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

Building a Knowledge Graph from Natural Language Definitions for Interpretable Text Entailment Recognition Vivian S. Silva1, Andr´e Freitas2, Siegfried Handschuh1 1Department of Computer Science and Mathematics, University of Passau, Innstraße 43, 94032, Passau, Germany 2School of Computer Science, University of Manchester, Kilburn Building, Oxford Road, M13 9PL, UK vivian.santossilva@uni-passau.de, andre.freitas@manchester.ac.uk, siegfried.handschuh@uni-passau.de 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. Keywords: lexical definitions, knowledge graph, text entailment 1. Introduction Natural language lexical definitions of terms can be used as a source of knowledge in a number of semantic tasks, such as Question Answering, Information Extraction and Text Entailment. While formal, structured resources such as ontologies are still scarce and usually target a very spe- cific domain, a large number of linguistic resources gather- ing dictionary definitions is available not only for particular domains, but also addressing wide-coverage commonsense knowledge. However, in order to make the most of those resources, it is necessary to capture the semantic shape of natural language definitions and structure them in a way that favors both the information extraction process and the subsequent informa- tion retrieval, allowing the effective construction of seman- tic models from these data sources while keeping the result- ing model easily searchable and interpretable. Furthermore, by using these models, systems can increase their own in- terpretability, benefiting from the structured data for per- forming traceable reasoning and generating explanations – features which are becoming even more valuable given the growing importance of Explainable AI (Gunning, 2017). In this work, we propose a method for automatically build- ing commonsense knowledge bases out of natural language dictionary definitions, which is easily extensible to any domain where natural language glossaries are available. Building upon a conceptual model based on a set of seman- tic roles for definitions, we classify each segment in a def- inition according to its relation to the entity being defined, and convert the classified data into a knowledge graph where each node is a meaningful phrase which contains a piece of self-contained information about the definiendum. Following this methodology, we processed the whole noun and verb databases of WordNet (Fellbaum, 1998) and built the WordNetGraph, and then used this knowledge graph to recognize text entailments in an interpretable way, provid- ing concise justifications for the entailment decisions. 2. Related Work The construction …

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