Logical foundations of object-oriented and frame-based languages
Journal of the ACM (JACM)
Induction of recursive transfer rules
Learning language in logic
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic Extraction of Biological Information from Scientific Text: Protein-Protein Interactions
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Constructing Biological Knowledge Bases by Extracting Information from Text Sources
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Classifying semantic relations in bioscience texts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
RelEx---Relation extraction using dependency parse trees
Bioinformatics
Learning Recursive Patterns for Biomedical Information Extraction
Inductive Logic Programming
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Information Extraction as an Ontology Population Task and Its Application to Genic Interactions
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research
Unsupervised learning of semantic relations between concepts of a molecular biology ontology
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Comparative experiments on learning information extractors for proteins and their interactions
Artificial Intelligence in Medicine
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
A multilingual/multimedia lexicon model for ontologies
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
BioNLP Shared Task 2011: bacteria gene interactions and renaming
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
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We introduce an Information Extraction (IE) system which uses the logical theory of an ontology as a generalisation of the typical information extraction patterns to extract biological interactions from text. This provides inferences capabilities beyond current approaches: first, our system is able to handle multiple relations; second, it allows to handle dependencies between relations, by deriving new relations from the previously extracted ones, and using inference at a semantic level; third, it addresses recursive or mutually recursive rules. In this context, automatically acquiring the resources of an IE system becomes an ontology learning task: terms, synonyms, conceptual hierarchy, relational hierarchy, and the logical theory of the ontology have to be acquired. We focus on the last point, as learning the logical theory of an ontology, and a fortiori of a recursive one, remains a seldom studied problem. We validate our approach by using a relational learning algorithm, which handles recursion, to learn a recursive logical theory from a text corpus on the bacterium Bacillus subtilis. This theory achieves a good recall and precision for the ten defined semantic relations, reaching a global recall of 67.7% and a precision of 75.5%, but more importantly, it captures complex mutually recursive interactions which were implicitly encoded in the ontology.