title: Using ontology network structure in text mining.
abstract: Statistical text mining treats documents as bags of words, with a focus on term frequencies within documents and across document collections. Unlike natural language processing (NLP) techniques that rely on an engineered vocabulary or a full-featured ontology, statistical approaches do not make use of domain-specific knowledge. The freedom from biases can be an advantage, but at the cost of ignoring potentially valuable knowledge. The approach proposed here investigates a hybrid strategy based on computing graph measures of term importance over an entire ontology and injecting the measures into the statistical text mining process. As a starting point, we adapt existing search engine algorithms such as PageRank and HITS to determine term importance within an ontology graph. The graph-theoretic approach is evaluated using a smoking data set from the i2b2 National Center for Biomedical Computing, cast as a simple binary classification task for categorizing smoking-related documents, demonstrating consistent improvements in accuracy.
authors.affiliation: Consortium for Health Informatics Research (CHIR), HSR&D/RR&D Center of Excellence: Maximizing Rehabilitation Outcomes, Tampa, FL.
authors.forename: Donald J
authors.initials: DJ
authors.lastname: Berndt
authors.forename: James A
authors.initials: JA
authors.lastname: McCart
authors.forename: Stephen L
authors.initials: SL
authors.lastname: Luther
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining; D006801:Humans; D009323:Natural Language Processing; D018875:Vocabulary, Controlled