EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 42 / No 1 (February 2024) JNWPU, 42 1 (2024) 157-164 References
Open Access
Issue
JNWPU
Volume 42, Number 1, February 2024
Page(s) 157 - 164
DOI https://doi.org/10.1051/jnwpu/20244210157
Published online 29 March 2024
  1. XIONG C, POWER R, CALLAN J. Explicit semantic ranking for academic search via knowledge graph embedding[C]//Proceedings of the International Conference on World Wide Web, Perth, 2017: 1271–1279 [Google Scholar]
  2. CAO Y, WANG X, HE X, et al. Unifying knowledge graph learning and recommendation: towards a better understanding of user preferences[C]//Proceedings of the 28th International Conference on World Wide Web, San Francisco, 2019: 151–161 [Google Scholar]
  3. HUANG X, ZHANG J, LI D, et al. Knowledge graph embedding based question answering[C]//Proceedings of the 12th ACM International Conference on Web Search and Data Mining, Melbourne, 2019: 105–113 [Google Scholar]
  4. XU Zenglin, SHENG Yongpan, HE Lirong, et al. Review on knowledge graph techniques[J]. Journal of University of Electronic Science and Technology of China, 2016, 45(4): 589–606. [Article] (in Chinese) [Google Scholar]
  5. ZHUANG Yan, LI Guoliang, FENG Jianhua. A survey on entity alignment of knowledge base[J]. Journal of Computer Research and Development, 2016, 53(1): 165–192. [Article] (in Chinese) [Google Scholar]
  6. BORDES A, USUNIER N, GARCIADURAN A, et al. Translating embeddings for modeling multi-relational data[J]. Advances in Neural Information Processing Systems, 2013, 26(1): 2787–2795 [Google Scholar]
  7. CHEN M, TIAN Y, YANG M, et al. Multilingual knowledge graph embeddings for cross-lingual knowledge alignment[C]//Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, 2017: 1511–1517 [Google Scholar]
  8. ZHU H, XIE R, LIU Z, et al. Iterative entity alignment via joint knowledge embeddings[C]//Proceedings of the Twenty-sixth International Joint Conference on Artificial Intelligence, Melbourne, 2017: 4258–4264 [Google Scholar]
  9. KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[C]//Proceedings of International Conference on Learning Representations, Toulon, 2017: 1–14 [Google Scholar]
  10. WANG Z, LV Q, LAN X, et al. Cross-lingual knowledge graph alignment via graph convolutional networks[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, 2018: 349–357 [Google Scholar]
  11. WU Y, LIU X, FENG Y, et al. Relation-aware entity alignment for heterogeneous knowledge graphs [C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, 2019: 5278–5284 [Google Scholar]
  12. WU Y, LIU X, FENG Y, et al. Neighborhood matching network for entity alignment[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Seattle, 2020: 6477–6487 [Google Scholar]
  13. ZHU Y, LIU H, WU Z, et al. Relation-aware neighborhood matching model for entity alignment[C]//Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, 2021: 4749–4756 [Google Scholar]
  14. SUN Z, HU W, LI C. Cross-lingual entity alignment via joint attribute preserving embedding[C]//Proceedings of the 16th International Semantic Web Conference, Cham, 2017: 628–644 [Google Scholar]
  15. SUN Z, HU W, ZHANG Q, et al. Bootstrapping entity alignment with knowledge graph embedding[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence, Stockholm, 2018: 4396–4402 [Google Scholar]
  16. MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[J]. Advances in Neural Information Processing Systems, 2013, 26(1): 3111–3119 [Google Scholar]
  17. SRIVASTAVA R K, GREFF K, SCHMIDHUBER J. Training very deep networks[J]. Advances in Neural Information Processing Systems, 2015, 28(1): 2377–2385 [Google Scholar]
  18. SCHLICHTKRULL M, KIPF T N, BLOEM P, et al. Modeling relational data with graph convolutional networks[C]//European Semantic Web Conference, Cham, 2018: 593–607 [Google Scholar]
  19. VELIKOVI P, CUCURULL G, CASANOVA A, et al. Graph attention networks[C]//International Conference of Learning Representation, Vancouver, 2018: 1–12 [Google Scholar]
  20. SUN Z, WANG C, HU W, et al. Knowledge graph alignment network with gated multi-hop neighborhood aggregation[C]//Proceedings of AAAI Conference on Artificial Intelligence, Palo Alto, 2020: 222–229 [Google Scholar]
  21. TRISEDYA B D, QI J, ZHANG R. Entity alignment between knowledge graphs using attribute embeddings[C]//Proceedings of the 2019 AAAI Conference on Artificial Intelligence, Honolulu, 2019: 297–304 [Google Scholar]
  22. ZHANG Q, SUN Z, HU W, et al. Multi-view knowledge graph embedding for entity alignment[C]//The 28th International Joint Conference on Artificial Intelligence, Macao, 2019: 5429–5435 [Google Scholar]
  23. YANG H W, ZOU Y, SHI P, et al. Aligning cross-lingual entities with multi-aspect information[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, Hong Kong, 2019: 4431–4441 [Google Scholar]
  24. PENNINGTON J, SOCHER R, MANNING C D. Glove: global vectors for word representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, 2014: 1532–1543 [Google Scholar]
  25. XU K, WANG L, YU M, et al. Cross-lingual knowledge graph alignment via graph matching neural network[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Firenze, 2019: 3156–3161 [Google Scholar]
  26. RAHIMI A, COHN T, BALDWIN T. Semi-supervised user geolocation via graph convolutional networks[C]//Proceedings of the Annual Meeting of the Association for Computational Linguistics, Melbourne, 2018: 2009–2019 [Google Scholar]
  27. CHEN M, TIAN Y, YANG M, et al. Multilingual knowledge graph embeddings for cross-lingual knowledge alignment[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, 2017: 1511–1517 [Google Scholar]
  28. CAO Y, LIU Z, LI C, et al. Multi-channel graph neural network for entity alignment[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Firenze, 2019: 1452–1461 [Google Scholar]
  29. MAO X, WANG W, XU H, et al. MRAEA: an efficient and robust entity alignment approach for cross-lingual knowledge graph[C]//Proceedings of the 13th ACM International Conference on Web Search and Data Mining, Houston, 2020: 420–428 [Google Scholar]
  30. ZENG W, ZHAO X, WANG W, et al. Degree-aware alignment for entities in tail[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Xi'an, 2020: 811–820 [Google Scholar]
  31. WU Y, LIU X, FENG Y, et al. Jointly learning entity and relation representations for entity alignment[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, Hong Kong, 2019: 240–249 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.