A Knowledge-based Approach for Scenario-specific Content Correlation in a Medical Digital Library
نویسندگان
چکیده
Manually searching the web for medical literature and teaching materials is labor-intensive and time-consuming. Content Correlation automatically creates semantic links among documents from different collections. As a result, navigating from a patient report to online medical documents is much easier for the user. In this paper, we present a knowledge-based (e.g. UMLS) approach for content correlation. First, we index each medical document using phrases (a combination of word stems and concepts). We illustrate how phrase-based indexing greatly ameliorates the problem of vocabulary mismatch among multiple document collections. Second, we use a phrase-indexed patient report to automatically form a query, and expand the query with scenario-specific phrases derived from a knowledge base. Experimental results reveal that the phrase-based indexing and the knowledge-based query expansion together, yield scenario-specific content correlation.
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