计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (8): 1800-1808.DOI: 10.3778/j.issn.1673-9418.2104084
于慧琳, 陈炜, 王琪, 高建伟, 万怀宇
收稿日期:
2021-03-26
修回日期:
2021-05-31
出版日期:
2022-08-01
发布日期:
2021-06-03
作者简介:
于慧琳(1997—),女,黑龙江佳木斯人,硕士研究生,主要研究方向为知识图谱推理、信息抽取。基金资助:
YU Huilin, CHEN Wei, WANG Qi, GAO Jianwei, WAN Huaiyu
Received:
2021-03-26
Revised:
2021-05-31
Online:
2022-08-01
Published:
2021-06-03
About author:
YU Huilin, born in 1997, M.S. candidate. Her research interests include knowledge graph reasoning and information extraction.Supported by:
摘要:
知识图谱中的关系推理旨在从现有数据中识别和推断出新的关系,为许多下游任务提供知识服务。当前的许多研究工作主要将实体与关系映射到向量空间中或对实体之间的路径进行搜索来解决关系推理问题。这些方法都只考虑了单一路径或一阶信息对关系推理的影响,忽视了广泛存在于实体之间的更复杂的关系信息。提出了一种新颖的基于子图的知识图谱关系推理方法,结合表示学习与路径推理的优势,使用具有丰富信息的子图结构获取实体对的邻域结构信息,实现实体之间的关系预测。首先将实体对之间的路径扩展为子图,分别从实体层面和关系层面出发,构建节点子图和关系子图;再结合图嵌入表示与图神经网络计算子图的高阶特征,从而获得更丰富的实体关系特征;最后从子图高阶特征中获取实体对的邻域结构信息,实现实体之间的关系预测。实验结果表明,在两个基准数据集上,该方法优于现有的其他基于推理的关系预测方法。
中图分类号:
于慧琳, 陈炜, 王琪, 高建伟, 万怀宇. 使用子图推理实现知识图谱关系预测[J]. 计算机科学与探索, 2022, 16(8): 1800-1808.
YU Huilin, CHEN Wei, WANG Qi, GAO Jianwei, WAN Huaiyu. Knowledge Graph Link Prediction Based on Subgraph Reasoning[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(8): 1800-1808.
数据集 | #实体数 | #关系数 | #三元组 | #任务 |
---|---|---|---|---|
FB15K-237 | 14 505 | 237 | 310 116 | 10 |
NELL-995 | 75 492 | 200 | 154 213 | 10 |
表1 实验数据集
Table 1 Experimental datasets
数据集 | #实体数 | #关系数 | #三元组 | #任务 |
---|---|---|---|---|
FB15K-237 | 14 505 | 237 | 310 116 | 10 |
NELL-995 | 75 492 | 200 | 154 213 | 10 |
FB15K-237 | TransE | TransR | DeepPath | MINERVA | M-Walk | RLH | SubGLP |
---|---|---|---|---|---|---|---|
personNationality | 0.641 | 0.720 | 0.823 | 0.746 | 0.793 | 0.821 | 0.855 |
filmLanguage | 0.642 | 0.641 | 0.670 | 0.603 | 0.623 | 0.682 | 0.716 |
birthPlace | 0.403 | 0.417 | 0.531 | 0.641 | — | — | 0.710 |
filmDirector | 0.386 | 0.399 | 0.441 | 0.589 | 0.618 | 0.683 | 0.654 |
filmWrittenBy | 0.563 | 0.605 | 0.457 | 0.675 | 0.672 | 0.725 | 0.793 |
musicianOrigin | 0.361 | 0.378 | 0.514 | 0.534 | 0.583 | 0.568 | 0.605 |
tvLanguage | 0.804 | 0.906 | 0.969 | 0.860 | — | — | 0.988 |
capitalOf | 0.554 | 0.493 | 0.783 | 0.729 | 0.743 | 0.737 | 0.803 |
organizationFounded | 0.390 | 0.339 | 0.309 | 0.475 | — | — | 0.600 |
teamSports | 0.896 | 0.784 | 0.955 | 0.960 | — | — | 0.906 |
Average | 0.564 | 0.568 | 0.645 | 0.681 | 0.672 | 0.703 | 0.763 |
表2 FB15K-237数据集上的关系推理(MAP)实验结果
Table 2 Link prediction results (MAP) on FB15K-237 datasets
FB15K-237 | TransE | TransR | DeepPath | MINERVA | M-Walk | RLH | SubGLP |
---|---|---|---|---|---|---|---|
personNationality | 0.641 | 0.720 | 0.823 | 0.746 | 0.793 | 0.821 | 0.855 |
filmLanguage | 0.642 | 0.641 | 0.670 | 0.603 | 0.623 | 0.682 | 0.716 |
birthPlace | 0.403 | 0.417 | 0.531 | 0.641 | — | — | 0.710 |
filmDirector | 0.386 | 0.399 | 0.441 | 0.589 | 0.618 | 0.683 | 0.654 |
filmWrittenBy | 0.563 | 0.605 | 0.457 | 0.675 | 0.672 | 0.725 | 0.793 |
musicianOrigin | 0.361 | 0.378 | 0.514 | 0.534 | 0.583 | 0.568 | 0.605 |
tvLanguage | 0.804 | 0.906 | 0.969 | 0.860 | — | — | 0.988 |
capitalOf | 0.554 | 0.493 | 0.783 | 0.729 | 0.743 | 0.737 | 0.803 |
organizationFounded | 0.390 | 0.339 | 0.309 | 0.475 | — | — | 0.600 |
teamSports | 0.896 | 0.784 | 0.955 | 0.960 | — | — | 0.906 |
Average | 0.564 | 0.568 | 0.645 | 0.681 | 0.672 | 0.703 | 0.763 |
NELL-995 | TransE | TransR | DeepPath | MINERVA | M-Walk | RLH | SubGLP |
---|---|---|---|---|---|---|---|
athleteHomeStadium | 0.718 | 0.722 | 0.846 | 0.928 | 0.919 | 0.934 | 0.962 |
bornLocation | 0.712 | 0.812 | 0.755 | 0.782 | 0.842 | 0.873 | 0.929 |
athletePlaysSport | 0.876 | 0.963 | 0.917 | 0.986 | 0.983 | 0.974 | 0.906 |
teamPlaySports | 0.761 | 0.814 | 0.696 | 0.875 | 0.884 | 0.891 | 0.876 |
orgHeadquaterCity | 0.620 | 0.657 | 0.790 | 0.945 | 0.950 | 0.936 | 0.915 |
worksFor | 0.677 | 0.692 | 0.699 | 0.827 | 0.827 | 0.826 | 0.836 |
athletePlaysForTeam | 0.627 | 0.673 | 0.721 | 0.826 | 0.847 | 0.869 | 0.949 |
athletePlaysInLeague | 0.773 | 0.912 | 0.960 | 0.952 | 0.978 | 0.946 | 0.958 |
personLeadsOrg | 0.751 | 0.772 | 0.790 | 0.830 | 0.812 | 0.814 | 0.936 |
orgHiredPerson | 0.719 | 0.737 | 0.738 | 0.870 | 0.888 | 0.895 | 0.866 |
Average | 0.723 | 0.775 | 0.791 | 0.882 | 0.893 | 0.896 | 0.913 |
表3 NELL-995数据集上的关系推理(MAP)实验结果
Table 3 Link prediction results (MAP) on NELL-995 datasets
NELL-995 | TransE | TransR | DeepPath | MINERVA | M-Walk | RLH | SubGLP |
---|---|---|---|---|---|---|---|
athleteHomeStadium | 0.718 | 0.722 | 0.846 | 0.928 | 0.919 | 0.934 | 0.962 |
bornLocation | 0.712 | 0.812 | 0.755 | 0.782 | 0.842 | 0.873 | 0.929 |
athletePlaysSport | 0.876 | 0.963 | 0.917 | 0.986 | 0.983 | 0.974 | 0.906 |
teamPlaySports | 0.761 | 0.814 | 0.696 | 0.875 | 0.884 | 0.891 | 0.876 |
orgHeadquaterCity | 0.620 | 0.657 | 0.790 | 0.945 | 0.950 | 0.936 | 0.915 |
worksFor | 0.677 | 0.692 | 0.699 | 0.827 | 0.827 | 0.826 | 0.836 |
athletePlaysForTeam | 0.627 | 0.673 | 0.721 | 0.826 | 0.847 | 0.869 | 0.949 |
athletePlaysInLeague | 0.773 | 0.912 | 0.960 | 0.952 | 0.978 | 0.946 | 0.958 |
personLeadsOrg | 0.751 | 0.772 | 0.790 | 0.830 | 0.812 | 0.814 | 0.936 |
orgHiredPerson | 0.719 | 0.737 | 0.738 | 0.870 | 0.888 | 0.895 | 0.866 |
Average | 0.723 | 0.775 | 0.791 | 0.882 | 0.893 | 0.896 | 0.913 |
FB15K-237 | SubGLP-nod | SubGLP-edg | SubGLP | NELL-995 | SubGLP-nod | SubGLP-edg | SubGLP |
---|---|---|---|---|---|---|---|
birthPlace | 0.708 | 0.679 | 0.710 | athletePlaysInLeague | 0.941 | 0.939 | 0.958 |
personNationality | 0.842 | 0.836 | 0.855 | athleteHomeStadium | 0.952 | 0.953 | 0.962 |
filmDirector | 0.639 | 0.627 | 0.654 | athletePlaysSport | 0.957 | 0.936 | 0.906 |
filmWrittenBy | 0.803 | 0.801 | 0.793 | teamPlaySports | 0.862 | 0.870 | 0.876 |
filmLanguage | 0.706 | 0.702 | 0.716 | orgHeadquaterCity | 0.913 | 0.906 | 0.915 |
tvLanguage | 0.978 | 0.975 | 0.988 | worksFor | 0.835 | 0.830 | 0.836 |
teamSports | 0.896 | 0.897 | 0.906 | athletePlaysForTeam | 0.957 | 0.882 | 0.949 |
capitalOf | 0.773 | 0.759 | 0.803 | bornLocation | 0.898 | 0.920 | 0.929 |
organizationFounded | 0.524 | 0.523 | 0.600 | personLeadsOrg | 0.934 | 0.931 | 0.936 |
musicianOrigin | 0.575 | 0.554 | 0.605 | orgHiredPerson | 0.860 | 0.825 | 0.866 |
Average | 0.744 | 0.735 | 0.763 | Average | 0.911 | 0.899 | 0.913 |
表4 消融实验结果
Table 4 Ablation experiment results
FB15K-237 | SubGLP-nod | SubGLP-edg | SubGLP | NELL-995 | SubGLP-nod | SubGLP-edg | SubGLP |
---|---|---|---|---|---|---|---|
birthPlace | 0.708 | 0.679 | 0.710 | athletePlaysInLeague | 0.941 | 0.939 | 0.958 |
personNationality | 0.842 | 0.836 | 0.855 | athleteHomeStadium | 0.952 | 0.953 | 0.962 |
filmDirector | 0.639 | 0.627 | 0.654 | athletePlaysSport | 0.957 | 0.936 | 0.906 |
filmWrittenBy | 0.803 | 0.801 | 0.793 | teamPlaySports | 0.862 | 0.870 | 0.876 |
filmLanguage | 0.706 | 0.702 | 0.716 | orgHeadquaterCity | 0.913 | 0.906 | 0.915 |
tvLanguage | 0.978 | 0.975 | 0.988 | worksFor | 0.835 | 0.830 | 0.836 |
teamSports | 0.896 | 0.897 | 0.906 | athletePlaysForTeam | 0.957 | 0.882 | 0.949 |
capitalOf | 0.773 | 0.759 | 0.803 | bornLocation | 0.898 | 0.920 | 0.929 |
organizationFounded | 0.524 | 0.523 | 0.600 | personLeadsOrg | 0.934 | 0.931 | 0.936 |
musicianOrigin | 0.575 | 0.554 | 0.605 | orgHiredPerson | 0.860 | 0.825 | 0.866 |
Average | 0.744 | 0.735 | 0.763 | Average | 0.911 | 0.899 | 0.913 |
[1] | CHEN X, JIA S, XIANG Y. A review: knowledge reasoning over knowledge graph[J]. Expert Systems with Applications, 2020, 141: 112948. |
[2] | HU S, ZOU L, YU J X, et al. Answering natural language questions by subgraph matching over knowledge graphs[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 30: 824-837. |
[3] | PALUMBO E, RIZZO G, TRONCY R.Entity2rec: learning user-item relatedness from knowledge graphs for Top-N item recommendation[C]// Proceedings of the 11th ACM Conference on Recommender Systems, Como, Aug 27-31, 2017. New York: ACM, 2017: 32-36. |
[4] | CORCOGLIONITI F, DRAGONI M, ROSPOCHER M, et al. Knowledge extraction for information retrieval[C]// Procee-dings of the 2016 European Semantic Web Conference, Crete, May 29-Jun 2, 2016. Cham:Springer, 2016: 317-333. |
[5] | BORDES A, USUNIER N, GARCIA-DURAN A, et al. Tran-slating embeddings for modeling multi-relational data[C]// Proceedings of the 2013 Neural Information Processing Sys-tems, Lake Tahoe, Dec 5-8, 2013. Piscataway: IEEE, 2013: 2787-2795. |
[6] | LIN Y K, LIU Z Y, SUN M S, et al. Learning entity and relation embeddings for knowledge graph completion[C]// Proceedings of the 29th AAAI Conference on Artificial In-telligence, Austin, Jan 25-30, 2015. Menlo Park: AAAI, 2015: 2181-2187. |
[7] | DAS R, NEELAKANTAN A, BELANGER D, et al. Chains of reasoning over entities, relations, and text using recurrent neural networks[C]// Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Apr 3- 7, 2017. Stroudsburg: ACL, 2017: 132-141. |
[8] | NEELAKANTAN A, ROTH B, MCCALLUM A. Composi-tional vector space models for knowledge base completion[C]// Proceedings of the 53rd Annual Meeting of the Asso-ciation for Computational Linguistics and the 7th Interna-tional Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Jul 26-31, 2015. Stroudsburg: ACL, 2015: 156-166. |
[9] | XIONG W, HOANG T, WANG W Y. DeepPath: a reinfor-cement learning method for knowledge graph reasoning[C]// Proceedings of the 2017 Conference on Empirical Me-thods in Natural Language Processing, Copenhagen, Sep 9-11, 2017. Stroudsburg: ACL, 2017: 564-573. |
[10] | DAS R, DHULIAWALA S, ZAHEER M, et al. Go for a walk and arrive at the answer: reasoning over paths in knowledge bases using reinforcement learning[J]. |
[11] | MORRIS C, RITZERT M, FEY M, et al. Weisfeiler and Leman go neural: higher-order graph neural networks[C]// Proceedings of the 33rd AAAI Conference on Artificial Intelligence, the 31st Innovative Applications of Artificial Intelligence Conference, the 9th AAAI Symposium on Edu-cational Advances in Artificial Intelligence, Honolulu, Jan 27 - Feb 1, 2019. Menlo Park: AAAI, 2019: 4602-4609. |
[12] | SCHOENMACKERS S, DAVIS J, ETZIONI O, et al. Lear-ning first-order horn clauses from Web text[C]// Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Oct 9-11, 2010. Stroudsburg: ACL, 2010: 1088-1098. |
[13] | SCHULTE O, QIAN Z, KIRKPATRICK A E, et al. Fast learning of relational kernels[J]. Machine Learning, 2010, 78(3): 305-342. |
[14] | WANG Z, ZHANG J W, FENG J L, et al. Knowledge graph embedding by translating on hyperplanes[C]// Proceedings of the 28th AAAI Conference on Artificial Intelligence, Québec City, Jul 27-31, 2014. Menlo Park: AAAI, 2014: 1112-1119. |
[15] | JI G, HE S, XU L, et al. Knowledge graph embedding via dynamic mapping matrix[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Lan-guage Processing, Beijing, Jul 26-31, 2015. Stroudsburg: ACL, 2015: 687-696. |
[16] | HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. |
[17] | LI Z, JIN X, GUAN S, et al. Path reasoning over knowledge graph: a multi-agent and reinforcement learning based me-thod[C]// Proceedings of the 2018 IEEE International Confer-ence on Data Mining, Singapore, Nov 17-20, 2018. Piscata-way: IEEE, 2018: 929-936. |
[18] | SHEN Y L, CHEN J S, HUANG P, et al. M-walk: learning to walk over graphs using Monte Carlo tree search[C]// Advances in Neural Information Processing Systems 31, Montréal, Dec 3-8, 2018: 6787-6798. |
[19] | WAN G J, PAN S R, GONG C, et al. Reasoning like human: hierarchical reinforcement learning for knowledge graph reasoning[C]// Proceedings of the 29th International Joint Conference on Artificial Intelligence, Yokohama, 2021: 1926-1932. |
[20] | ZHOU J, CUI G, ZHANG Z, et al. Graph neural networks: a review of methods and applications[J]. AI Open, 2020, 1: 57-81. |
[21] | DOUGLAS B L. The Weisfeiler-Lehman method and graph isomorphism testing[J]. |
[22] | GAO H Y, JI S W. Graph U-Nets[C]// Proceedings of the 36th International Conference on Machine Learning, Long Beach, Jun 9-15, 2019: 2083-2092. |
[23] | SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Drop-out: a simple way to prevent neural networks from over-fitting[J]. Journal of Machine Learning Research, 2014, 15(1): 1929-1958. |
[24] | POWLEY E. Monte Carlo tree search[M]. Berlin, Heidelberg: Springer, 2012. |
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