中文版 | English
题名

冷启动场景下基于下游任务的知识图谱补全

其他题名
KNOWLEDGE GRAPH COMPLETION BASED ON DOWNSTREAM TASK IN COLD-START SCENARIO
姓名
姓名拼音
WANG Zhiyuan
学号
12032878
学位类型
硕士
学位专业
0809 电子科学与技术
学科门类/专业学位类别
08 工学
导师
唐珂
导师单位
计算机科学与工程系
论文答辩日期
2023-05-13
论文提交日期
2023-06-28
学位授予单位
南方科技大学
学位授予地点
深圳
摘要

    知识图谱以结构化的表达方式对现实世界中的客观事实进行描述,被广泛应用于推荐系统、语义搜索等领域以提升下游任务性能。知识图谱补全是一种用于发现初始知识图谱中遗漏关系的技术且现有方法主要利用初始知识图谱中的已知关系来构建一个用于预测遗漏关系的模型,这意味着现有方法对于初始知识图谱的质量有着极高的依赖。然而在实际应用中,可能存在初始知识图谱中关系极其稀疏甚至没有关系的冷启动知识图谱补全场景,在这类场景下,现有知识图谱补全方法无法构建出一个有效的用于预测遗漏关系的模型,难以进行补全。面对这一挑战,本论文针对知识图谱主要用于提升下游任务性能的应用特点,提出了一种基于知识图谱下游任务性能进行知识图谱补全的算法框架。并以推荐系统作为下游任务的典型代表,围绕冷启动场景下基于下游任务的知识图谱补全这一主题开展了两项研究工作。

    第一项研究工作针对冷启动场景下基于下游任务的知识图谱补全问题存在的解空间庞大、优化目标复杂且评估代价高昂、约束条件复杂等求解难点,提出了一种基于遗传算法的冷启动知识图谱补全算法以缓解上述问题。在公开数据集上的实验结果证明了该算法可以在冷启动场景下对知识图谱进行补全并大幅提升下游推荐系统性能。

    在拥有大量关系类型的知识图谱补全场景中,第一项研究工作中提出的算法会面临可扩展性不足的问题。本论文结合现有知识图谱补全研究领域中的基于语义匹配的知识图谱补全方法,提出了一种基于演化策略的冷启动知识图谱补全算法。在公开数据集上的实验结果证明了该算法不仅可以在冷启动场景下对知识图谱进行补全并大幅提升下游推荐系统性能,相较第一个工作中提出的算法也具有性能和效率上的优势。

关键词
语种
中文
培养类别
独立培养
入学年份
2020
学位授予年份
2023-06
参考文献列表

[1] LAO N, MITCHELL T M, COHEN W W. Random Walk Inference and Learning in A LargeScale Knowledge Base[C/OL]//Proceedings of the 2011 Conference on Empirical Methods inNatural Language Processing, EMNLP 2011, 27-31 July 2011, John McIntyre Conference Cen-tre, Edinburgh, UK, A meeting of SIGDAT, a Special Interest Group of the ACL. ACL, 2011:529-539. https://aclanthology.org/D11-1049/.
[2] XIONG W H, HOANG T, WANG W Y. DeepPath: A Reinforcement Learning Method forKnowledge Graph Reasoning[C/OL]//PALMER M, HWA R, RIEDEL S. Proceedings of the2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copen-hagen, Denmark, September 9-11, 2017. Association for Computational Linguistics, 2017:564-573. https://doi.org/10.18653/v1/d17-1060.
[3] WANG H W, REN H Y, LESKOVEC J. Relational Message Passing for Knowledge GraphCompletion[C/OL]//ZHU F D, OOI B C, MIAO C Y. KDD ’21: The 27th ACM SIGKDDConference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14-18, 2021. ACM, 2021: 1697-1707. https://doi.org/10.1145/3447548.3467247.
[4] SHEN Y, DING N, ZHENG H, et al. Modeling Relation Paths for Knowledge Graph Completion[J/OL]. IEEE Trans. Knowl. Data Eng., 2021, 33(11): 3607-3617. https://doi.org/10.1109/TKDE.2020.2970044.
[5] DAS R, DHULIAWALA S, ZAHEER M, et al. Go for a Walk and Arrive at the Answer: Rea-soning Over Paths in Knowledge Bases using Reinforcement Learning[C/OL]//6th InternationalConference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May3, 2018, Conference Track Proceedings. OpenReview.net, 2018. https://openreview.net/forum?id=Syg-YfWCW.
[6] LI R P, CHENG X. DIVINE: A Generative Adversarial Imitation Learning Framework forKnowledge Graph Reasoning[C/OL]//INUI K, JIANG J, NG V, et al. Proceedings of the 2019Conference on Empirical Methods in Natural Language Processing and the 9th InternationalJoint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China,November 3-7, 2019. Association for Computational Linguistics, 2019: 2642-2651. https://doi.org/10.18653/v1/D19-1266.
[7] ZHANG D H, YUAN Z X, LIU H, et al.Learning to Walk with Dual Agents for Knowl-edge Graph Reasoning[C/OL]//Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI2022 Virtual Event, February 22 - March 1, 2022. AAAI Press, 2022: 5932-5941. https://ojs.aaai.org/index.php/AAAI/article/view/20538.
[8] DU Z X, ZHOU C, YAO J C, et al. CogKR: Cognitive Graph for Multi-Hop Knowledge Rea-soning[J/OL]. IEEE Trans. Knowl. Data Eng., 2023, 35(2): 1283-1295. https://doi.org/10.1109/TKDE.2021.3104310.
[9] EBISU T, ICHISE R. Graph Pattern Entity Ranking Model for Knowledge Graph Completion[C/OL]//BURSTEIN J, DORAN C, SOLORIO T. Proceedings of the 2019 Conference of theNorth American Chapter of the Association for Computational Linguistics: Human LanguageTechnologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Longand Short Papers). Association for Computational Linguistics, 2019: 988-997. https://doi.org/10.18653/v1/n19-1104.
[10] BORDES A, USUNIER N, GARCÍA-DURÁN A, et al. Translating Embeddings for ModelingMulti-relational Data[C/OL]//BURGES C J C, BOTTOU L, GHAHRAMANI Z, et al. Advancesin Neural Information Processing Systems 26: 27th Annual Conference on Neural InformationProcessing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe,Nevada, United States. 2013: 2787-2795. https://proceedings.neurips.cc/paper/2013/hash/1cecc7a77928ca8133fa24680a88d2f9-Abstract.html.
[11] WANG Z, ZHANG J W, FENG J L, et al. Knowledge Graph Embedding by Translating onHyperplanes[C/OL]//BRODLEY C E, STONE P. Proceedings of the Twenty-Eighth AAAIConference on Artificial Intelligence, July 27 -31, 2014, Québec City, Québec, Canada. AAAIPress, 2014: 1112-1119. https://ojs.aaai.org/index.php/AAAI/article/view/8870.
[12] XIAO H, HUANG M L, ZHU X Y. TransG : A Generative Model for Knowledge Graph Em-bedding[C/OL]//Proceedings of the 54th Annual Meeting of the Association for ComputationalLinguistics, ACL 2016, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers. TheAssociation for Computer Linguistics, 2016. https://doi.org/10.18653/v1/p16-1219.
[13] SUN Z Q, DENG Z, NIE J, et al. RotatE: Knowledge Graph Embedding by Relational Rotationin Complex Space[C/OL]//7th International Conference on Learning Representations, ICLR2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net, 2019. https://openreview.net/forum?id=HkgEQnRqYQ.
[14] BAI Y S, YING Z T, REN H Y, et al. Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones[C/OL]//RANZATO M, BEYGELZIMER A, DAUPHIN Y N, et al.Advances in Neural Information Processing Systems 34: Annual Conference on Neural Infor-mation Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual. 2021: 12316-12327. https://proceedings.neurips.cc/paper/2021/hash/662a2e96162905620397b19c9d249781-Abstract.html.
[15] NICKEL M, TRESP V, KRIEGEL H. A Three-Way Model for Collective Learning on Multi-Relational Data[C/OL]//GETOOR L, SCHEFFER T.Proceedings of the 28th InternationalConference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2,2011. Omnipress, 2011: 809-816. https://icml.cc/2011/papers/438_icmlpaper.pdf.
[16] YANG B S, YIH W, HE X D, et al. Embedding Entities and Relations for Learning and In-ference in Knowledge Bases[C/OL]//BENGIO Y, LECUN Y. 3rd International Conference onLearning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference TrackProceedings. 2015. http://arxiv.org/abs/1412.6575.
[17] TROUILLON T, WELBL J, RIEDEL S, et al. Complex Embeddings for Simple Link Prediction[C/OL]//BALCAN M, WEINBERGER K Q. JMLR Workshop and Conference Proceedings:volume 48Proceedings of the 33nd International Conference on Machine Learning, ICML2016, New York City, NY, USA, June 19-24, 2016. JMLR.org, 2016: 2071-2080. http://proceedings.mlr.press/v48/trouillon16.html.
[18] KAZEMI S M, POOLE D.SimplE Embedding for Link Prediction in Knowledge Graphs[C/OL]//BENGIO S, WALLACH H M, LAROCHELLE H, et al. Advances in Neural Infor-mation Processing Systems 31: Annual Conference on Neural Information Processing Systems2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada. 2018: 4289-4300. https://proceedings.neurips.cc/paper/2018/hash/b2ab001909a8a6f04b51920306046ce5-Abstract.html.
[19] BALAZEVIC I, ALLEN C, HOSPEDALES T M. TuckER: Tensor Factorization for Knowl-edge Graph Completion[C/OL]//INUI K, JIANG J, NG V, et al.Proceedings of the 2019Conference on Empirical Methods in Natural Language Processing and the 9th InternationalJoint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China,November 3-7, 2019. Association for Computational Linguistics, 2019: 5184-5193. https://doi.org/10.18653/v1/D19-1522.
[20] BORDES A, GLOROT X, WESTON J, et al. A semantic matching energy function for learningwith multi-relational data - Application to word-sense disambiguation[J/OL]. Mach. Learn.,2014, 94(2): 233-259. https://doi.org/10.1007/s10994-013-5363-6.
[21] SOCHER R, CHEN D Q, MANNING C D, et al. Reasoning With Neural Tensor Networksfor Knowledge Base Completion[C/OL]//BURGES C J C, BOTTOU L, GHAHRAMANI Z,et al. Advances in Neural Information Processing Systems 26: 27th Annual Conference onNeural Information Processing Systems 2013. Proceedings of a meeting held December 5-8,2013, Lake Tahoe, Nevada, United States. 2013: 926-934. https://proceedings.neurips.cc/paper/2013/hash/b337e84de8752b27eda3a12363109e80-Abstract.html.
[22] GUAN S P, JIN X L, WANG Y Z, et al.Shared Embedding Based Neural Networks forKnowledge Graph Completion[C/OL]//CUZZOCREA A, ALLAN J, PATON N W, et al. Pro-ceedings of the 27th ACM International Conference on Information and Knowledge Man-agement, CIKM 2018, Torino, Italy, October 22-26, 2018.ACM, 2018: 247-256.https://doi.org/10.1145/3269206.3271704.
[23] DETTMERS T, MINERVINI P, STENETORP P, et al. Convolutional 2D Knowledge GraphEmbeddings[C/OL]//MCILRAITH S A, WEINBERGER K Q.Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Appli-cations of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Ad-vances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018.AAAI Press, 2018: 1811-1818. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17366.
[24] NGUYEN D Q, NGUYEN T D, NGUYEN D Q, et al. A Novel Embedding Model for Knowl-edge Base Completion Based on Convolutional Neural Network[C/OL]//WALKER M A, JI H,STENT A. Proceedings of the 2018 Conference of the North American Chapter of the As-sociation for Computational Linguistics: Human Language Technologies, NAACL-HLT, NewOrleans, Louisiana, USA, June 1-6, 2018, Volume 2 (Short Papers). Association for Computa-tional Linguistics, 2018: 327-333. https://doi.org/10.18653/v1/n18-2053.
[25] SHANG C, TANG Y, HUANG J, et al. End-to-End Structure-Aware Convolutional Networksfor Knowledge Base Completion[C/OL]//The Thirty-Third AAAI Conference on Artificial In-telligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Con-ference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial In-telligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019. AAAI Press,2019: 3060-3067. https://doi.org/10.1609/aaai.v33i01.33013060.
[26] SCHLICHTKRULL M S, KIPF T N, BLOEM P, et al. Modeling Relational Data with GraphConvolutional Networks[C/OL]//GANGEMI A, NAVIGLI R, VIDAL M, et al. Lecture Notes inComputer Science: volume 10843The Semantic Web - 15th International Conference, ESWC2018, Heraklion, Crete, Greece, June 3-7, 2018, Proceedings. Springer, 2018: 593-607. https://doi.org/10.1007/978-3-319-93417-4_38.
[27] NATHANI D, CHAUHAN J, SHARMA C, et al. Learning Attention-based Embeddings forRelation Prediction in Knowledge Graphs[C/OL]//KORHONEN A, TRAUM D R, MÀRQUEZL. Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers. Association for Com-putational Linguistics, 2019: 4710-4723. https://doi.org/10.18653/v1/p19-1466.
[28] LIANG S, SHAO J, ZHANG D Y, et al. DRGI: Deep Relational Graph Infomax for KnowledgeGraph Completion[J/OL]. IEEE Trans. Knowl. Data Eng., 2023, 35(3): 2486-2499. https://doi.org/10.1109/TKDE.2021.3110898.
[29] GALÁRRAGA L A, TEFLIOUDI C, HOSE K, et al. AMIE: association rule mining underincomplete evidence in ontological knowledge bases[C/OL]//SCHWABE D, ALMEIDA V A F,GLASER H, et al. 22nd International World Wide Web Conference, WWW ’13, Rio de Janeiro,Brazil, May 13-17, 2013. International World Wide Web Conferences Steering Committee /ACM, 2013: 413-422. https://doi.org/10.1145/2488388.2488425.
[30] ZUPANC K, DAVIS J.Estimating Rule Quality for Knowledge Base Completion with theRelationship between Coverage Assumption[C/OL]//CHAMPIN P, GANDON F, LALMAS M,et al. Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018,Lyon, France, April 23-27, 2018. ACM, 2018: 1073-1081. https://doi.org/10.1145/3178876.3186006.
[31] MEILICKE C, CHEKOL M W, RUFFINELLI D, et al. Anytime Bottom-Up Rule Learningfor Knowledge Graph Completion[C/OL]//KRAUS S. Proceedings of the Twenty-Eighth Inter-national Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16,2019. ijcai.org, 2019: 3137-3143. https://doi.org/10.24963/ijcai.2019/435.
[32] MEILICKE C, CHEKOL M W, FINK M, et al. Reinforced Anytime Bottom Up Rule Learningfor Knowledge Graph Completion[J/OL]. CoRR, 2020, abs/2004.04412. https://arxiv.org/abs/2004.04412.
[33] ZHANG R C, MAO Y Y, ZHAO W H. Knowledge graphs completion via probabilistic reasoning[J/OL]. Inf. Sci., 2020, 521: 144-159. https://doi.org/10.1016/j.ins.2020.02.016.
[34] QU M, CHEN J, XHONNEUX L A C, et al. RNNLogic: Learning Logic Rules for Reasoningon Knowledge Graphs[C/OL]//9th International Conference on Learning Representations, ICLR2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021. https://openreview.net/forum?id=tGZu6DlbreV.
[35] YANG F, YANG Z, COHEN W W. Differentiable Learning of Logical Rules for KnowledgeBase Reasoning[C/OL]//GUYON I, VON LUXBURG U, BENGIO S, et al. Advances in Neu-ral Information Processing Systems 30: Annual Conference on Neural Information ProcessingSystems 2017, December 4-9, 2017, Long Beach, CA, USA. 2017: 2319-2328. https://proceedings.neurips.cc/paper/2017/hash/0e55666a4ad822e0e34299df3591d979-Abstract.html.
[36] WANG P, STEPANOVA D, DOMOKOS C, et al. Differentiable learning of numerical rulesin knowledge graphs[C/OL]//8th International Conference on Learning Representations, ICLR2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, 2020. https://openreview.net/forum?id=rJleKgrKwS.
[37] ROCKTÄSCHEL T, RIEDEL S. End-to-end Differentiable Proving[C/OL]//GUYON I, VONLUXBURG U, BENGIO S, et al. Advances in Neural Information Processing Systems 30:Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, LongBeach, CA, USA. 2017: 3788-3800. https://proceedings.neurips.cc/paper/2017/hash/b2ab001909a8a6f04b51920306046ce5-Abstract.html.
[38] WEI Z Y, ZHAO J, LIU K, et al.Large-scale Knowledge Base Completion: Inferring viaGrounding Network Sampling over Selected Instances[C/OL]//BAILEY J, MOFFAT A, AG-GARWAL C C, et al. Proceedings of the 24th ACM International Conference on Informationand Knowledge Management, CIKM 2015, Melbourne, VIC, Australia, October 19 - 23, 2015.ACM, 2015: 1331-1340. https://doi.org/10.1145/2806416.2806513.
[39] CHEN W H, XIONG W H, YAN X F, et al. Variational Knowledge Graph Reasoning[C/OL]//WALKER M A, JI H, STENT A. Proceedings of the 2018 Conference of the North Ameri-can Chapter of the Association for Computational Linguistics: Human Language Technologies,NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 1 (Long Papers).Association for Computational Linguistics, 2018: 1823-1832. https://doi.org/10.18653/v1/n18-1165.
[40] ZHENG S F, CHEN W, ZHAO P P, et al. When Hardness Makes a Difference: Multi-HopKnowledge Graph Reasoning over Few-Shot Relations[C/OL]//DEMARTINI G, ZUCCON G,CULPEPPER J S, et al. CIKM ’21: The 30th ACM International Conference on Information andKnowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021. ACM,2021: 2688-2697. https://doi.org/10.1145/3459637.3482402.
[41] ZHANG C X, YU L, SAEBI M, et al. Few-Shot Multi-Hop Relation Reasoning over Knowl-edge Bases[C/OL]//COHN T, HE Y, LIU Y. Findings of ACL: EMNLP 2020Findings ofthe Association for Computational Linguistics: EMNLP 2020, Online Event, 16-20 November2020. Association for Computational Linguistics, 2020: 580-585. https://doi.org/10.18653/v1/2020.findings-emnlp.51.
[42] LV X, GU Y X, HAN X, et al. Adapting Meta Knowledge Graph Information for Multi-HopReasoning over Few-Shot Relations[C/OL]//INUI K, JIANG J, NG V, et al. Proceedings of the2019 Conference on Empirical Methods in Natural Language Processing and the 9th Interna-tional Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong,China, November 3-7, 2019. Association for Computational Linguistics, 2019: 3374-3379.https://doi.org/10.18653/v1/D19-1334.
[43] ZHANG C X, YAO H X, HUANG C, et al. Few-Shot Knowledge Graph Completion[C/OL]//The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-SecondInnovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAISymposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY,USA, February 7-12, 2020. AAAI Press, 2020: 3041-3048. https://ojs.aaai.org/index.php/AAAI/article/view/5698.
[44] SHENG J W, GUO S, CHEN Z Y, et al. Adaptive Attentional Network for Few-Shot Knowl-edge Graph Completion[C/OL]//WEBBER B, COHN T, HE Y, et al.Proceedings of the2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, On-line, November 16-20, 2020. Association for Computational Linguistics, 2020: 1681-1691.https://doi.org/10.18653/v1/2020.emnlp-main.131.
[45] XIONG W H, YU M, CHANG S Y, et al. One-Shot Relational Learning for Knowledge Graphs[C/OL]//RILOFF E, CHIANG D, HOCKENMAIER J, et al. Proceedings of the 2018 Confer-ence on Empirical Methods in Natural Language Processing, EMNLP 2018, Brussels, Belgium,October 31 - November 4, 2018. Association for Computational Linguistics, 2018: 1980-1990.https://doi.org/10.18653/v1/d18-1223.
[46] CHEN M Y, ZHANG W, ZHANG W, et al.Meta Relational Learning for Few-Shot LinkPrediction in Knowledge Graphs[C/OL]//INUI K, JIANG J, NG V, et al. Proceedings of the2019 Conference on Empirical Methods in Natural Language Processing and the 9th Interna-tional Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong,China, November 3-7, 2019. Association for Computational Linguistics, 2019: 4216-4225.https://doi.org/10.18653/v1/D19-1431.
[47] SHI B X, WENINGER T. Open-World Knowledge Graph Completion[C/OL]//MCILRAITHS A, WEINBERGER K Q. Proceedings of the Thirty-Second AAAI Conference on ArtificialIntelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18),and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18),New Orleans, Louisiana, USA, February 2-7, 2018. AAAI Press, 2018: 1957-1964. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16055.
[48] SHAH H, VILLMOW J, ULGES A, et al. An Open-World Extension to Knowledge Graph Com-pletion Models[C/OL]//The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019,The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019,Honolulu, Hawaii, USA, January 27 - February 1, 2019.AAAI Press, 2019: 3044-3051.https://doi.org/10.1609/aaai.v33i01.33013044.
[49] ZHANG Y F, AI Q Y, CHEN X, et al. Learning over Knowledge-Base Embeddings for Rec-ommendation[J/OL]. CoRR, 2018, abs/1803.06540. http://arxiv.org/abs/1803.06540.
[50] AKRAMI F, SAEEF M S, ZHANG Q, et al.Realistic Re-evaluation of Knowledge GraphCompletion Methods: An Experimental Study[C/OL]//MAIER D, POTTINGER R, DOAN A,et al. Proceedings of the 2020 International Conference on Management of Data, SIGMODConference 2020, online conference [Portland, OR, USA], June 14-19, 2020. ACM, 2020:1995-2010. https://doi.org/10.1145/3318464.3380599.
[51] GUO Q Y, ZHUANG F Z, QIN C, et al. A Survey on Knowledge Graph-Based RecommenderSystems[J/OL]. IEEE Trans. Knowl. Data Eng., 2022, 34(8): 3549-3568. https://doi.org/10.1109/TKDE.2020.3028705.
[52] WANG H W, ZHANG F Z, ZHAO M, et al. Multi-Task Feature Learning for Knowledge GraphEnhanced Recommendation[C/OL]//LIU L, WHITE R W, MANTRACH A, et al. The WorldWide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019. ACM, 2019:2000-2010. https://doi.org/10.1145/3308558.3313411.
[53] HUANG J, LING C X. Using AUC and Accuracy in Evaluating Learning Algorithms[J/OL].IEEE Trans. Knowl. Data Eng., 2005, 17(3): 299-310. https://doi.org/10.1109/TKDE.2005.50.
[54] JÄRVELIN K, KEKÄLÄINEN J. Cumulated gain-based evaluation of IR techniques[J/OL].ACM Trans. Inf. Syst., 2002, 20(4): 422-446. http://doi.acm.org/10.1145/582415.582418.
[55] SEHNKE F, OSENDORFER C, RÜCKSTIESS T, et al. Parameter-exploring policy gradients[J/OL]. Neural Netw., 2010, 23(4): 551-559. https://doi.org/10.1016/j.neunet.2009.12.004.
[56] WILLIAMS R J. Simple Statistical Gradient-Following Algorithms for Connectionist Rein-forcement Learning[J/OL]. Mach. Learn., 1992, 8: 229-256. https://doi.org/10.1007/BF00992696.

所在学位评定分委会
电子科学与技术
国内图书分类号
TP182
来源库
人工提交
成果类型学位论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/544193
专题工学院_计算机科学与工程系
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汪至圆. 冷启动场景下基于下游任务的知识图谱补全[D]. 深圳. 南方科技大学,2023.
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