Abstract The reasoning ability of most existing dynamic knowledge map reasoning methods under the same time and multiple relationships is limited . Aiming at this problem, a method of dynamic knowledge graph inference based on multi-relational cyclic events(Multi-Net) is proposed. The improved multi-relational proximity aggregator is employed to fuse target entity neighborhood information to obtain more accurate representation of entity neighborhood vector, and Multi-Net is simplified by optimizing information fusion, and the ability to handle the conflict of relations between two entities in a specific scope is improved by adding the relationship prediction task to Multi-Net. Experiments of entity prediction and relationship prediction on large real datasets indicate that Multi-Net improves the reasoning ability of dynamic knowledge maps effectively.
Corresponding Authors:
LI Yongqiang, Ph.D., lecturer. His research interests include data-driven control, optimal control and natural language processing.
About author:: CHEN Hao, master student. His research interests include knowledge representation learning and knowledge graph reasoning..FENG Yuanjing, Ph.D., professor. His research interests include image processing, intelligent optimization and natural language processing.
[1] BOLLACKER K D, EVANS C, PARITOSH P, et al. Freebase: A Collaboratively Created Graph Database for Structuring Human Knowledge // Proc of the ACM SIGMOD International Conference on Management of Data. New York, USA: ACM, 2008: 1247-1250. [2] LEHMANN J, ISELE R, JAKOB M, et al. DBpedia-A Large-Scale, Multilingual Knowledge Base Extracted from Wikipedia. Semantic Web, 2015, 6(2): 167-195. [3] SUCHANEK F M, KASNECI G, WEIKUM G. YAGO: A Core of Semantic Knowledge // Proc of the 16th International Conference on World Wide Web. New York, USA: ACM, 2007: 697-706. [4] 官赛萍,靳小龙,贾岩涛,等.面向知识图谱的知识推理研究进展.软件学报, 2018, 29(10): 2966-2994. (GUAN S P, JIN X L, JIA Y T, et al. Knowledge Reasoning over Knowledge Graph: A Survey. Journal of Software, 2018, 29(10): 2966-2994.) [5] BORDES A, USUNIER N, GARCIA-DURAN A, et al. Translating Embeddings for Modeling Multi-relational Data // BURGES C J C, BOTTOU L, WELLING M, et al., eds. Advances in Neural Information Processing Systems 26. Cambridge, USA: The MIT Press, 2013: 2787-2795. [6] NICKEL M, TRESP V, KRIEGEL H P. A Three-Way Model for Collective Learning on Multi-relational Data // Proc of the 28th International Conference on Machine Learning. New York, USA: ACM, 2011: 809-816. [7] YANG B S, YIH W J, HE X D, et al. Embedding Entities and Relations for Learning and Inference in Knowledge Bases[J/OL]. [2019-11-20]. https://arxiv.org/pdf/1412.6575.pdf. [8] TROUILLON T, WELBL J, RIEDEL S, et al. Complex Embe-ddings for Simple Link Prediction // Proc of the 33rd International Conference on Machine Learning. New York, USA: ACM, 2016: 2071-2080. [9] LIU H X, WU Y X, YANG Y M. Analogical Inference for Multi-relational Embeddings[C/OL]. [2019-11-20]. http://proceedings.mlr.press/v70/liu17d/liu17d.pdf. [10] TRIVEDI R, DAI H J, WANG Y C, et al. Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs[C/OL]. [2019-11-20]. https://arxiv.org/pdf/1705.05742.pdf. [11] GARCÍA-DURÁN A, DUMANčIć S, NEIPERT M. Learning Sequence Encoders for Temporal Knowledge Graph Completion[C/OL]. [2019-11-20]. https://arxiv.org/pdf/1809.03202.pdf. [12] LEBLAY J, CHEKOL M W. Deriving Validity Time in Knowledge Graph // Proc of the International World Wide Web Conference. New York, USA: ACM, 2018: 1771-1776. [13] DASGUPTA S S, RAY S N, TALUKDAR P. HyTE: Hyperplane-Based Temporally Aware Knowledge Graph Embedding // Proc of the Conference on Empirical Methods in Natural Language Processing. Berlin, Germany: Springer, 2018: 2001-2011. [14] LIU J Q, ZHANG Q, FU L Y, et al. Evolving Knowledge Graphs // Proc of the IEEE Conference on Computer Communications. Wa- shington, USA: IEEE, 2019: 2260-2268. [15] JIN W, ZHANG C L, SZEKELY P, et al. Recurrent Event Network for Reasoning over Temporal Knowledge Graphs[C/OL]. [2019-11-20]. https://arxiv.org/pdf/1904.05530v1.pdf. [16] JIANG T S, LIU T Y,GE T, et al. Encoding Temporal Information for Time-Aware Link Prediction // Proc of the Conference on Empirical Methods in Natural Language Processing. Berlin, Germany: Springer, 2016: 2350-2354. [17] GRANGER C W J. Long Memory Relationships and the Aggregation of Dynamic Models. Journal of Econometrics, 1980, 14(2): 227-238. [18] SUNDERMEYER M, SCHLÜTER R, NEY H. LSTM Neural Networks for Language Modeling // Proc of the INTERSPEECH 2012. Berlin, Germany: Springer, 2012: 194-197. [19] BEALS E W. Bray-Curtis Ordination: An Effective Strategy for Analysis of Multivariate Ecological Data. Advances in Ecological Research, 1984, 14: 1-55. [20] WARD M D, BEGER A, CUTLER J, et al. Comparing GDELT and ICEWS Event Data. Analysis, 2013, 21(1): 267-297.
2020, Vol. 33 
