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

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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.

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Notes

  1. Horn clauses consist of first-order clauses containing at most one positive literal.

  2. ACE (2004). Automatic Content Extraction. Relation Detection and Characterization 2004 Evaluation. http://www.itl.nist.gov/iad/mig/tests/ace/2004.

  3. In an ontology, TBox statements describe a system in terms of a controlled vocabulary, or a set of classes and properties, whereas ABox is the assertional component, i.e., TBox-compliant statements about that vocabulary.

  4. Aleph Manual. http://www.cs.ox.ac.uk/activities/machinelearning/Aleph/aleph.

  5. Stanford CoreNLP Tools. http://nlp.stanford.edu/software/corenlp.shtml.

  6. Apache OpenNLP. The Apache Software Foundation. http://opennlp.apache.org.

  7. We have also experimented with 4-grams, but bi-grams and tri-grams achieved better results in our preliminary experiments.

  8. ProGolem ILP system runs on the YAP Prolog (http://www.dcc.fc.up.pt/~vsc/Yap).

  9. http://cogcomp.cs.illinois.edu/Data/ER/conll04.corp.

  10. LIBSVM. A library for Support Vector Machines. https://www.csie.ntu.edu.tw/~cjlin/libsvm/.

  11. WordNet. A lexical database for English. https://wordnet.princeton.edu.

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Acknowledgements

The authors are grateful to Hilário Oliveira for his help in the development of some of the OntoILPER components. We also thank the National Council for Scientific and Technological Development (CNPq/Brazil) for financial support (Grant No. 140791/2010-8).

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Authors and Affiliations

  1. Department of Statistics and Informatics, Federal Rural University of Pernambuco, Recife, PE, Brazil

    Rinaldo Lima

  2. LSIS-UMR CNRS, Aix-Marseille University, Marseille, France

    Bernard Espinasse

  3. Informatics Center, Federal University of Pernambuco, Recife, PE, Brazil

    Fred Freitas

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  1. Rinaldo Lima
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  2. Bernard Espinasse
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  3. Fred Freitas
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Correspondence to Rinaldo Lima.

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Lima, R., Espinasse, B. & Freitas, F. OntoILPER: an ontology- and inductive logic programming-based system to extract entities and relations from text. Knowl Inf Syst 56, 223–255 (2018). https://doi.org/10.1007/s10115-017-1108-3

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