Intelligent Focusing in Knowledge Indexing and Retrieval: The Relatedness Tool
Year: 1988
Type: article
Abstract: Most present day information retrieval systems use the presence or absence of certain words to decide which documents are appropriate for a user's query. This approach has had certain successes, but it fails to capture relationships between concepts represented by the words, and hence reduces the potential specificity of both indexing and searching of documents. A richer representation of the semantics of documents and queries, and methods for reasoning about these representations, have been provided by artificial intelligence. Navigational tools for browsing and authoring knowledge bases (KB's) add a convenient technique for focusing in the complex landscape of semantic representations. The center of such representations is usually a frame or a semantic network system. We are developing a prototype Unified Medical Language System (UMLS) taxonomy to represent objects and relationships in medicine. One focus of our research is improved methods for indexing and querying repositories of biomedical literature. The technique which we propose is based on the notion of relatedness of concepts. To this end we define heuristics which find related concepts and apply it to the UMLS taxonomy. Preliminary results from experiments with the implemented heuristics demonstrate its potential usefulness.
Source: PubMed Central
Institution: Harvard University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W61259660