Modeling path information for knowledge graph completion

Author:

Shen YingORCID,Li Dagang,Nan Du

Funder

Shenzhen Technical Project

Young Scientists Fund

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference35 articles.

1. Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. In: Advances in neural information processing systems, pp. 2787–2795

2. Bühmann L, Lehmann J (2013) Pattern based knowledge base enrichment. In: International Semantic Web Conference. Springer, pp. 33–48

3. Das R, Dhuliawala S, Zaheer M, Vilnis L, Durugkar I, Krishnamurthy A, Smola A, McCallum A (2017) Go for a walk and arrive at the answer: reasoning over paths in knowledge bases using reinforcement learning. arXiv preprint arXiv:1711.05851

4. Das R, Neelakantan A, Belanger D, McCallum A (2016) Chains of reasoning over entities, relations, and text using recurrent neural networks. arXiv preprint arXiv:1607.01426

5. Deng Y, Li Y, Du N, Fan W, Shen Y, Lei K (2018) When truth discovery meets medical knowledge graph: estimating trustworthiness degree for medical knowledge condition. arXiv preprint arXiv:1809.10404

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1. Community Enhanced Knowledge Graph for Recommendation;IEEE Transactions on Computational Social Systems;2024

2. ASLEEP: A Shallow neural modEl for knowlEdge graph comPletion;Communications in Computer and Information Science;2023

3. RIECN: learning relation-based interactive embedding convolutional network for knowledge graph;Neural Computing and Applications;2022-12-20

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