This paper presents a framework for knowledge discovery and concept exploration. In order to enhance the concept exploration capability of knowledge‐based systems and to alleviate the limitations of the manual browsing approach, we have developed two spreading activation‐based algorithms for concept exploration in large, heterogeneous networks of concepts (e.g., multiple thesauri). One algorithm, which is based on the symbolic AI paradigm, performs a conventional branch‐and‐bound search on a semantic net representation to identify other highly relevant concepts (a serial, optimal search process). The second algorithm, which is based on the neural network approach, executes the Hopfield net parallel relaxation and convergence process to identify “convergent” concepts for some initial queries (a parallel, heuristic search process). Both algorithms can be adopted for automatic, multiple‐thesauri consultation. We tested these two algorithms on a large text‐based knowledge network of about 13,000 nodes (terms) and 80,000 directed links in the area of computing technologies. This knowledge network was created from two external thesauri and one automatically generated thesaurus. We conducted experiments to compare the behaviors and performances of the two algorithms with the hypertext‐like browsing process. Our experiment revealed that manual browsing achieved higher‐term recall but lower‐term precision in comparison to the algorithmic systems. However, it was also a much more laborious and cognitively demanding process. In document retrieval, there were no statistically significant differences in document recall and precision between the algorithms and the manual browsing process. In light of the effort required by the manual browsing process, our proposed algorithmic approach presents a viable option for efficiently traversing large‐scale, multiple thesauri (knowledge network). © 1995 John Wiley & Sons, Inc.
|Original language||English (US)|
|Number of pages||22|
|Journal||Journal of the American Society for Information Science|
|State||Published - Jun 1995|
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