Building a Knowledge Graph from Natural Language Definitions for Interpretable Text Entailment Recognition

Vivian S Silva, Andre Freitas, Siegfried Handschuh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Natural language definitions of terms can serve as a rich source of knowledge, but structuring them into a comprehensible semantic model is essential to enable them to be used in semantic interpretation tasks. We propose a method and provide a set of tools for automatically building a graph world knowledge base from natural language definitions. Adopting a conceptual model composed of a set of semantic roles for dictionary definitions, we trained a classifier for automatically labeling definitions, preparing the data to be later converted to a graph representation. WordNetGraph, a knowledge graph built out of noun and verb WordNet definitions according to this methodology, was successfully used in an interpretable text entailment recognition approach which uses paths in this graph to provide clear justifications for entailment decisions.
Original languageEnglish
Title of host publication11th Language Resources and Evaluation Conference
Publication statusAccepted/In press - 15 Dec 2017

Keywords

  • lexical definitions
  • knowledge graph
  • text entailment
  • interpretability

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