Computer Science ›› 2021, Vol. 48 ›› Issue (5): 190-196.doi: 10.11896/jsjkx.200500023
• Artificial Intelligence • Previous Articles Next Articles
YANG Ru-han, DAI Yi-ru, WANG Jian, DONG Jin
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[1]WANG B C,ZANG J Y,QU X M,et al.Research on New-Generation Intelligent Manufacturing based on Human-Cyber-Physical Systems [J].Engineering Sciences,2018,20(4):29-34. [2]MAEDCHE A,STAAB S.Ontology Learning for the Semantic Web[J].Intelligent Systems IEEE,2001,16(2):72-79. [3]BORDES A,USUNIER N,GARCIA-DURAN A,et al.Translating embeddings for modeling multi-relational data[C]//Proceedings of International conference on Neural Information Processing Systems.Lake Tahoe:Curran Associates Inc,2013:2787-2795. [4]WANG Z,ZHANG J,FENG J,et al.Knowledge graph embedding by translating onhyperplanes [C]//Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence.Menlo Park,CA:AAAI Press,2014:1112-1119. [5]KAMIL S,GRZEGORZ D.Grounding of Human Observations as Uncertain Knowledge[C]//Proceedings of the 8th international conference on Computational Science.Berlin,Heidelberg:Springer-Verlag,2008:555-563. [6]XIONG S,LIU P Z,SU Z Y,et al.Research on the Geographic Information Ontology Fusion Method Basedon Mapping of Semantic Matching[J].Geomatics Science and Engineering,2017(1):51-58. [7]NATALYA F,MUSEN M A.The PROMPT suite:interactive tools for ontology merging and mapping[J].International Journal of Human-Computer Studies,2003,59(6):983-1024. [8]DOAN A,MADHAVAN J,DHAMANKAR R,et al.Learning to Map Ontologies on the Semantic Web[J].The VLDB Journal,2003,12:303-319. [9]QU Y Z,HU W,CHENG G.Constructing Virtual Documents for Ontology Matching[C]//Proceedings of the 15th International Conference on World Wide Web.Edinburgh:Association for Computing Machinery,2006:23-31. [10]ZHANG Y,ZHANG F,YAO P,et al.Name Disambiguation in AMiner:Clustering,Maintenance,and Human in the Loop[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.London:Association for Computing Machinery,2018:1002-1011. [11]BENGI O,YOSHU A,COURVILL E,et al.RepresentationLearning:A Review and New Perspectives[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2013,35(8):1798-1828. [12]LIU Z Y,SUN M S,LIN Y K,et al.Knowledge Representation Learning:A ReView[J].Journal of Computer Research and Development,2016,53(2):247-261. [13]BORDES A,WESTON J,COLLOBERT R,et al.Learningstructured embeddings of knowledge bases[C]//Proceedings of the 25th AAAI Conference on Artificial Intelligence.San Francisco:AAAI Press,2011:301-306. [14]BORDES A,GLOROT X,WESTON J,et al.A semantic ma-tching energy function for learning with multi-relational data[J].Machine Learning,2014,94 (2):233-259. [15]SOCHER R,CHEN D,MANNING C D,et al.Reasoning with neural tensor networks for knowledge base completion[C]//Proceedings of International conference on Neural Information Processing Systems.New York:Curran Associates Inc,2013:926-934. [16]LIN Y,LIU Z,SUN M,et al.Learning entity and relation embeddings for knowledge graph completion[C]//Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence.Texas:AAAI Press,2015:2181-2187. [17]JI G,HE S,XU L,et al.Knowledge graph embedding via dynamic mapping matrix[C]//Proceedings of ACL.2015:687-696. [18]XIAO H,HUANG M,HAO Y,et al.TransA:An adaptive ap-proach for knowledge graph embedding[J].arXiv:1509.05490,2015. [19]XIAO H,HUANG M,HAO Y,et al.TransG:A generativemixture model for knowledge graph embedding[J].arXiv:1509.05488,2015. [20]XIE R,LIU Z,JIA J,et al.Representation learning of knowledge graphs with entity descriptions[C]//Proceedings of 30th AAAI Conference on Artificial Intelligence.Phoenix:AAAI Press,2016:2659-2665. [21]XIE R,LIU Z,SUN M.Representation Learning of Knowledge Graphs with Hierarchical Types[C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.New York:AAAI Press,2016:2965-2971. [22]TANG X,CHEN L,CUI J,et al.Knowledge representationlearning with entity descriptions,hierarchical types,and textual relations[J].Information Processing & Management,2019,56(3):809-822. [23]LIN Y,LIU Z,SUN M.Knowledge Representation Learningwith Entities,Attributes and Relations[C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.Menlo Park:AAAI Press,2016:2866-2872. [24]ZHANG Z,CAO L,CHEN X,et al.Representation Learning of Knowledge Graphs With Entity Attributes[J].IEEE Access,2020,8:7435-7441. [25]LIN Y,LIU Z,SUN M.Modeling Relation Paths for Representation Learning of Knowledge Bases [J].arXiv:1506.00379,2015. [26]SEO S,OH B,LEE K H.Reliable Knowledge Graph Path Representation Learning[J].IEEE Access,2020,8:32816-32825. [27]SUN Z,HU W,LI C.Cross-lingual entity alignment via joint attribute-preserving embedding[C]//Proceedings of the 16th Int Semantic Web Conference.Cham:Springer International Publishing,2017:628-644. |
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