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

Abstract

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.