001, Louisiana, USA. New York: ACM [online], pages 327-333. Available from: https://doi.org/10.1145/383952.384017|
|Keywords||Conceptual space; Information flow; Information inference|
SONG 2001 Discovering information flow
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