Literature mining of blood flow regulatory mechanisms for finding cross context relations
Proceedings of the 12th International Conference on Computer Systems and Technologies
Evaluating outliers for cross-context link discovery
AIME'11 Proceedings of the 13th conference on Artificial intelligence in medicine
Bridging concept identification for constructing information networks from text documents
Bisociative Knowledge Discovery
Bisociative knowledge discovery by literature outlier detection
Bisociative Knowledge Discovery
Exploring the power of outliers for cross-domain literature mining
Bisociative Knowledge Discovery
Bisociative literature mining by ensemble heuristics
Bisociative Knowledge Discovery
Bisociative exploration of biological and financial literature using clustering
Bisociative Knowledge Discovery
| Hi-index | 0.00 |
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.