Abstract
In this demonstration, we present Deep-gAnswer, a knowledge-based question answering system. gAnswer is based on semantic parsing and heuristic rules for entity recognition, relation recognition, and SPARQL generation. By making use of a pre-trained model, we implement new entity and relation recognition networks. Also, it is found that the traditional method works better when information of entity and relation is correctly given. Therefore, we combine entity and relation recognition networks with the previous SPARQL generation process to get Deep-gAnswer. Experimental results show that Deep-gAnswer outperforms the previous one, especially on Chinese dataset.
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Acknowledgment
This work was supported by National Natural Science Foundation of China(NSFC) under grant 61932001. The corresponding author of this work is Lei Zou.
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Lin, Y., Zhang, M., Zhang, R., Zou, L. (2021). Deep-gAnswer: A Knowledge Based Question Answering System. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds) Web and Big Data. APWeb-WAIM 2021. Lecture Notes in Computer Science(), vol 12859. Springer, Cham. https://doi.org/10.1007/978-3-030-85899-5_33
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DOI: https://doi.org/10.1007/978-3-030-85899-5_33
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