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To support biomedical experts in their knowledge discovery process, we have developed a literature mining method called RaJoLink for identification of relations between biomedical concepts in disconnected sets of articles. The method implements Swanson's ABC model approach for generating hypotheses in a new way. The main novelty is a semi-automated suggestion of candidates for agents a that might be logically connected with a given phenomenon c under investigation. The choice of candidates for a is based on rare terms identified in the literature on c. As rare terms are not part of the typical range of information, which describe the phenomenon under investigation, such information might be considered as unusual observations about the phenomenon c. If literatures on these rare terms have an interesting term in common, this joint term is declared as a candidate for a. Linking terms b between literature on a and literature on c are then searched for in the closed discovery to provide additional supportive evidence for uncovered connections. We have applied the method to the literature on autism and have used MEDLINE as a source of data. Expert evaluation has confirmed that the discovered relations might contribute to a better understanding of autism.

Abstract. This paper presents ways to use subgroup discovery to generate actionable knowledge for decision support. Actionable knowledge is explicit symbolic knowledge, typically presented in the form of rules, that allows the decision maker to recognize some important relations and to perform an appropriate action, such as targeting a direct marketing campaign, or planning a population screening campaign aimed at detecting individuals with high disease risk. Different subgroup discovery approaches are outlined, and their advantages over using standard classification rule learning are discussed. Three case studies, a medical and two marketing ones, are used to present the lessons learned in solving problems requiring actionable knowledge generation for decision support.

This paper investigates the role of outliers in literature-based knowledge discovery. It shows that detecting interesting outliers which appear in the literature on a given phenomenon can help the expert to find implicit relationships among concepts of different domains. The underlying assumption is that while the majority of articles in the given scientific domain describe matters related to a common understanding of the domain, the exploration of outliers may lead to the detection of scientifically interesting bridging concepts among disjoint sets of scientific articles. The proposed approach contributes to cross-context link discovery by proving the utility of outlier detection for finding bisociative links in the process of autism literature exploration, as well as by uncovering implicit relationships in the articles from the migraine domain.

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