2011 | OriginalPaper | Buchkapitel
Evaluating Outliers for Cross-Context Link Discovery
verfasst von : Borut Sluban, Matjaž Juršič, Bojan Cestnik, Nada Lavrač
Erschienen in: Artificial Intelligence in Medicine
Verlag: Springer Berlin Heidelberg
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In literature-based creative knowledge discovery the goal is to identify interesting terms or concepts which relate different domains. We propose to support this cross-context link discovery process by inspecting outlier documents which are not in the mainstream domain literature. We have explored the utility of outlier documents, discovered by combining three classification-based outlier detection methods, in terms of their potential for bridging concept discovery in the migraine-magnesium cross-domain discovery problem and in the autism-calcineurin domain pair. Experimental results prove that outlier documents present a small fraction of a domain pair dataset that is rich on concept bridging terms. Therefore, by exploring only a small subset of documents, where a great majority of bridging terms are present and more frequent, the effort needed for finding cross-domain links can be substantially reduced.