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Chapitre D'ouvrage Année : 2022

Knowledge Graph Augmentation for Increased Question Answering Accuracy

Jorge Martinez-Gil
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Shaoyi Yin

Résumé

This research work presents a new augmentation model for knowledge graphs (KGs) that increases the accuracy of knowledge graph question answering (KGQA) systems. In the current situation, large KGs can represent millions of facts. However, the many nuances of human language mean that the answer to a given question cannot be found, or it is not possible to find always correct results. Frequently, this problem occurs because how the question is formulated does not fit with the information represented in the KG. Therefore, KGQA systems need to be improved to address this problem. We present a suite of augmentation techniques so that a wide variety of KGs can be automatically augmented, thus increasing the chances of finding the correct answer to a question. The first results from an extensive empirical study seem to be promising.
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Dates et versions

hal-03842359 , version 1 (07-11-2022)

Identifiants

Citer

Jorge Martinez-Gil, Shaoyi Yin, Josef Küng, Franck Morvan. Knowledge Graph Augmentation for Increased Question Answering Accuracy. Abdelkader Hameurlain; A Min Tjoa. Transactions on Large-Scale Data- and Knowledge-Centered Systems LII, 13470, Springer, pp.70-85, 2022, Lecture Notes in Computer Science book series (LNCS), 978-3-662-66145-1. ⟨10.1007/978-3-662-66146-8_3⟩. ⟨hal-03842359⟩
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