{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T09:49:48Z","timestamp":1774000188664,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":22,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,12,12]]},"DOI":"10.1145\/3788149.3788223","type":"proceedings-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T06:35:19Z","timestamp":1773988519000},"page":"451-456","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Entity Similarity Matching Algorithm Based on Gain Coefficient and User Query Text"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6438-1483","authenticated-orcid":false,"given":"Zihan","family":"Zhou","sequence":"first","affiliation":[{"name":"Wuhan Research Institute of Posts and Telecommunications, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0959-3568","authenticated-orcid":false,"given":"Zhiyong","family":"Tao","sequence":"additional","affiliation":[{"name":"Wuhan Research Institute of Posts and Telecommunications, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3527-3599","authenticated-orcid":false,"given":"Zhixiang","family":"Yang","sequence":"additional","affiliation":[{"name":"CSSC LINGJIU HI-TECH(WUHAN) Co., Ltd., Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6967-4503","authenticated-orcid":false,"given":"YunKe","family":"Xiong","sequence":"additional","affiliation":[{"name":"CSSC LINGJIU HI-TECH(WUHAN) Co., Ltd., Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,19]]},"reference":[{"issue":"11","key":"e_1_3_3_1_1_2","first-page":"2887","article-title":"A Survey of Research Progress in Large Language Model Enhanced Knowledge Graph Question Answering[J]","volume":"18","author":"Feng Tuoyu","year":"2024","unstructured":"Feng Tuoyu, Li Weiping, Guo Qinglang, et al. A Survey of Research Progress in Large Language Model Enhanced Knowledge Graph Question Answering[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(11): 2887-2900.","journal-title":"Journal of Frontiers of Computer Science and Technology"},{"key":"e_1_3_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.27188\/d.cnki.gzjxu.2023.000058"},{"key":"e_1_3_3_1_3_2","first-page":"53","volume-title":"Proceedings of the 27th International Conference on Machine Learning (ICML'10)","author":"Lao N.","unstructured":"Lao, N. and Cohen, W.W. 2010. Relational retrieval using a combination of path-constrained random walks. In Proceedings of the 27th International Conference on Machine Learning (ICML'10). 53-67."},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2763164"},{"key":"e_1_3_3_1_5_2","unstructured":"Bordes A. Usunier N. Garcia-Duran A. Weston J. and Yakhnenko O. 2013. Translating embeddings for modeling multi-relational data. In Advances in Neural Information Processing Systems 26 (NIPS 2013). 2787-2795."},{"key":"e_1_3_3_1_6_2","unstructured":"Yang B. Yih W. He X. Gao J. and Deng L. 2014. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575."},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"publisher","unstructured":"Schlichtkrull M. Kipf T.N. Bloem P. van den Berg R. Titov I. and Welling M. 2018. Modeling relational data with graph convolutional networks. In The Semantic Web - 15th International Conference (ESWC 2018). 593-607. DOI:10.1007\/978-3-319-93417-4_38.","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"e_1_3_3_1_8_2","unstructured":"Veli\u010dkovi\u0107 P. Cucurull G. Casanova A. Romero A. Lio P. and Bengio Y. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903."},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1561\/1500000019"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_3_1_11_2","first-page":"2659","volume-title":"Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16)","author":"Xie R.","unstructured":"Xie, R., Liu, Z., Jia, J., Luan, H., and Sun, M. 2016. Representation learning of knowledge graphs with entity descriptions. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16). 2659-2665."},{"key":"e_1_3_3_1_12_2","volume-title":"BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.","author":"Devlin J.","year":"2018","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805."},{"key":"e_1_3_3_1_13_2","unstructured":"Guo Z. Xia L. Yu Y. et al. 2024. LightRAG: Simple and Fast Retrieval-Augmented Generation. arXiv preprint arXiv:2410.05779v3."},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2025.naacl-long.449"},{"key":"e_1_3_3_1_15_2","first-page":"1100","article-title":"2025. KAG: Boosting LLMs in Professional Domains via Knowledge-Augmented Generation","author":"Liang J.","year":"2025","unstructured":"Liang, J., Zhang, Y., Zhou, B., et al. 2025. KAG: Boosting LLMs in Professional Domains via Knowledge-Augmented Generation. In Findings of the Association for Computational Linguistics: ACL 2025. 1100-1115.","journal-title":"Findings of the Association for Computational Linguistics: ACL"},{"key":"e_1_3_3_1_16_2","volume-title":"Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively. arXiv preprint arXiv:2404.19705v2.","author":"Labruna T.","year":"2024","unstructured":"Labruna, T., Campos, J.A., and Azkune, G. 2024. When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively. arXiv preprint arXiv:2404.19705v2."},{"key":"e_1_3_3_1_17_2","unstructured":"Edge D. Trinh H. Cheng N. et al. 2024. From Local to Global: A Graph RAG Approach to Query-Focused Summarization. arXiv preprint arXiv:2404.16130."},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2006.141"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93503-4_46"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.5555\/1859664.1859686"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.19328\/j.cnki.2096-8655.2024.05.020"},{"key":"e_1_3_3_1_22_2","volume-title":"Semantic Text Similarity Computation Methods[J]","author":"Han Chengcheng","year":"2020","unstructured":"Han Chengcheng, Li Lei, Liu Tingting, et al. Semantic Text Similarity Computation Methods[J]. Journal of East China Normal University (Natural Science), 2020, (5): 95-112."}],"event":{"name":"CSAI 2025: 2025 The 9th International Conference on Computer Science and Artificial Intelligence","location":"Beijing China","acronym":"CSAI 2025"},"container-title":["Proceedings of the 2025 9th International Conference on Computer Science and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3788149.3788223","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T06:39:12Z","timestamp":1773988752000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3788149.3788223"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,12]]},"references-count":22,"alternative-id":["10.1145\/3788149.3788223","10.1145\/3788149"],"URL":"https:\/\/doi.org\/10.1145\/3788149.3788223","relation":{},"subject":[],"published":{"date-parts":[[2025,12,12]]},"assertion":[{"value":"2026-03-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}