Knowledge Graph Completion Method Based on Interactively Connected Graph Attention Network

doi:10.12141/j.issn.1000-565X.220384

Abstract

Abstract:

Knowledge graph provides underlying support for many intelligent information service applications, including intelligent search, public safety, finance, medical care and other fields. However, the existing knowledge graph is usually incomplete, and knowledge graph completion has become an urgent problem to be solved. The existing knowledge graph completion method models ignore the important information rich in neighbor nodes and relationships, and often simply splice neighbor nodes and relationships together, ignoring the different importance of different relationships and neighbor nodes to nodes. To solve this problem, this paper proposed a knowledge graph completion method (ICGAT) based on the interactive connection graph attention network. The method firstly finds out the potential relationship by finding two-hop neighbor nodes, and expands the triples of each node. Then it fuses the relationship in each triplet with the features of the node, and adopts the method of interactive connection between nodes and neighbor nodes, using 4 space vectors to represent the interactively connected relationship. Finally, the vector of interactive connection was input into the graph attention network to obtain the weight of relations and neighbor nodes to the node, so as to illustrate its importance. In order to effectively represent triples of complex relationships such as one-to-many, many-to-many, etc., this method used the RotatE model as a pre-training model. The experimental results in the link prediction task show that the performance of the mean rank and HR@10indicators of the ICGAT method in the WN18RR and FB15k-237 datasets have been improved to a certain extent, indicating that ICGAT can improve the accuracy of the link prediction task.

Key words: knowledge graph, graph attention network, graph neural network, interactive connectivity

CLC Number:

TP391.1

LU Yiqin, PAN Zhoushuang, ZHANG Yang, et al. Knowledge Graph Completion Method Based on Interactively Connected Graph Attention Network[J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(12): 13-19.

Figures/Tables 7

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References 28

1	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．
2	BOLLACKER K， EVANS C， PARITOSH P，et al ．Freebase：a collaboratively created graph database for structuring human knowledge ［C］∥ Proceedings of 2008 ACM SIGMOD International Conference on Management of Data．Vancouver：ACM，2008：1247-1250．
3	MILLER G A ．WordNet：a lexical database for English ［J］．Communications of the ACM，1995，38（11）：39-41．
4	QI T， WU F， WU C，et al ．Personalized news recommendation with knowledge-aware interactive matching ［C］∥ Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval．New York：ACM，2021：61-70．
5	HAO Y， ZHANG Y， LIU K，et al ．An end-to-end model for question answering over knowledge base with cross-attention combining global knowledge ［C］∥ Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics．Vancouver：ACL，2017：221-231．
6	王屹超，朱慕华，许晨，等．利用图像描述与知识图谱增强表示的视觉问答［J］．清华大学学报（自然科学版），2022，62（5）：900-907．
	WANG Yichao， ZHU Muhua， XU Chen，et al ．Exploiting image captions and external knowledge as representation enhancement for VQA ［J］．Journal of Tsinghua University （Science and Technology），2022，62（5）：900-907．
7	GAVALI S， ROSS K， CHEN C，et al ．A knowledge graph representation learning approach to predict novel kinase-substrate interactions［EB/OL］．（2022-06-05）［2022-06-10］．．
8	NGUYEN D Q ．A survey of embedding models of entities and relationships for knowledge graph completion ［EB/OL］．（2017-03-23）［2022-06-10］．．
9	BORDES A， USUNIER N， GARCIA-DURAN A，et al ．Translating embeddings for modeling multi-relational data ［M］∥ BURGES C J C，BOTTOU L，WELLING M，et al．Advances in neural information processing systems．Red Hook：Curran Associates Inc.，2013：2787-2795．
10	CHURCH K W ．Word2Vec ［J］．Natural Language Engineering，2017，23（1）：155-162．
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．Quebec：AAAI，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．Austin：AAAI，2015：2181-2187．
13	SUN Z， DENG Z H， NIE J Y，et al ．RotatE：know-ledge graph embedding by relational rotation in complex space ［EB/OL］．（2019-02-26）［2022-06-10］．．
14	DEVLIN J， CHANG M W， LEE K，et al ．BERT：pre-training of deep bidirectional transformers for language understanding ［EB/OL］．（2018-10-11）［2022-06-10］．．
15	YAO L， MAO C， LUO Y ．KG-BERT：BERT for knowledge graph completion ［EB/OL］．（2019-09-07）［2022-06-10］．．
16	WANG B， SHEN T， LONG G，et al ．Structure-augmented text representation learning for efficient knowledge graph completion ［C］∥ Proceedings of the 30th Web Conference．Ljubljana：ACM，2021：1737-1748．
17	WANG K， LIU Y， XU X，et al ．Enhancing knowledge graph embedding by composite neighbors for link prediction ［J］．Computing，2020，102（12）：2587-2606．
18	KIPF T N， WELLING M ．Semi-supervised classification with graph convolutional networks ［J］．（2016-09-09）［2022-06-10］．．
19	VELIČKOVIĆ P， CUCURULL G， CASANOVA A，et al ．Graph attention networks ［EB/OL］．（2017-10-30）［2022-06-10］．．
20	ZHANG R， TRISEDYA B D， LI M，et al ．A benchmark and comprehensive survey on knowledge graph entity alignment via representation learning ［J］．VLDB Journal，2022，31（5）：1143-1168．
21	NATHANI D， CHAUHAN J， SHARMA C，et al ．Learning attention-based embeddings for relation prediction in knowledge graphs ［EB/OL］．（2019-06-04）［2022-06-10］．．
22	CHEN L， CUI J， TANG X，et al ．RMNA：a neighbor aggregation-based knowledge graph representation learning model using rule mining ［EB/OL］．（2021-11-01）［2022-06-10］．．
23	NGUYEN D Q， NGUYEN D Q， NGUYEN T D，et al ．A convolutional neural network-based model for know-ledge base completion and its application to search personalization ［J］．Semantic Web，2019，10（5）：947-960．
24	DETTMERS T， MINERVINI P， STENETORP P，et al ．Convolutional 2D knowledge graph embeddings ［C］∥ Proceedings of the 32nd AAAI Conference on Artificial Intelligence．New Orleans：AAAI，2018：1811-1818．
25	VASHISHTH S， SANYAL S， NITIN V，et al ．Inte-ractE：improving convolution-based knowledge graph embeddings by increasing feature interactions ［C］∥Proceedings of the 34th AAAI Conference on Artificial Intelligence．New York：AAAI，2020：3009-3016．
26	XIE Z， ZHOU G， LIU J，et al ．ReInceptionE：relation-aware inception network with joint local-global structural information for knowledge graph embedding ［C］∥ Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics．Seattle：ACL，2020：5929-5939．
27	ZHOU Z， WANG C， FENG Y，et al ．JointE：jointly utilizing 1D and 2D convolution for knowledge graph embedding ［J］．Knowledge-Based Systems，2022，240：108100/1-9．
28	FAN L J， SUN Y Y， XU F，et al ．Knowledge graph embedding based on semantic hierarchy ［J］．Cognitive Robotics，2022，2：147-154．

数据集	实体	关系	训练集	验证集	测试集
FB15k-237	14 541	237	272 115	17 535	20 466
WN18RR	40 943	11	86 835	3 034	3 134

模型	FB15k-237			WN18RR
模型	HR@10/%	MR	MRR	HR@10/%	MR	MRR
TransE	44.1	323	27.9	53.2RRR	2 300	24.3
RotatE	53.3	177	33.8	57.1	3 340	47.6
ConvE	49.7	245	31.2	53.1	4 464	45.6
ConvKB	47.1	216	28.9	55.8	1 295	26.5
InteractE	53.5	172	35.4	52.8	5 202	46.3
ReInceptionE	52.8	173	34.9	58.2	1 894	48.3
JointE	54.3	177	35.6	53.7	4 655	47.1
SHKE	54.3	186	35.2	54.0	3 205	45.2
KBGAT	62.6	210	51.8	58.1	1 940	44.0
ICGAT	63.1	157	49.9	58.3	1 786	43.8

模型	FB15k-237数据集			WN18RR数据集
模型	HR@10/%	MR	MRR	HR@10/%	MR	MRR
ICGAT-0	62.4	166	49.5	57.7	1 985	42.6
ICGAT	63.1	157	49.9	58.3	1 786	43.8

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