A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Open accessAbstract
Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In this survey, we provide a comprehensive review of the knowledge graph covering overall research topics about: 1) knowledge graph representation learning; 2) knowledge acquisition and completion; 3) temporal knowledge graph; and 4) knowledge-aware applications and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning are reviewed. We further explore several emerging topics, including metarelational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of data sets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions.
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Ji et al. divided research on KGs into four categories: knowledge representation learning, knowledge acquisition, temporal KGs and knowledge-aware applications.
“Ji et al. [31] divided the research on KGs into four categories: knowledge representation learning, knowledge acquisition, temporal KGs and knowledge-aware applications.”
From A Survey on Knowledge Graph Embeddings for Link Prediction (2021) · cites this paper as [31]
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Cites
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works
WordNet: a lexical database for English
Miller, G. A. (1995) · Communications of the ACM · doi:10.1145/219717.219748 · Cited by 14,015 · see citing works
(2013) · Cited by 5,726 · see citing works
Modeling Relational Data with Graph Convolutional Networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-93417-4_38 · Cited by 5,570 · see citing works
Freebase: a collaboratively created graph database for structuring human knowledge
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., & Taylor, J. (2008) · Proceedings of the 2008 ACM SIGMOD international conference on Management of data · doi:10.1145/1376616.1376746 · Cited by 4,852 · see citing works
DBpedia: A Nucleus for a Web of Open Data
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Richard Cyganiak, A., & Ives, Z. G. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-76298-0_52 · Cited by 4,847 · see citing works
Neural Architectures for Named Entity Recognition
Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1030 · Cited by 4,401 · see citing works
Knowledge Graph Embedding by Translating on Hyperplanes
Wang, Z., Zhang, J., Feng, J., & Chen, Z. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8870 · Cited by 3,915 · see citing works
DrugBank: a comprehensive resource for in silico drug discovery and exploration
Wishart, D. S. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj067 · Cited by 3,892 · see citing works
Suchanek, F. M., Kasneci, G., & Weikum, G. (2007) · International World Wide Web Conference (WWW) · doi:10.1145/1242572.1242667 · Cited by 3,886 · see citing works
Learning Entity and Relation Embeddings for Knowledge Graph Completion
Lin, Y., Liu, Z., Sun, M., Liu, Y., & Zhu, X. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9491 · Cited by 3,749 · see citing works
Vrandečić, D., & Krötzsch, M. (2014) · Communications of the ACM · doi:10.1145/2629489 · Cited by 3,367 · see citing works
DrugBank: a knowledgebase for drugs, drug actions and drug targets
Wishart, D. S., Knox, C., Guo, A. C., Cheng, D., Shrivastava, S., Tzur, D., Gautam, B., & Hassanali, M. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm958 · Cited by 2,852 · see citing works
Distant supervision for relation extraction without labeled data
Mintz, M. D., Bills, S., Snow, R., & Jurafsky, D. (2009) · Annual Meeting of the Association for Computational Linguistics · doi:10.3115/1690219.1690287 · Cited by 2,799 · see citing works
Knowledge Graph Embedding: A Survey of Approaches and Applications
Wang, Q., Mao, Z., Wang, B., & Guo, L. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2754499 · Cited by 2,654 · see citing works
Computer-Based Medical Consultations: MYCIN.
(1976) · Annals of Internal Medicine · doi:10.7326/0003-4819-85-6-831_1 · Cited by 2,505 · see citing works
Convolutional 2D Knowledge Graph Embeddings
Dettmers, T., Minervini, P., Stenetorp, P., & Riedel, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11573 · Cited by 2,486 · see citing works
Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., de Melo, G., Gutiérrez, C., Kirrane, S., Gayo, J. E. L., Roberto Navigli, A., Neumaier, S., et al. (2021) · ACM Computing Surveys · doi:10.1145/3447772 · Cited by 2,371 · see citing works
ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
Speer, R., Chin, J., & Havasi, C. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11164 · Cited by 2,312 · see citing works
Wang, X., He, X., Cao, Y., Liu, M., & Chua, T. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330989 · Cited by 2,274 · see citing works
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
Yang, B., Yih, W.-T., He, X., Gao, J., & Deng, L. (2014) · arXiv · doi:10.48550/arxiv.1412.6575 · Cited by 2,252 · see citing works
Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification
Zhou, P., Shi, W. Y., Tian, J., Qi, Z., Li, B., Hao, H., & Xu, B. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-2034 · Cited by 2,108 · see citing works
Complex Embeddings for Simple Link Prediction
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., & Bouchard, G. (2016) · arXiv · doi:10.48550/arxiv.1606.06357 · Cited by 2,100 · see citing works
5.7 The WORDNET lexical database of English
Ooi, V. B. Y. (2019) · Edinburgh University Press eBooks · doi:10.1515/9781474471459-050 · Cited by 2,034 · see citing works
Toward an architecture for never-ending language learning
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E., & Mitchell, T. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7519 · Cited by 2,000 · see citing works
Named Entity Recognition with Bidirectional LSTM-CNNs
Chiu, J., & Nichols, E. (2016) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00104 · Cited by 1,925 · see citing works
Knowledge Graph Embedding via Dynamic Mapping Matrix
Ji, G., He, S., Xu, L., Liu, K., & Zhao, J. (2015) · Annual Meeting of the Association for Computational Linguistics · doi:10.3115/v1/p15-1067 · Cited by 1,913 · see citing works
Language Models as Knowledge Bases?
Petroni, F., Rocktäschel, T., Riedel, S., Lewis, P., Bakhtin, A., Wu, Y., & Miller, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1250 · Cited by 1,834 · see citing works
Collaborative Knowledge Base Embedding for Recommender Systems
Zhang, F., Yuan, N. J., Lian, D., Xie, X., & Ma, W. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939673 · Cited by 1,820 · see citing works
A Three-Way Model for Collective Learning on Multi-Relational Data
Nickel, M., Tresp, V., & Kriegel, H. (2011) · Cited by 1,806 · see citing works
A Review of Relational Machine Learning for Knowledge Graphs
Nickel, M., Murphy, K. P., Tresp, V., & Gabrilovich, E. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2483592 · Cited by 1,684 · see citing works
Reasoning With Neural Tensor Networks for Knowledge Base Completion
Socher, R., Chen, D., Manning, C. D., & Ng, A. Y. (2013) · Cited by 1,656 · see citing works
Knowledge vault: a web-scale approach to probabilistic knowledge fusion
Dong, X., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., Murphy, K., Strohmann, T., Sun, S., & Zhang, W. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623623 · Cited by 1,533 · see citing works
ERNIE: Enhanced Language Representation with Informative Entities
Zhang, Z., Han, X., Liu, Z., Jiang, X., Sun, M., & Liu, Q. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1139 · Cited by 1,482 · see citing works
Relation Classification via Convolutional Deep Neural Network
Zeng, D., Liu, K., Lai, S., Zhou, G., & Zhao, J. (2015) · Cited by 1,467 · see citing works
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Sun, Z., Deng, Z.-H., Nie, J.-Y., & Tang, J. (2019) · arXiv · doi:10.48550/arxiv.1902.10197 · Cited by 1,414 · see citing works
Modeling Relations and Their Mentions without Labeled Text
Riedel, S., Yao, L., & McCallum, A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15939-8_10 · Cited by 1,342 · see citing works
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
Miwa, M., & Bansal, M. (2016) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p16-1105 · Cited by 1,284 · see citing works
Knowledge graph refinement: A survey of approaches and evaluation methods
Paulheim, H. (2016) · Semantic Web · doi:10.3233/sw-160218 · Cited by 1,253 · see citing works
Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
Zeng, D., Liu, K., Chen, Y., & Zhao, J. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1203 · Cited by 1,175 · see citing works
Observed versus latent features for knowledge base and text inference
Toutanova, K., & Chen, D. (2015) · Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality · doi:10.18653/v1/w15-4007 · Cited by 1,070 · see citing works
Neural Relation Extraction with Selective Attention over Instances
Lin, Y., Shen, S., Liu, Z., Luan, H., & Sun, M. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1200 · Cited by 1,052 · see citing works
A review: Knowledge reasoning over knowledge graph
Chen, X., Jia, S., & Xiang, Y. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112948 · Cited by 1,035 · see citing works
Wang, H., Zhang, F., Xie, X., & Guo, M. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3178876.3186175 · Cited by 1,034 · see citing works
ERNIE: Enhanced Representation through Knowledge IntegrationPreprint
Sun, Y., Wang, S., Li, Y., Feng, S., Chen, X., Zhang, H., Tian, X., Zhu, D., Tian, H., & Wu, H. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.09223 · Cited by 983 · see citing works
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network
Nguyen, D. Q., Nguyen, T. D., Nguyen, D. Q., & Phung, D. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · doi:10.18653/v1/n18-2053 · Cited by 923 · see citing works
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
Xiong, W., Hoang, T., & Wang, W. Y. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1060 · Cited by 862 · see citing works
Learning Structured Embeddings of Knowledge Bases
Bordes, A., Weston, J., Collobert, R., & Bengio, Y. (2011) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v25i1.7917 · Cited by 849 · see citing works
ConceptNet 5.5: An Open Multilingual Graph of General KnowledgePreprint
Speer, R., Chin, J., & Havasi, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.03975 · Cited by 832 · see citing works
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., & Xu, B. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1113 · Cited by 819 · see citing works
Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
Zhang, Y., Qi, P., & Manning, C. D. (2018) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1244 · Cited by 809 · see citing works
K-BERT: Enabling Language Representation with Knowledge Graph
Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., & Wang, P. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5681 · Cited by 797 · cited 1× · see citing works
“ntities. ERNIE-Tsinghua [168] fuses informative entities via aggregated pre-training and random masking. BERT-MK [169] encodes graph contextualized knowledge and focuses on the medical corpus. K-BERT [170] infuses domain knowledge into BERT contextual encoder. ERNIEBaidu [171] introduces named entity masking and phras…”
BackgroundExplainable Reasoning over Knowledge Graphs for Recommendation
Wang, X., Wang, D., Xu, C., He, X., Cao, Y., & Chua, T.-S. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33015329 · Cited by 785 · see citing works
ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding
Sun, Y., Wang, S., Li, Y., Feng, S., Tian, H., Wu, H., & Wang, H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6428 · Cited by 766 · see citing works
Wu, W., Li, H., Wang, H., & Zhu, K. Q. (2012) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/2213836.2213891 · Cited by 765 · see citing works
Matching the Blanks: Distributional Similarity for Relation Learning
Baldini Soares, L., FitzGerald, N., Ling, J., & Kwiatkowski, T. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1279 · Cited by 731 · see citing works
Representation Learning of Knowledge Graphs with Entity Descriptions
Xie, R., Liu, Z., Jia, J., Luan, H., & Sun, M. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10329 · Cited by 725 · see citing works
End-to-End Structure-Aware Convolutional Networks for Knowledge Base Completion
Shang, C., Tang, Y., Huang, J., Bi, J., He, X., & Zhou, B. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013060 · Cited by 725 · see citing works
A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
Wei, Z., Su, J., Wang, Y., Tian, Y., & Chang, Y. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.136 · Cited by 714 · see citing works
A Unified MRC Framework for Named Entity Recognition
Li, X., Feng, J., Meng, Y., Han, Q., Wu, F., & Li, J. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.519 · Cited by 697 · see citing works
TuckER: Tensor Factorization for Knowledge Graph Completion
Balazevic, I., Allen, C., & Hospedales, T. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1522 · Cited by 696 · cited 4× · see citing works
“nt. & Rel. embed. Scoring Function f r(h;t) RotatE [23] h;t 2Cd, r 2Cd khr tk TorusE [30] [h];[t] 2Tn, [r] 2Tn min (x;y)2([h]+[r]) [t] kx yk i SimplE [47] h;t 2Rd, r;r02Rd 1 2 hrt+ tr0t TuckER [51] h;t 2Rd e , r 2R d r W 1 h 2 r 3 t ITransF [36] h;t 2Rd, r 2Rd H r Dh+ r T r Dt ‘ HolEx [40] h;t 2Rd, r 2Rd P l j=0 p…”
BackgroundMethodInfluentialClassifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths
Xu, Y., Mou, L., Li, G., Chen, Y., Peng, H. M., & Jin, Z. G. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1206 · Cited by 685 · see citing works
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
Wang, X., Gao, T., Zhu, Z., Zhang, Z., Liu, Z., Li, J., & Tang, J. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00360 · Cited by 676 · see citing works
Bordes, A., Glorot, X., Weston, J., & Bengio, Y. (2014) · Machine Learning · doi:10.1007/s10994-013-5363-6 · Cited by 675 · see citing works
Learning Attention-based Embeddings for Relation Prediction in \nKnowledge Graphs
Nathani, D., Chauhan, J., Sharma, C., & Kaul, M. (2019) · Research Archive of Indian Institute of Technology Hyderabad (Indian Institute of Technology Hyderabad) · Cited by 665 · see citing works
Zero-Shot Recognition via Semantic Embeddings and Knowledge Graphs
Wang, X., Ye, Y., & Gupta, A. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00717 · Cited by 659 · see citing works
Han, X., Zhu, H., Yu, P., Wang, Z., Yao, Y., Liu, Z., & Sun, M. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1514 · Cited by 651 · see citing works
Self-citationConvolutional 2D Knowledge Graph EmbeddingsPreprint
Dettmers, T., Minervini, P., Stenetorp, P., & Riedel, S. (2017) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.833287 · Cited by 639 · see citing works
KG-BERT: BERT for Knowledge Graph CompletionPreprint
Yao, L., Mao, C., & Luo, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.03193 · Cited by 638 · see citing works
Relational retrieval using a combination of path-constrained random walks
Lao, N., & Cohen, W. W. (2010) · Machine Learning · doi:10.1007/s10994-010-5205-8 · Cited by 616 · see citing works
Galárraga, L., Teflioudi, C., Hose, K., & Suchanek, F. M. (2013) · International World Wide Web Conference (WWW) · doi:10.1145/2488388.2488425 · Cited by 606 · see citing works
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM
Ma, Y., Peng, H., & Cambria, E. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12048 · Cited by 606 · see citing works
Self-citationConstructing biological knowledge bases by extracting information from text sources.
Craven, M. W., & Kumlien, J. (1999) · PubMed · Cited by 584 · see citing works
Learning Sequence Encoders for Temporal Knowledge Graph Completion
García-Durán, A., Dumančić, S., & Niepert, M. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1516 · Cited by 584 · see citing works
Composition-based Multi-Relational Graph Convolutional Networks
Vashishth, S., Sanyal, S., Nitin, V., & Talukdar, P. (2019) · arXiv · doi:10.48550/arxiv.1911.03082 · Cited by 581 · see citing works
Inductive Representation Learning on Temporal GraphsPreprint
Xu, D., Ruan, C., Körpeoğlu, E., Kumar, S., & Achan, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.07962 · Cited by 565 · see citing works
Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
Chen, M., Tian, Y., Yang, M., & Zaniolo, C. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/209 · Cited by 562 · see citing works
Holographic embeddings of knowledge graphs
Nickel, M., Rosasco, L., & Poggio, T. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10314 · Cited by 559 · see citing works
Bootstrapping Entity Alignment with Knowledge Graph Embedding
Sun, Z., Hu, W. P., Zhang, Q., & Qu, Y. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/611 · Cited by 559 · see citing works
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
Wang, H., Zhang, F., Zhao, M., Li, W., Xie, X., & Guo, M. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313411 · Cited by 559 · see citing works
A Frustratingly Easy Approach for Entity and Relation Extraction
Zhong, Z., & Chen, D. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.5 · Cited by 555 · see citing works
Attention Guided Graph Convolutional Networks for Relation Extraction
Guo, Z., Zhang, Y., & Lu, W. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1024 · Cited by 552 · see citing works
Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
Xian, Y., Fu, Z., Muthukrishnan, S., de Melo, G., & Zhang, Y. (2019) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3331184.3331203 · Cited by 546 · see citing works
A Frustratingly Easy Approach for Entity and Relation Extraction
Zhong, Z., & Chen, D. (2020) · arXiv · doi:10.48550/arxiv.2010.12812 · Cited by 531 · see citing works
HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding
Dasgupta, S. S., Ray, S. N., & Talukdar, P. P. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1225 · Cited by 530 · see citing works
Large-scale Simple Question Answering with Memory Networks
Bordes, A., Usunier, N., Chopra, S., & Weston, J. (2015) · arXiv · doi:10.48550/arxiv.1506.02075 · Cited by 528 · see citing works
(1959) · IFIP Congress · Cited by 514 · see citing works
Relation Extraction: Perspective from Convolutional Neural Networks
Nguyen, T. H., & Grishman, R. (2015) · Workshop on Vector Space Modeling for Natural Language Processing (VSM-NLP) · doi:10.3115/v1/w15-1506 · Cited by 507 · see citing works
Variational Reasoning for Question Answering With Knowledge Graph
Zhang, Y., Dai, H., Kozareva, Z., Smola, A. J., & Song, L. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12057 · Cited by 490 · see citing works
TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking
Wang, Y., Yu, B., Zhang, Y., Liu, T., Zhu, H., & Sun, L. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.138 · Cited by 490 · see citing works
Das, R., Dhuliawala, S., Zaheer, M., Vilnis, L., Durugkar, I., Krishnamurthy, A., Smola, A. J., & McCallum, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.05851 · Cited by 479 · see citing works
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
Lin, B. Y., Chen, X., Chen, J., & Ren, X. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1282 · Cited by 477 · see citing works
SimplE Embedding for Link Prediction in Knowledge Graphs
Kazemi, S. M., & Poole, D. (2018) · arXiv · doi:10.48550/arxiv.1802.04868 · Cited by 476 · see citing works
Cross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding
Sun, Z., Hu, W. P., & Li, C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68288-4_37 · Cited by 472 · see citing works
Knowledge Graph Completion with Adaptive Sparse Transfer Matrix
Ji, G., Liu, K., He, S., & Zhao, J. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10089 · Cited by 445 · see citing works
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
Zhang, Z., Cai, J., Zhang, Y., & Wang, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5701 · Cited by 443 · see citing works
Deriving Validity Time in Knowledge Graph
Leblay, J., & Chekol, M. W. (2018) · doi:10.1145/3184558.3191639 · Cited by 436 · see citing works
Multi-Hop Knowledge Graph Reasoning with Reward Shaping
Lin, X., Socher, R., & Xiong, C. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1362 · Cited by 428 · see citing works
Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding
Xiong, C., Power, R., & Callan, J. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052558 · Cited by 422 · see citing works
Differentiable Learning of Logical Rules for Knowledge Base ReasoningPreprint
Yang, F., Yang, Z., & Cohen, W. W. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.08367 · Cited by 414 · see citing works
SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings
Cambria, E., Poria, S., Hazarika, D., & Kwok, K. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11559 · Cited by 412 · see citing works
Self-citationTowards a Definition of Knowledge Graphs.
Ehrlinger, L., & Wöß, W. (2016) · Cited by 410 · see citing works
Entity-Relation Extraction as Multi-Turn Question Answering
Li, X., Yin, F., Sun, Z., Li, X., Yuan, A., Chai, D., Zhou, M., & Li, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1129 · Cited by 406 · see citing works
Knowledge Graph and Text Jointly Embedding
Wang, Z., Zhang, J., Feng, J., & Chen, Z. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1167 · Cited by 406 · see citing works
Computer-Based Medical Consultations: Mycin
(1976) · Elsevier eBooks · doi:10.1016/b978-0-444-00179-5.x5001-x · Cited by 402 · see citing works
Iterative Entity Alignment via Joint Knowledge Embeddings
Zhu, H., Xie, R., Liu, Z., & Sun, M. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/595 · Cited by 398 · see citing works
Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification
Gao, T., Han, X., Liu, Z., & Sun, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016407 · Cited by 396 · see citing works
Learning to Represent Knowledge Graphs with Gaussian Embedding
He, S., Liu, K., Ji, G., & Zhao, J. (2015) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2806416.2806502 · Cited by 395 · see citing works
Modeling Joint Entity and Relation Extraction with Table Representation
Miwa, M., & Sasaki, Y. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1200 · Cited by 390 · see citing works
Low-Dimensional Hyperbolic Knowledge Graph Embeddings
Chami, I., Wolf, A., Juan, D.-C., Sala, F., Ravi, S., & Ré, C. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.617 · Cited by 384 · see citing works
A latent factor model for highly multi-relational data
Jenatton, R., Roux, N. L., Bordes, A., & Obozinski, G. (2012) · Cited by 373 · see citing works
Quaternion Knowledge Graph EmbeddingsPreprint
Zhang, S., Tay, Y., Yao, L., & Liu, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.10281 · Cited by 372 · see citing works
TransG : A Generative Model for Knowledge Graph Embedding
Xiao, H., Huang, M., & Zhu, X. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1219 · Cited by 370 · see citing works
Factorizing YAGO: scalable machine learning for linked data
Nickel, M., Tresp, V., & Kriegel, H.-P. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187874 · Cited by 370 · see citing works
Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions
Ji, G., Liu, K., He, S., & Zhao, J. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10953 · Cited by 366 · see citing works
OpenKE: An Open Toolkit for Knowledge Embedding
Han, X., Cao, S., Lv, X., Lin, Y., Liu, Z., Sun, M., & Li, J. (2018) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/d18-2024 · Cited by 364 · see citing works
KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction
Lin, X., Quan, Z., Wang, Z., Ma, T., & Zeng, X. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/380 · Cited by 364 · see citing works
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge GraphsPreprint
Trivedi, R., Dai, H., Wang, Y., & Song, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.05742 · Cited by 362 · see citing works
Reinforcement Learning for Relation Classification From Noisy Data
Feng, J., Huang, M., Li, Z., Yang, Y., & Zhu, X. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12063 · Cited by 360 · see citing works
An Introduction to the Syntax and Content of Cyc i
Matuszek, C., Witbrock, M., Cabral, J., & DeOliveira, J. (2006) · doi:10.13016/m2j09w76t · Cited by 356 · see citing works
Deep Joint Entity Disambiguation with Local Neural Attention
Ganea, O.-E., & Hofmann, T. F. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1277 · Cited by 342 · see citing works
Entity Alignment between Knowledge Graphs Using Attribute Embeddings
Trisedya, B. D., Qi, J., & Zhang, R. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301297 · Cited by 322 · see citing works
Knowledge-Driven Encode, Retrieve, Paraphrase for Medical Image Report Generation
Li, C. Y., Liang, X., Hu, Z., & Xing, E. P. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016666 · Cited by 316 · see citing works
Combining Knowledge with Deep Convolutional Neural Networks for Short Text Classification
Wang, J., Wang, Z., Zhang, D., & Yan, J. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/406 · Cited by 313 · see citing works
OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs
Moon, S., Shah, P., Kumar, A., & Subba, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1081 · Cited by 306 · see citing works
One-Shot Relational Learning for Knowledge Graphs
Xiong, W., Yu, M., Chang, S., Guo, X., & Wang, W. Y. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1223 · Cited by 297 · see citing works
Analogical Inference for Multi-Relational EmbeddingsPreprint
Liu, H., Wu, Y., & Yang, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.02426 · Cited by 295 · see citing works
Open-World Knowledge Graph Completion
Shi, B., & Weninger, T. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11535 · Cited by 294 · see citing works
Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees
Katiyar, A., & Cardie, C. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1085 · Cited by 291 · see citing works
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
Cai, L., & Wang, W. Y. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1133 · Cited by 289 · see citing works
Image-embodied Knowledge Representation Learning
Xie, R., Liu, Z., Luan, H., & Sun, M. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/438 · Cited by 288 · see citing works
Moal, I. H., & Fernández-Recio, J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts489 · Cited by 281 · see citing works
A benchmarking study of embedding-based entity alignment for knowledge graphs
Sun, Z., Zhang, Q., Hu, W. P., Wang, C., Chen, M., Akrami, F., & Li, C. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3407790.3407828 · Cited by 275 · see citing works
Multi-view Knowledge Graph Embedding for Entity Alignment
Zhang, Q., Sun, Z., Hu, W. P., Chen, M., Guo, L., & Qu, Y. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/754 · Cited by 273 · see citing works
Jointly Embedding Knowledge Graphs and Logical Rules
Guo, S., Wang, Q., Wang, L., Wang, B., & Guo, L. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1019 · Cited by 265 · see citing works
LC-QuAD: A Corpus for Complex Question Answering over Knowledge Graphs
Trivedi, P., Maheshwari, G., Dubey, M., & Lehmann, J. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-68204-4_22 · Cited by 261 · see citing works
Representation learning of knowledge graphs with hierarchical types
Xie, R., Liu, Z., & Sun, M. (2016) · International Joint Conference on Artificial Intelligence · Cited by 257 · see citing works
Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning
Qin, P., Xu, W., & Wang, W. Y. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1199 · Cited by 246 · see citing works
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
Zhang, N., Deng, S., Sun, Z., Wang, G., Chen, X., Zhang, W., & Chen, H. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1306 · Cited by 244 · see citing works
Compositional Vector Space Models for Knowledge Base Completion
Neelakantan, A., Roth, B., & McCallum, A. (2015) · doi:10.3115/v1/p15-1016 · Cited by 242 · see citing works
Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks
Das, R., Neelakantan, A., Belanger, D., & McCallum, A. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1013 · Cited by 239 · see citing works
Improving Entity Linking by Modeling Latent Relations between Mentions
Le, P., & Titov, I. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1148 · Cited by 231 · see citing works
Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs
Chen, M., Zhang, W., Zhang, W., Chen, Q., & Chen, H. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1431 · Cited by 230 · see citing works
Adversarial Training for Relation Extraction
Wu, Y., Bamman, D., & Russell, S. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1187 · Cited by 229 · see citing works
A Hierarchical Framework for Relation Extraction with Reinforcement Learning
Takanobu, R., Zhang, T., Liu, J., & Huang, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017072 · Cited by 229 · see citing works
Chen, M., Tian, Y., Chang, K.-W., Skiena, S., & Zaniolo, C. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/556 · Cited by 229 · see citing works
Hypernetwork Knowledge Graph Embeddings
Balažević, I., Allen, C., & Hospedales, T. M. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-30493-5_52 · Cited by 229 · see citing works
Discovering protein drug targets using knowledge graph embeddings
Mohamed, S. K., Nováček, V., & Nounu, A. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz600 · Cited by 227 · see citing works
DKN: Deep Knowledge-Aware Network for News RecommendationPreprint
Wang, H., Zhang, F., Xie, X., & Guo, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.08284 · Cited by 227 · see citing works
Bidirectional Recurrent Convolutional Neural Network for Relation Classification
Cai, R., Zhang, X., & Wang, H. (2016) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/p16-1072 · Cited by 226 · see citing works
Knowledge Graph Embedding With Iterative Guidance From Soft Rules
Guo, S., Wang, Q., Wang, L., Wang, B., & Guo, L. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11918 · Cited by 223 · see citing works
Cognitive Graph for Multi-Hop Reading Comprehension at Scale
Ding, M., Zhou, C., Chen, Q., Yang, H., & Tang, J. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1259 · Cited by 222 · see citing works
ProjE: Embedding Projection for Knowledge Graph Completion
Shi, B., & Weninger, T. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10677 · Cited by 218 · see citing works
An upper‐level ontology for the biomedical domain
McCray, A. T. (2003) · Comparative and Functional Genomics · doi:10.1002/cfg.255 · Cited by 216 · see citing works
Few-Shot Knowledge Graph Completion
Zhang, C., Yao, H., Huang, C., Jiang, M., Li, Z., & Chawla, N. V. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5698 · Cited by 213 · see citing works
TorusE: Knowledge Graph Embedding on a Lie Group
Ebisu, T., & Ichise, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11538 · Cited by 212 · see citing works
Learning to Exploit Long-term Relational Dependencies in Knowledge GraphsPreprint
Guo, L., Sun, Z., & Hu, W. P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.04914 · Cited by 201 · see citing works
Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention
Gaur, M., Alambo, A., Sain, J. P., Kursuncu, U., Thirunarayan, K., Kavuluru, R., Sheth, A., Welton, R., & Pathak, J. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313698 · Cited by 193 · see citing works
Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases
Gardner, M., Talukdar, P., Krishnamurthy, J., & Mitchell, T. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1044 · Cited by 189 · see citing works
Text Generation from Knowledge Graphs with Graph Transformers
Koncel-Kedziorski, R., Bekal, D., Luan, Y., Lapata, M., & Hajishirzi, H. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1238 · Cited by 189 · see citing works
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
Zhang, W., Paudel, B., Wang, L., Chen, J., Zhu, H. J., Zhang, W., Bernstein, A., & Chen, H. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313612 · Cited by 186 · see citing works
CoLAKE: Contextualized Language and Knowledge Embedding
Sun, T., Shao, Y., Qiu, X., Guo, Q., Hu, Y., Huang, X., & Zhang, Z. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.327 · Cited by 185 · see citing works
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Zhang, W., Paudel, B., Zhang, W., Bernstein, A., & Chen, H. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3291014 · Cited by 180 · see citing works
Tensor Decompositions for temporal knowledge base completionPreprint
Lacroix, T., Obozinski, G., & Usunier, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.04926 · Cited by 177 · see citing works
SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions
Xiao, H., Huang, M., Lian, M., & Zhu, X. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10952 · Cited by 175 · see citing works
Commonsense for Generative Multi-Hop Question Answering Tasks
Bauer, L., Wang, Y., & Bansal, M. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1454 · Cited by 168 · see citing works
DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction
Qin, P., Xu, W., & Wang, W. Y. (2018) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/p18-1046 · Cited by 168 · see citing works
Barack’s Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling
Logan, R., Liu, N. F., Peters, M. E., Gardner, M., & Singh, S. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1598 · Cited by 168 · see citing works
Neural Knowledge Acquisition via Mutual Attention Between Knowledge Graph and Text
Han, X., Liu, Z., & Sun, M. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11927 · Cited by 164 · see citing works
Deductive Question-Answering on Relational Data Bases
Reiter, R. (1978) · Springer eBooks · doi:10.1007/978-1-4684-3384-5_6 · Cited by 161 · see citing works
Hao, J., Chen, M., Yu, W., Sun, Y., & Wang, W. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330838 · Cited by 160 · see citing works
Encoding Temporal Information for Time-Aware Link Prediction
Jiang, T., Liu, T., Ge, T., Sha, L., Li, S., Chang, B., & Sui, Z. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1260 · Cited by 154 · see citing works
Semantically Smooth Knowledge Graph Embedding
Guo, S., Wang, Q., Wang, B., Wang, L., & Guo, L. (2015) · doi:10.3115/v1/p15-1009 · Cited by 153 · see citing works
Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention
Han, X., Yu, P., Liu, Z., Sun, M., & Li, P. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1247 · Cited by 152 · see citing works
Self-citationOpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction
Han, X., Gao, T., Yao, Y., Ye, D., Liu, Z., & Sun, M. (2019) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/d19-3029 · Cited by 152 · see citing works
ProjE: Embedding Projection for Knowledge Graph CompletionPreprint
Shi, B., & Weninger, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.05425 · Cited by 150 · see citing works
Transition-based Knowledge Graph Embedding with Relational Mapping Properties
Fan, M., Zhou, Q., Chang, E. J., & Zheng, T. F. (2014) · Institutional Repositories DataBase (IRDB) · Cited by 148 · see citing works
Joint Extraction of Entities and Overlapping Relations Using Position-Attentive Sequence Labeling
Dai, D., Xiao, X., Lyu, Y., Dou, S., She, Q., & Wang, H. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016300 · Cited by 148 · see citing works
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
Ren, X., He, W., Qu, M., Voss, C. R., Ji, H., & Han, J. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939822 · Cited by 147 · see citing works
SimplE embedding for link prediction in knowledge graphs
Kazemi, S. M., & Poole, D. (2018) · neural information processing systems · Cited by 147 · see citing works
CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases
Dai, Z., Li, L., & Xu, W. H. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1076 · Cited by 145 · see citing works
Attention-Based Convolutional Neural Network for Semantic Relation Extraction
Shen, Y., & Huang, X. (2016) · International Conference on Computational Linguistics · Cited by 143 · see citing works
Embedding Uncertain Knowledge Graphs
Chen, X., Chen, M., Shi, W., Sun, Y., & Zaniolo, C. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013363 · Cited by 132 · see citing works
Towards Time-Aware Knowledge Graph Completion
Jiang, T., Liu, T., Ge, T., Sha, L., Chang, B., Li, S., & Sui, Z. (2016) · International Conference on Computational Linguistics · Cited by 129 · see citing works
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases
Chen, Y., Wu, L., & Zaki, M. J. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1299 · Cited by 129 · see citing works
Multi-relational Poincaré Graph Embeddings
Balažević, I., Allen, C., & Hospedales, T. (2019) · arXiv · doi:10.48550/arxiv.1905.09791 · Cited by 123 · see citing works
He, S., Liu, C., Liu, K., & Zhao, J. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1019 · Cited by 120 · see citing works
TransA: An Adaptive Approach for Knowledge Graph EmbeddingPreprint
Xiao, H., Huang, M., Hao, Y., & Zhu, X. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.05490 · Cited by 118 · see citing works
Deep Residual Learning for Weakly-Supervised Relation Extraction
Huang, Y. Y., & Wang, W. Y. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1191 · Cited by 118 · see citing works
End-to-End Differentiable ProvingPreprint
Rocktäschel, T., & Riedel, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.11040 · Cited by 117 · see citing works
Variational Knowledge Graph Reasoning
Chen, W., Xiong, W., Yan, X., & Wang, W. Y. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1165 · Cited by 117 · see citing works
Label Embedding for Zero-shot Fine-grained Named Entity Typing.
Ma, Y., Cambria, E., & Gao, S. (2016) · International Conference on Computational Linguistics · Cited by 116 · see citing works
Self-citationProbabilistic Logic Neural Networks for ReasoningPreprint
Qu, M., & Tang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.08495 · Cited by 116 · see citing works
An Interpretable Knowledge Transfer Model for Knowledge Base Completion
Xie, Q., Ma, X., Dai, Z., & Hovy, E. H. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1088 · Cited by 109 · see citing works
Mohammed, S., Shi, P., & Lin, J. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-2047 · Cited by 109 · see citing works
Embedding models for episodic knowledge graphs
Ma, Y., Tresp, V., & Daxberger, E. (2018) · Journal of Web Semantics · doi:10.1016/j.websem.2018.12.008 · Cited by 108 · see citing works
Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks
Peng, H. M., Li, J., Gong, Q., Song, Y., Ning, Y., Lai, K., & Yu, P. S. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/449 · Cited by 108 · see citing works
Self-citationSemantic Multidimensional Scaling for Open-Domain Sentiment Analysis
Cambria, E., Song, Y., Wang, H., & Howard, N. (2013) · IEEE Intelligent Systems · doi:10.1109/mis.2012.118 · Cited by 106 · see citing works
Self-citationM-Walk: Learning to Walk over Graphs using Monte Carlo Tree SearchPreprint
Shen, Y., Chen, J., Huang, P.-S., Guo, Y., & Gao, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04394 · Cited by 104 · see citing works
Flagged first, then by citations
4 of the 272 references couldn’t be matched to a known paper.
Research works printed but not linked: 7
- S. Zhang, Y. Tay, L. Yao, and Q. Liu, "Quaternion knowledge graph embedding," in NeurIPS, 2019, pp. 2731-2741.
- A. Bordes, X. Glorot, J. Weston, and Y. Bengio, "A semantic matching energy function for learning with multi-relational data," Machine Learning, vol. 94, no. 2, pp. 233-259, 2014.
- W. Wu, H. Li, H. Wang, and K. Q. Zhu, "Probase: A probabilistic taxonomy for text understanding," in SIGMOD, 2012, pp. 481-492.
- L. A. Galárraga, C. Teflioudi, K. Hose, and F. Suchanek, "AMIE: association rule mining under incomplete evidence in ontological knowledge bases," in WWW, 2013, pp. 413-422.
- X. Wang, X. He, Y. Cao, M. Liu, and T.-S. Chua, "KGAT: Knowledge graph attention network for recommendation," in SIGKDD, 2019, pp. 950-958.
- F. M. Suchanek, G. Kasneci, and G. Weikum, "Yago: a core of semantic knowledge," in WWW, 2007, pp. 697-706.
- D. Vrandečić and M. Krötzsch, "Wikidata: a free collaborative knowl- edge base," Communications of the ACM, vol. 57, no. 10, pp. 78-85, 2014.
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Wang, Y., Chen, M., Luo, T., Saad, W., Niyato, D. T., Poor, H. V., & Cui, S. R. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3191112 · Cited by 227 · see citing works
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- Authors
- Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S.
- Affiliations
- Aalto University (FI)
- Monash University (AU)
- Nanyang Technological University (SG)
- University of Illinois Chicago (US)
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2022-02
- Volume
- 33
- Issue
- 2
- Pages
- 494-514
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W3003265726
- Cited by
- 2,947
- References
- 272
- Citations by year
- 2026: 514 · 2025: 623 · 2024: 588 · 2023: 566 · 2022: 364 · 2021: 222 · 2020: 69 · 2012: 1
- FWCI
- 153.72
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
- PubMed (publishedVersion)
- Aaltodoc (Aalto University) (submittedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- DR-NTU (Nanyang Technological University) (submittedVersion)
- Griffith Research Online (submittedVersion)
- arXiv (Cornell University)
- Funders
- NSF (III-1763325, III-1909323, SaTC-1930941)
- Agency for Science, Technology and Research (A*STAR) through its AME Programmatic Funding Scheme (A18A2b0046)
- Academy of Finland (336033, 315896)
- Business Finland (884/31/2018)
- EU H2020 (101016775)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- knowledge graphs, knowledge graph embedding, knowledge acquisition, knowledge graph completion, temporal knowledge graphs, commonsense reasoning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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