An Associative Semantic Network for Machine-Aided Indexing, Classification and Searching

Abstract

Capturing and exploiting textual database associations has played a pivotal role in the evolution of automated information systems. A variety of statistical, linguistic and artificial intelligence approaches have been described in the literature.Many of these R and D concepts and techniques are now being incorporated into commercially available search systems and services. This paper discusses prior work and reports on research in progress aimed at creating and utilizing a global semantic associative database, AURA (Associative User Retrieval Aid) to facilitate machine-assisted indexing, classification and searching in the large-scale information processing environment of NLM's core bibliographic databases, MEDLINE and CATLINE. AURA is a semantic network of over two million natural language phrases derived from more than a million MEDLINE titles. These natural language phrases are associatively linked to NLM's MeSH (Medical Subject Headings) and UMLS Metathesaurus (Unified Medical Language System) controlled vocabulary and classification resources.