Knowledge graph multi-hop reasoning model fusing path and subgraph features

doi:10.11772/j.issn.1001-9081.2024010050

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (1): 32-39.DOI: 10.11772/j.issn.1001-9081.2024010050

• Artificial intelligence • Previous Articles Next Articles

Knowledge graph multi-hop reasoning model fusing path and subgraph features

Rui LI¹, Guanfeng LI¹^,²(), Dezhou HU¹, Wenxin GAO¹

^1.School of Information Engineering，Ningxia University，Yinchuan Ningxia 750021，China
^2.Ningxia Key Laboratory of Artificial Intelligence and Information Security for Channeling Computing Resources from the East to the West （Ningxia University），Yinchuan Ningxia 750021，China

Received:2024-01-18 Revised:2024-04-10 Accepted:2024-04-10 Online:2024-05-09 Published:2025-01-10
Contact: Guanfeng LI
About author:LI Rui， born in 1998， M. S. candidate. Her research interests include knowledge graph.
HU Dezhou， born in 2000， M. S. candidate. His research interests include knowledge graph， named entity recognition.
GAO Wenxin， born in 1998， M. S. candidate. Her research interests include knowledge graph， recommendation model.
Supported by:
National Natural Science Foundation of China(62066038);Ningxia Hui Autonomous Region Key Research and Development Program(2023BSB03066);2023 Graduate Innovation Project of Ningxia University(CXXM202356)

融合路径与子图特征的知识图谱多跳推理模型

李瑞¹, 李贯峰¹^,²(), 胡德洲¹, 高文馨¹

^1.宁夏大学信息工程学院，银川 750021
^2.宁夏“东数西算”人工智能与信息安全重点实验室（宁夏大学），银川 750021

通讯作者: 李贯峰
作者简介:李瑞（1998—），女（回族），宁夏银川人，硕士研究生，CCF会员，主要研究方向：知识图谱；
胡德洲（2000—），男，河北邯郸人，硕士研究生，CCF会员，主要研究方向：知识图谱、命名实体识别；
高文馨（1998—），女，宁夏石嘴山人，硕士研究生，主要研究方向：知识图谱、推荐模型。
基金资助:
国家自然科学基金资助项目(62066038);宁夏重点研发项目(2023BSB03066);宁夏大学2023研究生创新项目(CXXM202356)

Abstract

Abstract:

To address the issues that the knowledge reasoning model has difficulties in capturing multi-level semantic information and the lack of consideration for the different influence of weights of the interpretability of single paths on the correct answer， a Knowledge Graph （KG） multi-hop reasoning model fusing path and subgraph features — PS-HAM （Hierarchical Attention Model fusing Path-Subgraph features） was proposed. In PS-HAM， entity neighborhood information and connection path information were fused， and the multi-granularity features were explored aiming at different paths. Firstly， the path-level feature extraction module was used to extract the connection path between each entity pair， and the hierarchical attention mechanism was employed to capture information with different granularities， which was used as the path-level representation. Secondly， the subgraph feature extraction module was used to aggregate the entity's neighborhood information through the Relational Graph Convolutional Network （RGCN）. Finally， the path-subgraph feature fusion module was employed to fuse the path-level and subgraph-level feature vectors for the realization of fusion reasoning. Experimental results on two public datasets show that PS-HAM has effective performance improvement in both Mean Reciprocal Rank （MRR） and Hit@k （k=1， 3， 10） indices. Compared with the MemoryPath model， the PS-HAM increased MRR index by 1.5 and 1.2 percentage points respectively on FB15k-237 and WN18RR datasets. At the same time， the parameter verification results of the subgraph hop number show that the optimal effect is achieved when the subgraph hop number is 3 on both datasets.

Key words: Knowledge Graph (KG), multi-hop reasoning, subgraph feature, path extraction, feature fusion

摘要：

针对知识推理模型在捕获实体之间的复杂语义特征方面难以捕捉多层次语义信息，同时未考虑单一路径的可解释性对正确答案的影响权重不同等问题，提出一种融合路径与子图特征的知识图谱（KG）多跳推理模型PS-HAM （Hierarchical Attention Model fusing Path-Subgraph features）。PS-HAM将实体邻域信息与连接路径信息进行融合，并针对不同路径探索多粒度的特征。首先，使用路径级特征提取模块提取每个实体对之间的连接路径，并采用分层注意力机制捕获不同粒度的信息，且将这些信息作为路径级的表示；其次，使用子图特征提取模块通过关系图卷积网络（RGCN）聚合实体的邻域信息；最后，使用路径-子图特征融合模块对路径级与子图级特征向量进行融合，以实现融合推理。在两个公开数据集上进行实验的结果表明，PS-HAM在指标平均倒数秩（MRR）和Hit@k （k=1，3，10）上的性能均存在有效提升。对于指标MRR，与MemoryPath模型相比，PS-HAM在FB15k-237和WN18RR数据集上分别提升了1.5和1.2个百分点。同时，对子图跳数进行的参数验证的结果表明，PS-HAM在两个数据集上都在子图跳数在3时推理效果达到最佳。

关键词: 知识图谱, 多跳推理, 子图特征, 路径提取, 特征融合

CLC Number:

TP391.1

Rui LI, Guanfeng LI, Dezhou HU, Wenxin GAO. Knowledge graph multi-hop reasoning model fusing path and subgraph features[J]. Journal of Computer Applications, 2025, 45(1): 32-39.

李瑞, 李贯峰, 胡德洲, 高文馨. 融合路径与子图特征的知识图谱多跳推理模型[J]. 《计算机应用》唯一官方网站, 2025, 45(1): 32-39.

Figures/Tables 7

References 34

1	陈子睿，王鑫，王林，等.开放领域知识图谱问答研究综述［J］.计算机科学与探索， 2021， 15（10）： 1843-1869.
	CHEN Z R， WANG X， WANG L， et al. Survey of open-domain knowledge graph question answering ［J］. Journal of Frontiers of Computer Science and Technology， 2021， 15（10）： 1843-1869.
2	HUANG H， WANG Y， FENG C， et al. Leveraging conceptualization for short-text embedding ［J］. IEEE Transactions on Knowledge and Data Engineering， 2018， 30（7）： 1282-1295.
3	CHEN Y， LI H， QI G， et al. Outlining and filling： hierarchical query graph generation for answering complex questions over knowledge graphs ［J］. IEEE Transactions on Knowledge and Data Engineering， 2023， 35（8）： 8343-8357.
4	HE H， BALAKRISHNAN A， ERIC M， et al. Learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings ［C］// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics （Volume 1： Long Papers）. Stroudsburg： ACL， 2017： 1766-1776.
5	YANG A， WANG Q， LIU J， et al. Enhancing pre-trained language representations with rich knowledge for machine reading comprehension ［C］// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg： ACL， 2019： 2346-2357.
6	侯中妮，靳小龙，陈剑赟，等.知识图谱可解释推理研究综述［J］.软件学报， 2022， 33（12）： 4644-4667.
	HOU Z N， JIN X L， CHEN J Y， et al. Survey of interpretable reasoning on knowledge graphs ［J］. Journal of Software， 2022， 33（12）： 4644-4667.
7	LIANG Y， XU F， ZHANG S H， et al. Knowledge graph construction with structure and parameter learning for indoor scene design ［J］. Computational Visual Media， 2018， 4（2）： 123-137.
8	陈跃鹤，谈川源，陈文亮，等.结合多重嵌入表示的中文知识图谱补全［J］.中文信息学报， 2023， 37（1）： 54-63.
	CHEN Y H， TAN C Y， CHEN W L， et al. Chinese knowledge graph complementation with multiple embeddings ［J］. Journal of Chinese Information Processing， 2023， 37（1）： 54-63.
9	WANG Y， LIU Y， ZHANG H， et al. Leveraging lexical semantic information for learning concept-based multiple embedding representations for knowledge graph completion ［C］// Proceedings of the 2019 Asia-Pacific Web （APWeb） and Web-Age Information Management （WAIM） Joint International Conference on Web and Big Data， LNCS 11641. Cham： Springer， 2019： 382-397.
10	BORDES A， USUNIER N， GARCIA-DURÁN A， et al. Translating embeddings for modeling multi-relational data ［C］// Proceedings of the 26th International Conference on Neural Information Processing Systems. Red Hook： Curran Associates Inc.， 2013： 2787-2795.
11	WANG Z， ZHANG J， FENG J， et al. Knowledge graph embedding by translating on hyperplanes ［C］// Proceedings of the 28th AAAI Conference on Artificial Intelligence. Palo Alto： AAAI Press， 2014： 1112-1119.
12	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. Palo Alto： AAAI Press， 2015： 2181-2187.
13	YANG B， YIH W T， HE X， et al. Embedding entities and relations for learning and inference in knowledge bases ［EB/OL］. ［2023-11-13］. .
14	李源，马新宇，杨国利，等.面向知识图谱和大语言模型的因果关系推断综述［J］.计算机科学与探索， 2023， 17（10）： 2358-2376.
	LI Y， MA X Y， ZHAO G L， et al. Survey of causal inference for knowledge graphs and large language models ［J］. Journal of Frontiers of Computer Science and Technology， 2023， 17（10）： 2358-2376.
15	LAO N， COHEN W W. Relational retrieval using a combination of path-constrained random walks ［J］. Machine Learning， 2010， 81（1）： 53-67.
16	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. Madison， WI： Omnipress， 2011： 809-816.
17	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： JMLR.org， 2016： 2071-2080.
18	DETTMERS T， MINERVINI P， STENETORP P， et al. Convolutional 2D knowledge graph embeddings ［C］// Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Palo Alto： AAAI Press， 2018： 1811-1818.
19	BALAŽEVIĆ I， ALLEN C， HOSPEDALES T M. Hypernetwork knowledge graph embeddings ［C］// Proceedings of the 2019 International Conference on Artificial Neural Networks： Workshop and Special Sessions， LNCS 11731. Cham： Springer， 2019： 553-565.
20	LE T， LE N， LE B. Knowledge graph embedding by relational rotation and complex convolution for link prediction ［J］. Expert Systems with Applications， 2023， 214： No.119122.
21	XIONG B， ZHU S， NAYYERI M， et al. Ultrahyperbolic knowledge graph embeddings ［C］// Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York： ACM， 2022： 2130-2139.
22	WANG Q， LIU J， LUO Y， et al. Knowledge base completion via coupled path ranking ［C］// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics （Volume 1： Long Papers）. Stroudsburg： ACL， 2016： 1308-1318.
23	NEELAKANTAN A， ROTH B， McCALLUM A. Compositional vector space models for knowledge base inference ［C］// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing （Volume 1： Long Papers）. Stroudsburg： ACL， 2015： 156-166.
24	McCALLUM A， 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： Volume 1， Long Papers. Stroudsburg： ACL， 2017： 132-141.
25	JAGVARAL B， LEE W K， ROH J S， et al. Path-based reasoning approach for knowledge graph completion using CNN-BiLSTM with attention mechanism ［J］. Expert Systems with Applications， 2020， 142： No.112960.
26	XIONG W， HOANG T L G， 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. Stroudsburg： ACL， 2017： 564-573.
27	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 ［EB/OL］. ［2023-11-13］. .
28	LI S， WANG H， PAN R， et al. MemoryPath： a deep reinforcement learning framework for incorporating memory component into knowledge graph reasoning ［J］. Neurocomputing， 2021， 419： 273-286.
29	BALLOCCU G， BORATTO L， FENU G， et al. Reinforcement recommendation reasoning through knowledge graphs for explanation path quality ［J］. Knowledge-Based Systems， 2023， 260： No.110098.
30	SHEN Y， DING N， ZHENG H T， et al. Modeling relation paths for knowledge graph completion ［J］. IEEE Transactions on Knowledge and Data Engineering， 2021， 33（11）： 3607-3617.
31	LI C， PENG X， ZHANG S， et al. Modeling relation paths for knowledge base completion via joint adversarial training ［J］. Knowledge-Based Systems， 2020， 201/202： No.105865.
32	WANG Y， XIAO W， TAN Z， et al. Caps-OWKG： a capsule network model for open-world knowledge graph ［J］. International Journal of Machine Learning and Cybernetics， 2021， 12（6）： 1627-1637.
33	SCHLICHTKRULL M， KIPF T N， BLOEM P， et al. Modeling relational data with graph convolutional networks ［C］// Proceedings of the 2018 European Semantic Web Conference， LNCS 10843. Cham： Springer， 2018： 593-607.
34	TOUTANOVA K， CHEN D. Observed versus latent features for knowledge base and text inference ［C］// Proceedings of the 3rd Workshop on Continuous Vector Space Models and Their Compositionality. Stroudsburg： ACL， 2015： 57-66.

数据集	实体数	关系数	三元组	训练集	测试集	验证集
FB15k-237	14 541	237	310 116	272 115	17 535	20 466
WN18RR	40 943	11	93 003	86 835	3 034	3 134

数据集	实体数	关系数	三元组	训练集	测试集	验证集
FB15k-237	14 541	237	310 116	272 115	17 535	20 466
WN18RR	40 943	11	93 003	86 835	3 034	3 134

模型	MRR	Hit@1	Hit@3	Hit@10
Complex	28.6	22.3	36.0	47.2
ConvE	33.8	32.7	35.5	50.1
RotatE	31.0	32.0	35.1	48.5
DeepPath	32.1	23.5	35.6	49.3
MINERVA	29.3	21.7	32.9	45.6
MemoryPath	33.2	29.6	36.2	50.7
PS-HAM	34.7	33.4	36.3	51.3

模型	MRR	Hit@1	Hit@3	Hit@10
Complex	28.6	22.3	36.0	47.2
ConvE	33.8	32.7	35.5	50.1
RotatE	31.0	32.0	35.1	48.5
DeepPath	32.1	23.5	35.6	49.3
MINERVA	29.3	21.7	32.9	45.6
MemoryPath	33.2	29.6	36.2	50.7
PS-HAM	34.7	33.4	36.3	51.3

模型	MRR	Hit@1	Hit@3	Hit@10
Complex	42.3	40.9	44.9	45.1
ConvE	48.3	39.8	43.2	48.9
RotatE	47.6	41.2	46.5	50.4
DeepPath	46.7	42.5	47.2	52.1
MINERVA	45.0	41.3	46.1	51.5
MemoryPath	48.4	41.7	46.8	50.4
PS-HAM	49.6	42.6	49.1	52.5

Knowledge graph multi-hop reasoning model fusing path and subgraph features

融合路径与子图特征的知识图谱多跳推理模型

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 34

Related Articles 15

Recommended Articles

Metrics

[1]	Shang LIU, Yuwei ZHOU, Rao DAI, Linfang DONG, Meng LIU. Small target detection algorithm in remote sensing images integrating attention and contextual information [J]. Journal of Computer Applications, 2025, 45(1): 292-300.
[2]	Pengcheng SONG, Lijun GUO, Rong ZHANG. Weakly supervised video anomaly detection with local-global temporal dependency [J]. Journal of Computer Applications, 2025, 45(1): 240-246.
[3]	Yexin PAN, Zhe YANG. Optimization model for small object detection based on multi-level feature bidirectional fusion [J]. Journal of Computer Applications, 2024, 44(9): 2871-2877.
[4]	Ruihua LIU, Zihe HAO, Yangyang ZOU. Gait recognition algorithm based on multi-layer refined feature fusion [J]. Journal of Computer Applications, 2024, 44(7): 2250-2257.
[5]	Mengyuan HUANG, Kan CHANG, Mingyang LING, Xinjie WEI, Tuanfa QIN. Progressive enhancement algorithm for low-light images based on layer guidance [J]. Journal of Computer Applications, 2024, 44(6): 1911-1919.
[6]	Jianjing LI, Guanfeng LI, Feizhou QIN, Weijun LI. Multi-relation approximate reasoning model based on uncertain knowledge graph embedding [J]. Journal of Computer Applications, 2024, 44(6): 1751-1759.
[7]	Yue LIU, Fang LIU, Aoyun WU, Qiuyue CHAI, Tianxiao WANG. 3D object detection network based on self-attention mechanism and graph convolution [J]. Journal of Computer Applications, 2024, 44(6): 1972-1977.
[8]	Xin LI, Qiao MENG, Junyi HUANGFU, Lingchen MENG. YOLOv5 multi-attribute classification based on separable label collaborative learning [J]. Journal of Computer Applications, 2024, 44(5): 1619-1628.
[9]	Guijin HAN, Xinyuan ZHANG, Wentao ZHANG, Ya HUANG. Self-supervised image registration algorithm based on multi-feature fusion [J]. Journal of Computer Applications, 2024, 44(5): 1597-1604.
[10]	Hongtian LI, Xinhao SHI, Weiguo PAN, Cheng XU, Bingxin XU, Jiazheng YUAN. Few-shot object detection via fusing multi-scale and attention mechanism [J]. Journal of Computer Applications, 2024, 44(5): 1437-1444.
[11]	Jie GUO, Jiayu LIN, Zuhong LIANG, Xiaobo LUO, Haitao SUN. Recommendation method based on knowledge‑awareness and cross-level contrastive learning [J]. Journal of Computer Applications, 2024, 44(4): 1121-1127.
[12]	Zongze JIA, Pengfei GAO, Yinglong MA, Xiaofeng LIU, Haixin XIA. Multi-feature fusion attention-based hierarchical classification method for dialogue act [J]. Journal of Computer Applications, 2024, 44(3): 715-721.
[13]	Zhanjun JIANG, Baijing WU, Long MA, Jing LIAN. Faster-RCNN water-floating garbage recognition based on multi-scale feature and polarized self-attention [J]. Journal of Computer Applications, 2024, 44(3): 938-944.
[14]	Xinye LI, Yening HOU, Yinghui KONG, Zhiqi YAN. Few-shot object detection combining feature fusion and enhanced attention [J]. Journal of Computer Applications, 2024, 44(3): 745-751.
[15]	Ning WU, Yangyang LUO, Huajie XU. Semantic segmentation method for remote sensing images based on multi-scale feature fusion [J]. Journal of Computer Applications, 2024, 44(3): 737-744.