OntoILPER: an ontology- and inductive logic programming-based system to extract entities and relations from text

Named entity recognition (NER) and relation extraction (RE) are two important subtasks in information extraction (IE). Most of the current learning methods for NER and RE rely on supervised machine learning techniques with more accurate results for NER than RE. This paper presents OntoILPER a system...

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Bibliographic Details
Published in:Knowledge and information systems 2018-07, Vol.56 (1), p.223-255
Main Authors: Lima, Rinaldo, Espinasse, Bernard, Freitas, Fred
Format: Article
Language:English
Subjects:
Artificial intelligence
Computation and Language
Computer Science
Data Mining and Knowledge Discovery
Database Management
Graphical representations
Information retrieval
Information Storage and Retrieval
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
IT in Business
Knowledge representation
Logic programming
Machine learning
Ontology
Regular Paper
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Summary:Named entity recognition (NER) and relation extraction (RE) are two important subtasks in information extraction (IE). Most of the current learning methods for NER and RE rely on supervised machine learning techniques with more accurate results for NER than RE. This paper presents OntoILPER a system for extracting entity and relation instances from unstructured texts using ontology and inductive logic programming, a symbolic machine learning technique. OntoILPER uses the domain ontology and takes advantage of a higher expressive relational hypothesis space for representing examples whose structure is relevant to IE. It induces extraction rules that subsume examples of entities and relation instances from a specific graph-based model of sentence representation. Furthermore, OntoILPER enables the exploitation of the domain ontology and further background knowledge in the form of relational features. To evaluate OntoILPER, several experiments over the TREC corpus for both NER and RE tasks were conducted and the yielded results demonstrate its effectiveness in both tasks. This paper also provides a comparative assessment among OntoILPER and other NER and RE systems, showing that OntoILPER is very competitive on NER and outperforms the selected systems on RE.
ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-017-1108-3