Two different approaches to Ontology Extension Through Machine Reading

Authors

  • Paulo Henrique Barch
  • Estevam Rafael Hruschka Jr

Keywords:

knowledge aquisition, ontology extension, machine reading, machine learning.

Abstract

NELL (Never Ending Language Learning system) is the first system to practice the Never-Ending Machine Learning paradigm techniques. It has an inactive component to continually extend its KB: OntExt. Its main idea is to identify and add to the KB new relations which are frequently asserted in huge text data. Co-occurrence matrices are used to structure the normalized values of co-occurrence between the contexts for each category pair to identify those context patterns. The clustering of each matrix is done with Weka K-means algorithm: from each cluster, a new possible relation. This work present newOntExt: a new approach with new features to turn the ontology extension task feasible to NELL. This approach has also an alternative task of naming new relations found by another NELL component: Prophet. The relations are classified as valid or invalid by humans; the precision is calculated for each experiment and the results are compared to those relative to OntExt. Initial results show that ontology extension with newOntExt can help Never-Ending Learning systems to expand its volume of beliefs and to keep learning with high precision by acting in auto-supervision and auto-reflection.

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Published

2015-01-01

How to Cite

Paulo Henrique Barch, & Estevam Rafael Hruschka Jr. (2015). Two different approaches to Ontology Extension Through Machine Reading. Journal of Network and Innovative Computing, 3, 10. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/92

Issue

Vol. 3 (2015)

Section

Original Article

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