融合关系路径与实体邻域信息的知识图谱补全方法

doi:10.3778/j.issn.1002-8331.2303-0369

摘要/Abstract

摘要： 知识图谱为许多人工智能应用提供了底层的技术支持，包括电子商务、智能导航、医疗保健、社交媒体等领域。但现有的知识图谱通常是不完整的，大量的知识隐含在其中，因此如何将知识图谱补全完整成为亟需解决的问题。现有方法大多是独立处理实体邻域信息或关系路径，忽略了实体邻域信息对关系路径探索过程的重要性。为此，提出了一种融合关系路径与实体邻域信息的知识图谱补全方法（RPEN-KGC）。RPEN-KGC由采样器和推理器构成，采样器通过在实体对之间随机游走，为推理器提供可靠的推理策略。同时采样器利用实体邻域相似性对比机制约束随机游走的方向，有效提高采样的效率，并且使推理策略更加丰富。推理器通过提取关系路径的语义特征，在语义空间中推理出更加多样化的关系路径。在公开的NELL-995和FB15K-237数据集中通过链接预测任务进行实验验证，结果表明，RPEN-KGC在多数指标上相较于基准方法均有一定的提升，说明RPEN-KGC能够有效预测知识图谱中缺失的知识。

关键词: 知识图谱, 知识图谱补全, 生成对抗网络, 多跳推理

Abstract: Knowledge graph provides the underlying technical support for many AI applications, including e-commerce, smart navigation, healthcare, social media, and more. However, the existing knowledge graph is usually sparse, and a large amount of hidden knowledge has not been mined, so how to complete the knowledge map has become an urgent problem to be solved. Most of the existing methods process entity neighborhood information or relationship paths independently, ignoring the importance of entity neighborhood information to the relationship path exploration process. Therefore, a knowledge graph completion method （RPEN-KGC） is proposed to fuse relational path and entity neighborhood information. RPEN-KGC consists of a sampler and an inference. The sampler provides an expert path for the inferent by randomly walking between pairs of entities, and at the same time restricts the direction of random walk with the entity neighborhood similarity comparison mechanism to enrich the expert path. By extracting the semantic features in the relationship path, the inferent can infer more diverse relationship paths in the semantic space. Experimental verification is carried out on the publicly available NELL-995 and FB15K-237 datasets by link prediction task. The experimental results show that RPEN-KGC is improved compared with the baseline method in most indicators, indicating that RPEN-KGC can effectively predict the missing knowledge in the knowledge graph.

Key words: knowledge graph, knowledge graph completion, generative adversarial network, multi-hop reasoning

翟社平, 亢鑫年, 李方怡, 杨锐. 融合关系路径与实体邻域信息的知识图谱补全方法[J]. 计算机工程与应用, 2024, 60(13): 136-142.

ZHAI Sheping, KANG Xinnian, LI Fangyi, YANG Rui. Incorporating Relation Path and Entity Neighborhood Information for Knowledge Graph Completion Method[J]. Computer Engineering and Applications, 2024, 60(13): 136-142.

参考文献

[1] 张天成, 田雪, 孙相会, 等. 知识图谱嵌入技术研究综述[J]. 软件学报, 2023, 34(1): 277-311.
ZHANG T C, TIAN X, SUN X H, et al. Overview on knowledge graph embedding technology research[J]. Journal of Software, 2023, 34(1): 277-311.
[2] 周毅, 刘峥, 粟小青, 等. 融合多层次数据的问答知识图谱本体模型构建[J]. 图书情报工作, 2022, 66(5): 125-132.
ZHOU Y, LIU Z, SU X Q, et al. Ontology model construction of question-answering knowledge graph integrating multi-level data[J]. Library and Information Service, 2022, 66(5): 125-132.
[3] 李贺, 刘嘉宇, 李世钰, 等. 基于疾病知识图谱的自动问答系统优化研究[J]. 数据分析与知识发现, 2021, 5(5): 115-126.
LI H, LIU J Y, LI S Y, et al. Optimizing automatic question answering system based on disease knowledge graph[J]. Data Analysis and Knowledge Discovery, 2021, 5(5): 115-126.
[4] LIU W F, LIU J P, LIU B, et al. Survey on construction of code knowledge graph and intelligent software development[J]. Journal of Software, 2019, 31(1): 47-66.
[5] 余传明, 王峰, 安璐. 基于深度学习的领域知识对齐模型研究: 知识图谱视角[J]. 情报学报, 2019, 38(6): 641-654.
YU C M, WANG F, AN L. Research on the domain knowledge alignment model based on deep learning: the knowledge graph perspective[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(6): 641-654.
[6] YIN G, CHEN F, DONG Y, et al. Knowledge-aware recommendation model with dynamic co-attention and attribute regularize[J]. Applied Intelligence, 2022, 52(4): 3807-3824.
[7] LEHMANN J, ISELE R, JAKOB M, et al. DBpedia a large-scale, multilingual knowledge base extracted from wikipedia[J]. Semantic Web, 2015, 6(2): 167-195.
[8] BOLLACKER K D, EVANS C, PARITOSH P K, et al. Freebase: a collaboratively created graph database for structuring human knowledge[C]//Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, Vancouver, Jun 10-12, 2008. New York: ACM, 2008: 1247-1250.
[9] VRANDE?I? D, KR?TZSCH M. Wikidata: a free collaborative knowledgebase[J]. Communications of the ACM, 2014, 57(10): 78-85.
[10] MILLER G A. WordNet: a lexical database for English[J]. Communications of the ACM, 1995, 38(11): 39-41.
[11] WEST R, GABRILOVICH E, MURPHY K, et al. Knowledge base completion via search-based question answering[C]//Proceedings of the 23rd International Conference on World Wide Web, Seoul, Apr 7-11, 2014. New York: ACM, 2014: 515-526.
[12] CHEN Z, WANG Y, ZHAO B, et al. Knowledge graph completion: a review[J]. IEEE Access, 2020, 8(8): 192435-192456.
[13] 张贞港, 余传明. 基于实体与关系融合的知识图谱补全模型研究[J]. 数据分析与知识发现, 2023, 7(2): 15-25.
ZHANG Z G, YU C M. Research on the entity and relation fusion-based knowledge graph completion model[J]. Data Analysis and Knowledge Discovery, 2023, 7(2): 15-25.
[14] BORDES A, USUNIER N, GARCIA-DURAN A, et al. Translating embeddings for modeling multi-relational data[C]//Proceedings of the Annual Conference on Neural Information Processing Systems 2013, Lake Tahoe, Dec 5-8, 2013. Red Hook: Curran Associates, 2013: 2787-2795.
[15] 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.
[16] LIN Y, LIU Z, SUN M, et al. Learning entity and relation embeddings for knowledge graph completion[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence, Austin, Jan 25-30, 2015. Menlo Park: AAAI, 2015: 2181-2187.
[17] JI G L, HE S Z, XU L H, 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 Language Processing of the Asian Federation of Natural Language Processing, Beijing, Jul 26-31, 2015. Menlo Park: AAAI, 2015: 687-696.
[18] NICKEL M, TRESP V, KRIEGEL H P. A three-way model for collective learning on multi-relational data[C]//Proceedings of the 28th International Conference on Machine Learning, Bellevue, Jun 28-Jul 2, 2011. Madison: Omni Press, 2011: 809-816.
[19] YANG B S, YIH W T, HE X D, et al. Embedding entities and relations for learning and inference in knowledge bases[J]. arXiv:1412.6575, 2014.
[20] TROUILLON T, WELBL J, RIEDEL S, et al. Complex embeddings for simple link prediction[C]//Proceedings of the 33rd International Conference on Machine Learning, New York, Jun 20-22, 2016. New York: ICML, 2016: 2071-2080.
[21] DETTMERS T, MINERVINI P, STENETORP P, et al. Convolutional 2D knowledge graph embeddings[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence, the 30th Innovative Applications of Artificial Intelligence, and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence, New Orleans, Feb 2-7, 2018. Menlo Park: AAAI, 2018: 1811-1818.
[22] NGUYEN D Q, NGUYEN T D, NGUYEN D Q, et al. A novel embedding model for knowledge base completion based on convolutional neural network[C]//Proceedings of the North American Chapter of the Association for Computational Linguistics, New Orleans, Jun 1-6, 2018. PA: ACL, 2018: 327-333.
[23] NEELAKANTAN A, ROTH B, MCCALLUM A. Compositional vector space models for knowledge base completion[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, Jul 26-31, 2015. PA: ACL, 2015: 156-166.
[24] 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. PA: ACL, 2017: 132-141.
[25] LAO N, COHEN W W. Relational retrieval using a combination of path-constrained random walks[J]. Machine Learning, 2010, 81(1): 53-67.
[26] LIN Y, LIU Z, LUAN H, et al. Modeling relation paths for representation learning of knowledge bases[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Sep 17-21, 2015. PA: ACL, 2015: 705-714.
[27] XIONG W, HOANG T, WANG W Y. DeepPath: a reinforcement learning method for knowledge graph reasoning[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Sep 9-11, 2017. PA: ACL, 2017: 564-573.
[28] 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]. arXiv:1711.05851, 2017.
[29] SHEN Y L, CHEN J S, HUANG P S, et al. M-walk: learning to walk over graphs using Monte Carlo tree search[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montréal, Dec 2-8, 2018. La Jolla: MIT Press, 2018: 6787-6798.
[30] LI R, CHENG X. DIVINE: a generative adversarial imitation learning framework for knowledge graph reasoning[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China, Nov 3-7, 2019. PA: ACL, 2019: 2642-2651.
[31] JIANG X, WANG Q, QI B, et al. Attentive path combination for knowledge graph completion[C]//Proceedings of the Ninth Asian Conference on Machine Learning, Seoul, Nov 15-17, 2017. New York: PMLR, 2017: 590-605.
[32] 陈新元, 谢晟祎, 陈庆强, 等. 结合卷积特征提取和路径语义的知识推理[J]. 智能系统学报, 2021, 16(4): 729-738.
CHEN X Y, XIE S Y, CHEN Q Q, et al. Knowledge-based inference on convolutional feature extraction and path semantics[J]. CAAI Transactions on Intelligent Systems, 2021, 16(4): 729-738.
[33] GULRAJANI I, AHMED F, ARJOVSKY M, et al. Improved training of Wasserstein GANs[C]//Advances in Neural Information Processing Systems, 2017: 5769-5779.
[34] KINGMA D, BA J. Adam: a method for stochastic optimization[C]//Proceedings of the International Conference on Learning Representations, San Diego, May 7-9, 2015. USA: ICLR Press, 2015: 1-15.
[35] CARLSON A, BETTERIDGE J, KISIEL B, et al. Toward an architecture for never-ending language learning[C]//Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, Atlanta, Jul 11-15, 2010. Menlo Park: AAAI, 2010: 1306-1313.
[36] LIANG S, SHAO J, ZHANG D, et al. DRGI: deep relational graph infomax for knowledge graph completion[J]. IEEE Transactions on Knowledge and Data Engineering, 2021, 35(3): 2486-2499.
[37] HUANG J, LU T, ZHU J, et al. Multi-relational knowledge graph completion method with local information fusion[J]. Applied Intelligence, 2022, 52(7): 7985-7994.