Abstract
In this work we tackle the link prediction task in knowledge graphs. Following recent success of Question Answering systems in outperforming humans, we employ the developed tools to identify and verify new links. To identify the gaps in a knowledge graph, we use the existing techniques and combine them with Question Answering tools to extract concealed knowledge. We outline the overall procedure and discuss preliminary results.
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CITATION STYLE
Khvalchik, M., Revenko, A., & Blaschke, C. (2019). Question Answering for Link Prediction and Verification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11762 LNCS, pp. 116–120). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32327-1_23
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