Constructing Personal Knowledge Graph from Conversation via Deep Reinforcement Learning
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3623-2_16
Reference23 articles.
1. Ji, S., Pan, S., Cambria, E., Marttinen, P., Philip, S.Y.: A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans. Neural Netw. Learn. Syst. 33(2), 494–514 (2021)
2. Balog, K., Kenter, T.: Personal knowledge graphs: a research agenda. In: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval, Santa Clara, CA, USA, pp. 217–220. ACM (2019). https://doi.org/10.1145/3341981.3344241
3. Hao, X., et al.: Construction and application of a knowledge graph. Remote Sens. 13(13), 2511 (2021)
4. Xiao, X.: Knowledge graph technology based on natural language processing and reinforcement learning e-commerce customer service. In: 2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS), Dalian, China, pp. 495–500. IEEE (2022). https://doi.org/10.1109/TOCS56154.2022.10015978
5. Ji, H., Ke, P., Huang, S., Wei, F., Zhu, X., Huang, M.: Language generation with multi-hop reasoning on commonsense knowledge graph, arXiv preprint arXiv:2009.11692 (2020)
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