[1] 徐增林,盛泳潘,贺丽荣,等.知识图谱技术综述[J].电子科技大学学报, 2016, 45(04): 589-606.
[2] 漆桂林, 高桓, 吴天星. 知识图谱研究进展[J]. 情报工程, 2017, 3(1): 4-25.
[3] 刘峤,李杨,段宏,等. 知识图谱构建技术综述[J].计算机研究与发展, 2016, 53(03): 582-600.
[4] 阮彤,王梦婕,王昊奋,等.垂直知识图谱的构建与应用研究[J].知识管理论坛, 2016,1(03): 226-234.
[5] Peters M, Neumann M, Iyyer M, et al. Deep contextualized word representations[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Illinois, USA, 2018: 102-114.
[6] Guan N, Song D, Liao L. Knowledge graph embedding with concepts[J]. Knowledge Based Systems, 2019, 164: 38-44.
[7] Arkhipov M, Trofimova M, Kuratov Y, et al. Tuning multilingual transformers for language-specific named entity recognition[C]//Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing. Chicago, USA, 2019: 18-28.
[8] Giuliano C, Lavelli A, Romano L. Relation extraction and the influence of automatic named-entity recognition[J]. ACM Transaction on Speech and Language Processing (TSLP), 2007, 5(1): 1-26.
[9] 陈钰枫,宗成庆,苏克毅. 汉英双语命名实体识别与对齐的交互式方法[J].计算机学报, 2011, 34(9): 1688-1696.
[10] 秦兵,刘安安,刘挺.无指导的中文开放式实体关系抽取[J]. 计算机研究与发展,2015, 52(5): 1029-1035.
[11] 陈宇,郑德权,赵铁军. 基于Deep Belief Nets 的中文名实体关系抽取[J]. 计算机学报, 2012, 23(10): 2572-2585.
[12] 甘丽新,万常选,刘德喜,等.基于句法语义特征的中文实体关系抽取[J].计算机研究与发展, 2016,53(2): 284-302.
[13] Bilgic M, Licamele L, Getoor L, et al. D-Dupe: An interactive tool for entity resolution in social networks[C]//Proceedings of the 13th International Symposium on Graph Drawing. Limerick, Ireland, 2006:43-50.
[14] Li L, Wang H,Gao H, et al. EIF: A framework of effective entity identification[C]//Proceedings of the 11th International Conference of Web-Age Information Management. Jiuzhaigou, China, 2010: 717-728.
[15] Li P F, Zhu Q M, Zhou G D. Using compositional semantics and discourse consistency to improve Chinese trigger identification[J]. Information Processing & Management, 2014, 50(2): 399-415.
[16] Liu H, Jang C, Hu C, et al. Efficient relation extraction method based on spatial feature using ELM[J]. Neural Computing and Applications, 2014,12(30): 1-11.
[17] Zhou H, Young T, Huang M, et al. Commonsense knowledge aware conversation generation with graph attention[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 2018: 110-118.
[18] Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva,et al. Variational reasoning for question answering with knowledge graph[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence, Louisiana, USA, 2018: 324-333.
[19] Qingheng Zheng, Zequn Sun, Wei Hu, et al. Multi-view knowledge graph embedding for entity alignment[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, 2019: 78-88.
[20] 刘峤, 钟云, 李杨, 等.基于图的中文集成实体链接算法[J].计算机研究与发展, 2016,53(2): 270-283.
[21] 怀宝兴,宝腾飞,祝恒书, 等.一种基于概率主题模型的命名实体链接方法[J].软件学报, 2014, 25(9): 2076-2087.
[22] Dong X L, Gabrilovich E, Heitz G, et al. From data fusion to knowledge fusion[J]. VLDB Endowment, 2014, 7(10): 881-892.
[23] Li J, Tang J, Li Y, et al. RiMOM: Adynamic multistrategy ontology alignment framework[J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(8): 1218-1232.
[24] Duan S, Fokoue A, Hassanzadeh O, et al. Instance-based matching of large ontologies using locality-sensitive hashing[C]//Proceedings of the International Conference on the Semantic Web, Berlin, Germany, 2012: 49-64.
[25] 刘雪莉,王宏志,李建中,等.基于实体的相似性连接算法[J].软件学报, 2015, 26(6): 1421-1437.
[26] 张涛, 刘康, 赵军.一种基于图模型的维基概念相似度计算方法及其实体链接系统中的应用[J].中文信息学报, 2015, 29(2): 58-67.
[27] 刘峤, 钟云, 刘瑶, 等.基于语义一致性的集成实体链接算法[J].计算机研究与发展, 2016, 53(8): 1696-1708.
[28] 庄严,李国良,冯建华.知识库实体对齐技术综述[J].计算机研究与发展, 2016, 53(1): 165-192.
[29] 王雪鹏,刘康,何世柱, 等.基于网络语义标签的多源知识库实体对齐算法[J].计算机学报,2017, 40(3): 701-711.
[30] Chen Z, Kalashnikov D V, Mehrotra S. Exploiting context analysis for combining multiple entity resolution systems[C]//Proceeding of the 2009 ACM SIGMOD International Conference on Management of Data. New York: ACM,2009: 207-218.
[31] Xin Lv, Lei Hou, Juanzi Li, et al. Differentiating concepts and instances for knowledge graph embedding[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018: 1971-1979.
[32] Zhang W, Paudel B, Zhang W, et al. Interaction Embeddings for prediction and explanation in knowledge graphs[C]//Proceedings of the International Conference on Web Search and Data Mining (WSDM), Melbourne, Australia, 2019: 123-133.
[33] 刘峤, 韩明皓,杨晓慧, 等.基于表示学习和语义要素感知的关系推理算法[J].计算机研究与发展, 2017, 54(08): 1682-1692.
[34] Lao Ni, Mitchell T, Cohen W. Random walk inference and learning in a large scale knowledge base[C]//Proceeding of the 2011 Conference on Empirical Methods in Natural Language Proceeding. Stroudsburg, PA: ACL, 2011: 529-539.
[35] Lin Yankai, Liu Zhiyuan, Luan Hanbo, et al. Modeling relation paths for representation learning of knowledge bases[C]//Proceeding of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: ACL, 2015: 705-714.
[36] 刘峤,韩明皓,江浏祎. 基于双层随机游走的关系推理算法[J].计算机学报,2017, 40(6): 1275-1290.