Machine learning: applications in expert systems and information retrieval
Machine learning: applications in expert systems and information retrieval
Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A vector space model for automatic indexing
Communications of the ACM
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In this paper we present a suite of tools to automatically acquire and browse conceptual schemas from large collections of HTML-based biomedical documents. This suite is composed of two tools: the schema acquisition tool (SAT) and the zoomable browser (ZB). The SAT is the implementation of a novel four-phased method to extract conceptual schemas from non-structured sources. First, all documents in the collection are analyzed to extract relevant concepts. Second, the vocabulary discovered during the first phase is organized into a hierarchical structure. Third, the schema is enriched with non-hierarchical ad-hoc relationships. The last phase is an optional refinement activity that must be conducted by experts in the domain covered by the collection. The extracted schemas can be navigated using the ZB. We have used these tools for different purposes in the EC funded biomedical research project Advancing Clinico-Genomic Trials on Cancer (ACGT), obtaining promising results.