Abstract
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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Acknowledgements
The authors thank the anonymous reviewers for their help in improving the work. This work has been supported by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry of Science, Research and Economy, and the State of Upper Austria through the COMET center SCCH. And by the project FR06/2020 - International Cooperation & Mobility (ICM) of the Austrian Agency for International Cooperation in Education and Research (OeAD-GmbH). We would also thank ‘the French Ministry of Foreign and European Affairs’ and ‘The French Ministry of Higher Education and Research’ which support the Amadeus program 2020 (French-Austrian Hubert Curien Partnership - PHC) Project Number 44086TD.
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Martinez-Gil, J., Yin, S., Küng, J., Morvan, F. (2022). Knowledge Graph Augmentation for Increased Question Answering Accuracy. In: Hameurlain, A., Tjoa, A.M. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems LII. Lecture Notes in Computer Science(), vol 13470. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-66146-8_3
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