Abstract
Relation detection is a core component for Knowledge Base Question Answering (KBQA). In this paper, we propose a knowledge base (KB) relation detection model based on multi-view matching, which utilizes useful information extracted from questions and KB. The matching inside each view is through multiple perspectives to compare two input texts thoroughly. All these components are trained in an end-to-end neural network model. Experiments on SimpleQuestions and WebQSP yield state-of-the-art results on relation detection.
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- 1.
We empirically found that 500 instances were sufficient for our entity type extraction experiment..
- 2.
In Freebase, the relation type.object.type lists the types for an entity.
- 3.
See Wang et al. [13] for details.
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Yu, Y., Hasan, K.S., Yu, M., Zhang, W., Wang, Z. (2018). Knowledge Base Relation Detection via Multi-View Matching. In: Benczúr, A., et al. New Trends in Databases and Information Systems. ADBIS 2018. Communications in Computer and Information Science, vol 909. Springer, Cham. https://doi.org/10.1007/978-3-030-00063-9_27
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DOI: https://doi.org/10.1007/978-3-030-00063-9_27
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