Outlier Detection in Cross-Context Link Discovery for Creative Literature Mining

Abstract

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.

Reducing the Search Space in Literature-Based Discovery by Exploring Outlier Documents: a Case Study in Finding Links Between Gut Microbiome and Alzheimer’s Disease

“…Results obtained by Sluban et al [10] and by Petrič et al [9] confirm the hypothesis that most bridging terms appear in outlier documents and that by considering only outlier documents the search space for b-term identification can be largely reduced. In this way, we can substantially reduce the search space for finding b-term candidates.…”

“…An example tool that we have developed in previous research is CrossBee [8], a web-based tool for bridging term (b-term) discovery and exploration, which implements an ensemble based term ranking approach to finding new connections between two predefined domains, represented by two user defined sets of biomedical articles. The research conducted by Petrič et al [9] and Sluban et al [10] complements this research by showing that bridging terms are substantially more frequent in documents that are outlier documents of their own domain, compared to their frequency in normal, non-outlier documents. Analogously to statistics, where an outlier is defined as an observation that falls outside the overall pattern of a distribution [11], an outlier document in the field of LBD is a document that lies outside the main group of documents of its own domain and is, therefore, in two domain settings more similar to the documents of the other explored domain than to the documents of the domain of its origin.…”

“…In the approach, proposed by Petrič et al [9], we concentrated on a specific type of outliers -the domain outliers -i.e. the documents that tend to be more similar to the documents of the opposite domain than to those of their own domain.…”

“…This well-known example has become the gold standard in the literature mining field and has been used as a benchmark in several studies, including our own work, developed by Juršič et al [8], Sluban et al [10] and Petrič et al [9], which is the basis for the methodology presented in this paper.…”

“…Instead of a single step outlier detection process, used in Petrič et al [9], we here use a two-step outlier detection process using OntoGen, illustrated in Figure 3 when applying OntoGen on the actual documents of this application.…”