KGIPSL: A Knowledge Graph Inference Method based on Probabilistic Soft Logic

doi:10.23940/ijpe.19.12.p12.32093218

Abstract

Abstract: Knowledge graph inference has a wide range of applications in semantic search, question answering systems, entity disambiguation, link prediction, and recommendation systems. However, the accuracy and operational efficiency of existing methods do not meet the needs of large-scale knowledge graphs. Aiming at the problem of large-scale knowledge graph inference, this paper proposes a knowledge graph inference method based on probabilistic soft logic (KGIPSL). Firstly, KGIPSL uses the Markov logic network to construct the relationship between entities. Secondly, KGIPSL employs probabilistic soft logic to represent non-deterministic knowledge and infers the relationship between entities in the knowledge graph. Thirdly, KGIPSL conducts accurate knowledge inference. Experiments on real knowledge graph datasets show that the KGIPSL method is superior to the existing baseline method in accuracy, recall, and efficiency. Among them, the average accuracy of KGIPSL on the YAGO dataset is 14.9% higher than that of the baseline method.

Key words: knowledge graph inference, probabilistic soft logic, Markov logic network

Yaqiong Qiao, Yanjun Wang, Jiangtao Ma, Xiangyang Luo, and Huaiguang Wu. KGIPSL: A Knowledge Graph Inference Method based on Probabilistic Soft Logic [J]. Int J Performability Eng, 2019, 15(12): 3209-3218.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

[1] R. West, E. Gabrilovich, K. Murphy, S. Sun, R. Gupta,D. Lin, “Knowledge Base Completion via Search-based Question Answering,” inProceedings of the 23rd International Conference on World Wide Web (WWW), pp. 515-526, Seoul, Korea, April 2014
[2] S. Muggleton and L. D. Raedt, “Inductive Logic Programming: Theory and Methods,” Journal of Logic Programming, Vol. 19, pp. 629-679, May 1994
[3] J. R. Quinlan, “Learning Logical Definitions from Relations,” Machine Learning, Vol. 5, No. 3, pp. 239-266, August 1990
[4] L. A. Galárraga, C. Teflioudi, K. Hose,F. Suchanek, “AMIE: Association Rule Mining under Incomplete Evidence in Ontological Knowledge Bases,” inProceedings of the 22nd International Conference on World Wide Web (WWW), pp. 413-422, Rio de Janeiro, Brazil, May 2013
[5] N. Lao, T. Mitchell,W. W. Cohen, “Random Walk Inference and Learning in a Large Scale Knowledge Base,” inProceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 529-539, Edinburgh, United Kingdom, July 2011
[6] M. Gardner and T. M. Mitchell, “Efficient and Expressive Knowledge Base Completion using Subgraph Feature Extraction,” inProceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1488-1498, Lisbon, Portugal, September 2015
[7] A. Kimmig, S. H. Bach, M. Broecheler, B. Huang,L. Getoor, “A Short Introduction to Probabilistic Soft Logic,” inProceedings of the NIPS Workshop on Probabilistic Programming: Foundations and Applications, pp. 1-4, Nevada, United States, December 2012
[8] M. Nickel, V. Tresp,H. -P. Kriegel, “Factorizing Yago: Scalable Machine Learning for Linked Data,” inProceedings of the 21st International Conference on World Wide Web (WWW), pp. 271-280, Lyon, France, April 2012
[9] A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston,O. Yakhnenko, “Translating Embeddings for Modeling Multi-Relational Data,”Advances in Neural Information Processing Systems 26, pp. 2787-2795, Nevada, United States, December 2013
[10] M. Nickel, L. Rosasco,T. Poggio, “Holographic Embeddings of Knowledge Graphs,” inProceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp. 1955-1961, Phoenix, United States, February 2016
[11] S. Bhatia, A. Goel,A. Jain, “Separating Wheat from the Chaff - A Relationship Ranking Algorithm,” inProceedings of European Semantic Web Conference (ESWC), pp. 79-83, Heraklion, Crete, Greece, May 2016
[12] O. Kuželka and J. Davis, “Markov Logic Networks for Knowledge Base Completion: A Theoretical Analysis under the MCAR Assumption,” in Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 427-437,Tel Aviv, Israel, July, 2019
[13] S. H. Bach, M. Broecheler, B. Huang,L. Getoor, “Hinge-Loss Markov Random Fields and Probabilistic Soft Logic,” Journal of Machine Learning Research, Vol. 18, pp.109:1-109:67, 2017
[14] J. Minker, “On Indefinite Databases and the Closed World Assumption,” inProceedings of the 6th Conference on Automated Deduction, pp. 292-308, New York, United States, June 1982
[15] N. Drummond and R. Shearer, “The Open World Assumption,” eSI Workshop: The Closed World of Databases Meets the Open World of the Semantic Web, Vol. 15, pp. 1-23, October 2006
[16] X. Dong, E. Gabrilovich, G. Heitz, W. Horn, N. Lao, K. Murphy, et al., “Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion,” inProceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 601-610, New York, United States, August 2014
[17] A. Nath and M. Richardson, “Counting-MLNs: Learning Relational Structure for Decision Making,” inProceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, pp. 1068-1074, Toronto, Canada, July 2012
[18] N. Lao and W. W. Cohen, “Relational Retrieval using a Combination of Path-Constrained Random Walks,” Machine Learning, Vol. 81, No. 1, pp. 53-67, October 2010
[19] M. Gardner, P. P. Talukdar, J. Krishnamurthy,T. Mitchell, “Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases,” inProceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 397-406, Doha, Qatar, October 2014