Abstract
In this paper, we conduct an empirical investigation of neural query graph ranking approaches for the task of complex question answering over knowledge graphs. We propose a novel self-attention based slot matching model which exploits the inherent structure of query graphs, our logical form of choice. Our proposed model generally outperforms other ranking models on two QA datasets over the DBpedia knowledge graph, evaluated in different settings. We also show that domain adaption and pre-trained language model based transfer learning yield improvements, effectively offsetting the general lack of training data.
G. Maheshwari, P. Trivedi, D. Lukovnikov and N. Chakraborty—These four authors contributed equally.
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Notes
- 1.
Entity that has been linked in the question.
- 2.
That is, we use the first 70% of dataset, as made available on https://figshare.com/projects/LC-QuAD/21812 by [20], to train our models. Next 10% is used to decide the best hyperparamters. The metrics we report in the rest of this section are based on the model’s performance on the last 20% of it.
- 3.
as provided by the authors at https://github.com/google-research/bert.
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Acknowledgements
This work has been supported by the Fraunhofer-Cluster of Excellence “Cognitive Internet Technologies” (CCIT).
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Maheshwari, G., Trivedi, P., Lukovnikov, D., Chakraborty, N., Fischer, A., Lehmann, J. (2019). Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11778. Springer, Cham. https://doi.org/10.1007/978-3-030-30793-6_28
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