NLTK: the Natural Language Toolkit
ETMTNLP '02 Proceedings of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics - Volume 1
Document Visualization Based on Semantic Graphs
IV '09 Proceedings of the 2009 13th International Conference Information Visualisation
Towards biopathway modeling and simulation
ICATPN'03 Proceedings of the 24th international conference on Applications and theory of Petri nets
Developing a robust part-of-speech tagger for biomedical text
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Link discovery in graphs derived from biological databases
DILS'06 Proceedings of the Third international conference on Data Integration in the Life Sciences
Towards creative information exploration based on koestler's concept of bisociation
Bisociative Knowledge Discovery
Biomine: a network-structured resource of biological entities for link prediction
Bisociative Knowledge Discovery
Applications and evaluation: overview
Bisociative Knowledge Discovery
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The chapter proposes an approach to support modelling of plant defence response to pathogen attacks. Such models are currently built manually from expert knowledge, experimental results, and literature search, which is a very time consuming process. Manual model construction can be effectively complemented by automated model extraction from biological literature. This work focuses on the construction of triplets in the form of subject-predicate-object extracted from scientific papers, which are used by the Biomine automated graph construction and visualisation engine to create the biological model. The approach was evaluated by comparing the automatically generated graph with a manually developed Petri net model of plant defence. This approach to automated model creation was explored also in a bisociative setting. The emphasis is not on creative knowledge discovery, but rather on specifying and crossing the boundaries of knowledge of individual scientists. This could be used to model the expertise of virtual scientific consortia.