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.
This work has been partially funded by the project LYNX. The project LYNX has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 780602. More information is available online at http://www.lynx-project.eu.
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Khvalchik, M., Revenko, A., Blaschke, C. (2019). Question Answering for Link Prediction and Verification. In: Hitzler, P., et al. The Semantic Web: ESWC 2019 Satellite Events. ESWC 2019. Lecture Notes in Computer Science(), vol 11762. Springer, Cham. https://doi.org/10.1007/978-3-030-32327-1_23
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DOI: https://doi.org/10.1007/978-3-030-32327-1_23
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