Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
An algorithm for suffix stripping
Readings in information retrieval
Relational learning of pattern-match rules for information extraction
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
An introduction to inductive logic programming and learning language in logic
Learning language in logic
Learning for semantic interpretation: scaling up without dumbing down
Learning language in logic
Learning for text categorization and information extraction with ILP
Learning language in logic
Machine Learning
Relational Data Mining
Constructing Biological Knowledge Bases by Extracting Information from Text Sources
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Learning Logic Models for Automated Text Categorization
AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Toward general-purpose learning for information extraction
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Learning Recursive Theories in the Normal ILP Setting
Fundamenta Informaticae
Extraction of genic interactions with the recursive logical theory of an ontology
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
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Information in text form remains a greatly unexploited source of biological information. Information Extraction (IE) techniques are necessary to map this information into structured representations that allow facts relating domain-relevant entities to be automatically recognized. In biomedical IE tasks, extracting patterns that model implicit relations among entities is particularly important since biological systems intrinsically involve interactions among several entities. In this paper, we resort to an Inductive Logic Programming (ILP) approach for the discovery of mutual recursive patterns from text. Mutual recursion allows dependencies among entities to be explored in data and extraction models to be applied in a context-sensitive mode. In particular, IE models are discovered in form of classification rules encoding the conditions to fill a pre-defined information template. An application to a real-world dataset composed by publications selected to support biologists in the task of automatic annotation of a genomic database is reported.