Inferencing in Information Retrieval

INFERENCING IN INFORMATION RETRIEVAL Alexa T. McCray National Library of Medicine Bethesda, Maryland 20894 ABSTRACT We have been addressing the problem of providing access to the free text in biomedical databases. The focus of our work is the development of SPECIALIST, an experimental NLP system for the biomedical domain. The system includes a broad coverage parser supported by a large lexicon, a module that provides access to extensive biomedical knowledge sources, and a retrieval module that allows us to carry out experiments in information retrieval. We have recently conducted experiments with a test collection of queries and documents retrieved for those queries. The purpose of the investigation has been to determine the type of information that is required in order to effect a map between the language of queries and the language of relevant documents. 1, INTRODUCTION Retrieval of information from computerized databases is a complex process whose success depends heavily on the user's knowledge of the structure and logic of the particular database being searched. Many databases have associated with them a controlled indexing vocabulary, or thesaurus, which is the primary access point to the material at search time. For example, the National of Library of Medicine's MESH® thesaurus includes some 16,000 headings that are available for indexing and searching the biomedical literature stored in MEDLINE®, NLM's bibliographic database. The major retrieval strategy is to coordinate MeSH terms with boolean operators, although limited text word searching of titles and abstracts is also possible. Several years ago NLM launched its Unified Medical Lan- guage System TM (UMLS TM) project. This is a major research initiative whose goal it is to facilitate retrieval and integration of information from multiple disparate biomedical databases. NLM itself has developed and maintains over 40 databases, and there are many other sources of computerized information in the biomedical sciences. These include factual databases of various kinds, diagnostic expert systems, clinical informa- tion systems, as well as bibliographic databases. The UMLS project is attempting to develop methods whereby access is provided to these different systems with their different vo- cabularies in a way which allows the user to navigate among them with relative ease. Recent results of the project have been the development of an Information Sources Map of biomedical databases, a Metathesaurus TM of biomedical vo- cabularies and a Semantic Network of high-level biomedical concepts[I,2]. The first release of the Information Sources Map contains a description of the scope, content, and ac- cess conditions for approximately fifty biomedical databases. The Metathesaurus includes over 67,000 biomedical concepts from a variety of controlled vocabularies. Definitions, lexical category information, hierarchical contexts, and interrela- tionships among many of the terms found in its constituent vocabularies are provided. Each concept in the Metathesaurus has been assigned to at least one of the 131 semantic types in the Semantic Network. The Network has top level nodes for organisms, anatomical structures, biologic function and dysfunction, chemicals, events, and concepts. The Network defines these types and establishes a set of 35 potential rela- tionships …

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