GETT-QA: Graph Embedding Based T2T Transformer for Knowledge Graph Question Answering

Author:

Banerjee Debayan,Nair Pranav Ajit,Usbeck Ricardo,Biemann Chris

Publisher

Springer Nature Switzerland

Reference45 articles.

1. Abujabal, A., Roy, R.S., Yahya, M., Weikum, G.: Never-ending learning for open-domain question answering over knowledge bases. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018, Lyon, France, pp. 1053–1062. ACM (2018). https://doi.org/10.1145/3178876.3186004

2. Banerjee, D., Nair, P., Kaur, J.N., Usbeck, R., Biemann, C.: Modern baselines for SPARQL semantic parsing. In: SIGIR 2022: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid. ACM (2022). https://arxiv.org/abs/2204.12793

3. Bhutani, N., Zheng, X., Jagadish, H.V.: Learning to answer complex questions over knowledge bases with query composition. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, pp. 739–748. ACM (2019). https://doi.org/10.1145/3357384.3358033

4. Bordes, A., Usunier, N., Chopra, S., Weston, J.: Large-scale simple question answering with memory networks. CoRR abs/1506.02075 (2015). https://dblp.uni-trier.de/db/journals/corr/corr1506.html#BordesUCW15

5. Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013, Lake Tahoe, Nevada, United States, pp. 2787–2795 (2013). https://proceedings.neurips.cc/paper/2013/hash/1cecc7a77928ca8133fa24680a88d2f9-Abstract.html

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