Semantic indexing and searching using a Hopfield net

Hsinchun Chen, Yin Zhang, Andrea L. Houston

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


This paper presents a neural network approach to document semantic indexing. A Hopfield net algorithm was used to simulate human associative memory for concept exploration in the domain of computer science and engineering. INSFEC, a collection of more than 320,000 document abstracts from leading journals, was used as the document testbed. Benchmark tests confirmed that three parameters (maximum number of activated nodes, ε - maximum allowable error, and maximum number of iterations] were useful in positively influencing network convergence behavior without negatively impacting central processing unit performance. Another series of benchmark tests was performed to determine the effectiveness of various filtering techniques in reducing the negative impact of noisy input terms. Preliminary user tests confirmed our expectation that the Hopfield net algorithm is potentially useful as an associative memory technique to improve document recall and precision by solving discrepancies between indexer vocabularies and end-user vocabularies.

Original languageEnglish (US)
Pages (from-to)3-18
Number of pages16
JournalJournal of Information Science
Issue number1
StatePublished - 1998

ASJC Scopus subject areas

  • Information Systems
  • Library and Information Sciences


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