基于深度学习的领域知识对齐模型研究:知识图谱视角
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摘要
为了解决领域知识融合过程中所带来的冗余和不一致问题,本文从知识图谱视角研究领域知识对齐。在知识图谱深度表示学习的基础上,提出了一种新的知识图谱对齐(knowledge graph alignment,KGA)模型。为验证模型的有效性,在异构知识图谱和跨语言知识图谱的相关数据集上进行对比实验。在异构数据集上,相比于传统的MTransE和IPTransE,KGA模型的Hits@1指标值最高提升了6.40%,MRR指标值最高提升了6.30%;在跨语言数据集上,模型的Hits@1指标值最高提升了9.66%,MRR指标值最高提升了9.60%。实验结果表明,KGA模型在领域知识对齐上的效果优于传统领域知识对齐方法。研究结果对于改进知识图谱实体对齐效果,提升领域知识的覆盖率和正确率,促进知识图谱在情报学领域的应用具有重要意义。
To solve the problems of redundancy and inconsistency in the process of domain knowledge fusion, this paper studies domain knowledge alignment from the perspective of the knowledge graph. A novel knowledge graph alignment(KGA) model is proposed based on knowledge graph deep-representation learning. To verify the validity of the model,comparative experiments are conducted on the datasets of heterogeneous knowledge graphs and cross-lingual knowledge graphs. On heterogeneous datasets, the experimental results show that the Hits@1 value of the model is increased by6.40% and the MRR value is increased by 6.30% over the traditional MTransE and IPTransE. On cross-lingual datasets,the experimental results show that the Hits@1 value of the model is increased by 9.66% and the MRR value is increased by 9.60%. The experimental results show that the effect of the KGA model on domain knowledge alignment is better than the traditional domain knowledge alignment methods. These research results are of great significance for improving the alignment effect of knowledge graph entities, improving the coverage and the correct rate of domain knowledge, and pro‐moting the performance of knowledge graphs in the information field.
To solve the problems of redundancy and inconsistency in the process of domain knowledge fusion, this paper studies domain knowledge alignment from the perspective of the knowledge graph. A novel knowledge graph alignment(KGA) model is proposed based on knowledge graph deep-representation learning. To verify the validity of the model,comparative experiments are conducted on the datasets of heterogeneous knowledge graphs and cross-lingual knowledge graphs. On heterogeneous datasets, the experimental results show that the Hits@1 value of the model is increased by6.40% and the MRR value is increased by 6.30% over the traditional MTransE and IPTransE. On cross-lingual datasets,the experimental results show that the Hits@1 value of the model is increased by 9.66% and the MRR value is increased by 9.60%. The experimental results show that the effect of the KGA model on domain knowledge alignment is better than the traditional domain knowledge alignment methods. These research results are of great significance for improving the alignment effect of knowledge graph entities, improving the coverage and the correct rate of domain knowledge, and pro‐moting the performance of knowledge graphs in the information field.
引文
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[33]Sun Z Q,Hu W,Li C K.Cross-lingual entity alignment via joint attribute-preserving embedding[C]//Proceedings of International Semantic Web Conference.Cham:Springer,2017,10587:628-644.
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[35]王汀,高迎,刘经纬.一种面向中文本体模式的本体对齐框架[J].数据分析与知识发现,2017,1(2):47-57.
[36]孙辉,王颖,张智雄.本体构建中的协同问题研究--以中华人民共和国史本体为例[J].情报学报,2015,34(9):958-969.
[37]赵蓉英,张心源.大数据环境对知识融合的影响研究[J].情报学报,2017,36(9):878-885.
[38]Zhang Q H,Sun Z Q,Hu W.iswc2018 dataset[EB/OL].[2018-05-09].https://www.dropbox.com/s/jmkumdyv6etx4hn/iswc2018-dataset.7z?dl=0.
[2]胡泽文,武夷山,袁军鹏.零被引研究文献的知识图谱分析--历史发展脉络、主体和高频主题[J].情报科学,2016,36(3):85-91.
[3]张洋,赵镇宁.共现科学知识图谱构建技术与工具研究[J].图书情报知识,2019(1):119-129.
[4]赵一鸣.知识图谱是一种知识组织系统吗?[J].图书情报知识,2017(5):卷首语.
[5]Newton C.Google.s Knowledge graph tripled in size in seven months[EB/OL].[2019-01-20].https://en.wikipedia.org/wiki/CBS_Interac‐tive.
[6]董慧,杨宁,余传明,等.基于本体的数字图书馆检索模型研究(Ⅰ)--体系结构解析[J].情报学报,2006,25(3):269-275.
[7]董慧,余传明,姜赢,等.基于本体的数字图书馆检索模型研究(Ⅱ)--语义信息的提取[J].情报学报,2006,25(4):451-461.
[8]董慧,余传明,杨宁,等.基于本体的数字图书馆检索模型研究(Ⅲ)--历史领域资源本体构建[J].情报学报,2006,25(5):564-574.
[9]董慧,余传明,徐国虎,等.基于本体的数字图书馆检索模型研究(Ⅳ)--历史领域知识推理机制[J].情报学报,2006,25(6):666-678.
[10]娄国哲,王兰成.基于知识图谱的网络舆情知识组织方法研究[J].情报理论与实践,2019,42(1):58-64.
[11]孙雨生,常凯月,朱礼军.大规模知识图谱及其应用研究[J].情报理论与实践,2018,41(11):138-143.
[12]张兆锋,张均胜,姚长青.一种基于知识图谱的技术功效图自动构建方法[J].情报理论与实践,2018,41(3):149-155.
[13]Bizer C,Lehmann J,Kobilarov G,et al.DBpedia-A crystalliza‐tion point for the Web of Data[J].Journal of Web Semantics,2009,7(3):154-165.
[14]Vrande?i?D,Kr?tzsch M.Wikidata:A free collaborative knowl‐edgebase[J].Communications of the ACM,2014,57(10):78-85.
[15]Suchanek F M,Kasneci G,Weikum G.YAGO:A large ontology from Wikipedia and WordNet[J].Journal of Web Semantic,2008,6(3):203-217.
[16]马飞翔,廖祥文,於志勇,等.基于知识图谱的文本观点检索方法[J].山东大学学报(理学版),2016,51(11):33-40.
[17]Fader A,Zettlemoyer L,Etzioni O.Open question answering over curated and extracted knowledge bases[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM Press,2014:1156-1165.
[18]徐增林,盛泳潘,贺丽荣,等.知识图谱技术综述[J].电子科技大学学报,2016,45(4):589-606.
[19]Bordes A,Usunier N,Garcia-Durán A,et al.Translating embed‐dings for modeling multi-relational data[C]//Proceedings of the Neural Information Processing Systems.Cambridge:MIT Press,2013,26:2787-2795.
[20]Wang Z,Zhang J W,Feng J L,et al.Knowledge graph embed‐ding by translating on hyperplanes[C]//Proceedings of the Twen‐ty-Eighth AAAI Conference on Artificial Intelligence.Palo Alto:AAAI Press,2014:1112-1119.
[21]Ji G L,He S Z,Xu L H,et al.Knowledge graph embedding via dynamic mapping matrix[C]//Proceedings of the Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing.Stroudsburg:ACL Press,2015:687-696.
[22]Lin Y K,Liu Z Y,Zhu M S,et al.Learning entity and relation em‐beddings for knowledge graph completion[C]//Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence.Palo Alto:AAAI Press,2015:2181-2187.
[23]Lin Y K,Liu Z Y,Luan H B,et al.Modeling relation paths for representation learning of knowledge bases[C]//Proceedings of the Conference on Empirical Methods in Natural Language Pro‐cessing.Stroudsburg:ACL Press,2015:705-714.
[24]Nickel M,Tresp V,Kriegel H P.A three-way model for collective learning on multi-relational data[C]//Proceedings of the 28th In‐ternational Conference on Machine Learning.New York:ACMPress,2011:809-816.
[25]吴运兵,朱丹红,廖祥文,等.路径张量分解的知识图谱推理算法[J].模式识别与人工智能,2017,30(5):473-480.
[26]Socher R,Chen D Q,Manning C D,et al.Reasoning with neural tensor networks for knowledge base completion[C]//Proceedings of the 26th International Conference on Neural Information Pro‐cessing Systems.Red Hook:Curran Associates,2013,1:926-934.
[27]Dettmers T,Minervini P,Stenetorp P,et al.Convolutional 2Dknowledge graph embeddings[C]//Proceedings of the Thirty-Sec‐ond AAAI Conference on Artificial Intelligence.Palo Alto:AAAIPress,2018:1811-1818.
[28]Kotnis B,Nastase V.Analysis of the impact of negative sampling on link prediction in knowledge graphs[OL].[2018-05-02].https://arxiv.org/pdf/1708.06816v2.pdf.
[29]Cai L W,Wang W Y.KBGAN:Adversarial learning for knowl‐edge graph embeddings[OL].[2018-04-16].https://arxiv.org/pdf/1711.04071.pdf.
[30]Hao Y C,Zhang Y Z,He S Z,et al.A joint embedding method for entity alignment of knowledge bases[C]//Proceedings of the 1st China Conference on Knowledge Graph and Semantic Comput‐ing.Singapore:Springer,2016,650:3-14.
[31]Sun M S,Zhu H,Xie R B,et al.Iterative entity alignment via joint knowledge embeddings[C]//Proceedings of the 26th Interna‐tional Joint Conference on Artificial Intelligence.Palo Alto:AAAI Press,2017:4258-4264.
[32]Chen M H,Tian Y T,Yang M H,et al.Multilingual knowledge graph embeddings for cross-lingual knowledge alignment[C]//Proceedings of the 26th International Joint Conference on Artifi‐cial Intelligence.Palo Alto:AAAI Press,2017:1511-1517.
[33]Sun Z Q,Hu W,Li C K.Cross-lingual entity alignment via joint attribute-preserving embedding[C]//Proceedings of International Semantic Web Conference.Cham:Springer,2017,10587:628-644.
[34]Sun Z Q,Hu W,Zhang Q H,et al.Bootstrapping entity alignment with knowledge graph embedding[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence and the23rd European Conference on Artificial Intelligence.Strouds‐burg:ACL Press,2018:4396-4402.
[35]王汀,高迎,刘经纬.一种面向中文本体模式的本体对齐框架[J].数据分析与知识发现,2017,1(2):47-57.
[36]孙辉,王颖,张智雄.本体构建中的协同问题研究--以中华人民共和国史本体为例[J].情报学报,2015,34(9):958-969.
[37]赵蓉英,张心源.大数据环境对知识融合的影响研究[J].情报学报,2017,36(9):878-885.
[38]Zhang Q H,Sun Z Q,Hu W.iswc2018 dataset[EB/OL].[2018-05-09].https://www.dropbox.com/s/jmkumdyv6etx4hn/iswc2018-dataset.7z?dl=0.