title: Discovering relations between indirectly connected biomedical concepts.
abstract: BACKGROUND
  The complexity and scale of the knowledge in the biomedical domain has motivated research work towards mining heterogeneous data from both structured and unstructured knowledge bases. Towards this direction, it is necessary to combine facts in order to formulate hypotheses or draw conclusions about the domain concepts. This work addresses this problem by using indirect knowledge connecting two concepts in a knowledge graph to discover hidden relations between them. The graph represents concepts as vertices and relations as edges, stemming from structured (ontologies) and unstructured (textual) data. In this graph, path patterns, i.e. sequences of relations, are mined using distant supervision that potentially characterize a biomedical relation.
  
  
  RESULTS
  It is possible to identify characteristic path patterns of biomedical relations from this representation using machine learning. For experimental evaluation two frequent biomedical relations, namely "has target", and "may treat", are chosen. Results suggest that relation discovery using indirect knowledge is possible, with an AUC that can reach up to 0.8, a result which is a great improvement compared to the random classification, and which shows that good predictions can be prioritized by following the suggested approach.
  
  
  CONCLUSIONS
  Analysis of the results indicates that the models can successfully learn expressive path patterns for the examined relations. Furthermore, this work demonstrates that the constructed graph allows for the easy integration of heterogeneous information and discovery of indirect connections between biomedical concepts.
authors.affiliation: DFKI Projektbüro Berlin, Alt-Moabit 91c, Berlin, 10559 Germany ; Biotechnology Center, Technische Universität Dresden, Tatzberg 47/49, Dresden, 01307 Germany.
authors.forename: Dirk
authors.initials: D
authors.lastname: Weissenborn
authors.affiliation: Biotechnology Center, Technische Universität Dresden, Tatzberg 47/49, Dresden, 01307 Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Schroeder
authors.affiliation: Biotechnology Center, Technische Universität Dresden, Tatzberg 47/49, Dresden, 01307 Germany.
authors.forename: George
authors.initials: G
authors.lastname: Tsatsaronis
keywords: Biomedical concepts; Relation discovery; Text mining