Modeling Relation Paths for Representation Learning of Knowledge Bases
Open accessAbstract
Representation learning of knowledge bases aims to embed both entities and relations into a low-dimensional space.Most existing methods only consider direct relations in representation learning.We argue that multiple-step relation paths also contain rich inference patterns between entities, and propose a path-based representation learning model.This model considers relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all relation paths are reliable, we design a path-constraint resource allocation algorithm to measure the reliability of relation paths.(2) We represent relation paths via semantic composition of relation embeddings.Experimental results on real-world datasets show that, as compared with baselines, our model achieves significant and consistent improvements on knowledge base completion and relation extraction from text.The source code of this paper can be obtained from https://github.com/mrlyk423/relation_extraction.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
PTransE introduces contextual information by using multiple-step relation paths to extend TransE.
“PTransE [83] is a translation-based model, and it introduces contextual information by using multiple-step relation paths to extend TransE, which treats relation paths as transformations of representation learning between entities.”
From A Survey on Knowledge Graph Embeddings for Link Prediction (2021) · cites this paper as [83]
Using inverse of relations to address the independence of the entity vectors in CP is a novel contribution.
“While inverse of relations has been used for other purposes (see e.g., [20, 21, 6]), using them to address the independence of the entity vectors in CP is a novel contribution.”
From SimplE Embedding for Link Prediction in Knowledge Graphs (2018) · cites this paper as [21]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390 · see citing works
Flagged first, then by citations
Research works printed but not linked: 31
- Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008. Freebase: a col- laboratively created graph database for structuring human knowledge. In Proceedings of KDD, pages 1247-1250.
- Antoine Bordes, Jason Weston, Ronan Collobert, Yoshua Bengio, et al. 2011. Learning structured embeddings of knowledge bases. In Proceedings of AAAI, pages 301-306.
- Antoine Bordes, Xavier Glorot, Jason Weston, and Yoshua Bengio. 2012. Joint learning of words and meaning representations for open-text seman- tic parsing. In Proceedings of AISTATS, pages 127- 135.
- Antoine Bordes, Nicolas Usunier, Alberto Garcia- Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi- relational data. In Proceedings of NIPS, pages 2787-2795.
- Antoine Bordes, Xavier Glorot, Jason Weston, and Yoshua Bengio. 2014. A semantic matching en- ergy function for learning with multi-relational data. Machine Learning, 94(2):233-259.
- Danqi Chen, Richard Socher, Christopher D Manning, and Andrew Y Ng. 2013. Learning new facts from knowledge bases with neural tensor networks and semantic word vectors. Proceedings of ICLR.
- Matt Gardner, Partha Pratim Talukdar, Bryan Kisiel, and Tom M Mitchell. 2013. Improving learning and inference in a large knowledge-base using latent syntactic cues. In Proceedings of EMNLP, pages 833-838.
- Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, and Daniel S Weld. 2011. Knowledge- based weak supervision for information extraction of overlapping relations. In Proceedings of ACL- HLT, pages 541-550.
- Rodolphe Jenatton, Nicolas L Roux, Antoine Bordes, and Guillaume R Obozinski. 2012. A latent factor model for highly multi-relational data. In Proceed- ings of NIPS, pages 3167-3175.
- Charles Kemp, Joshua B Tenenbaum, Thomas L Grif- fiths, Takeshi Yamada, and Naonori Ueda. 2006. Learning systems of concepts with an infinite rela- tional model. In Proceedings of AAAI, volume 3, page 5.
- Ni Lao and William W Cohen. 2010. Relational re- trieval using a combination of path-constrained ran- dom walks. Machine learning, 81(1):53-67.
- Ni Lao, Tom Mitchell, and William W Cohen. 2011. Random walk inference and learning in a large scale knowledge base. In Proceedings of EMNLP, pages 529-539.
- Ni Lao, Amarnag Subramanya, Fernando Pereira, and William W Cohen. 2012. Reading the web with learned syntactic-semantic inference rules. In Pro- ceedings of EMNLP-CoNLL, pages 1017-1026.
- Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation em- beddings for knowledge graph completion. In Pro- ceedings of AAAI, pages 2181-2187.
- Linyuan Lü and Tao Zhou. 2011. Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications, 390(6):1150-1170.
- Kurt Miller, Michael I Jordan, and Thomas L Griffiths. 2009. Nonparametric latent feature models for link prediction. In Proceedings of NIPS, pages 1276- 1284.
- Mike Mintz, Steven Bills, Rion Snow, and Dan Juraf- sky. 2009. Distant supervision for relation extrac- tion without labeled data. In Proceedings of ACL- IJCNLP, pages 1003-1011.
- Jeff Mitchell and Mirella Lapata. 2008. Vector-based models of semantic composition. In Proceedings of ACL, pages 236-244.
- Arvind Neelakantan, Benjamin Roth, and Andrew Mc- Callum. 2015. Compositional vector space models for knowledge base inference. In 2015 AAAI Spring Symposium Series.
- Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2011. A three-way model for collective learning on multi-relational data. In Proceedings of ICML, pages 809-816.
- Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2012. Factorizing yago: scalable machine learning for linked data. In Proceedings of WWW, pages 271-280.
- Sebastian Riedel, Limin Yao, and Andrew McCallum. 2010. Modeling relations and their mentions with- out labeled text. In Proceedings of ECML-PKDD, pages 148-163.
- Ajit P Singh and Geoffrey J Gordon. 2008. Relational learning via collective matrix factorization. In Pro- ceedings of KDD, pages 650-658.
- Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng. 2013. Reasoning with neural ten- sor networks for knowledge base completion. In Proceedings of NIPS, pages 926-934.
- Mihai Surdeanu, Julie Tibshirani, Ramesh Nallapati, and Christopher D Manning. 2012. Multi-instance multi-label learning for relation extraction. In Pro- ceedings of EMNLP, pages 455-465.
- Ilya Sutskever, Joshua B Tenenbaum, and Ruslan Salakhutdinov. 2009. Modelling relational data us- ing bayesian clustered tensor factorization. In Pro- ceedings of NIPS, pages 1821-1828.
- Hanghang Tong, Christos Faloutsos, and Jia-Yu Pan. 2006. Fast random walk with restart and its applica- tions. In Proceedings of ICDM, pages 613-622.
- Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge graph embedding by trans- lating on hyperplanes. In Proceedings of AAAI, pages 1112-1119.
- Jason Weston, Antoine Bordes, Oksana Yakhnenko, and Nicolas Usunier. 2013. Connecting language and knowledge bases with embedding models for re- lation extraction. In Proceedings of EMNLP, pages 1366-1371.
- Tao Zhou, Jie Ren, Matúš Medo, and Yi-Cheng Zhang. 2007. Bipartite network projection and personal rec- ommendation. Physical Review E, 76(4):046115.
- Jun Zhu. 2012. Max-margin nonparametric latent fea- ture models for link prediction. In Proceedings of ICML, pages 719-726.
Cited by
Sorted by citations
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
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
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
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
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
Self-citationKnowledge Graph Embedding for Link Prediction
Rossi, A., Barbosa, D., Firmani, D., Matinata, A., & Merialdo, P. (2021) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3424672 · Cited by 601 · see citing works
“PTransE [34] or, in our analysis, RSN [19]; some models do not employ paths directly in training but use them for additional tasks, as the explanation approach proposed by CrossE [72].”
MethodSimplE 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
“, [20, 21, 6]), using them to address the independence of the entity vectors in CP is a novel contribution.”
BackgroundA Survey on Knowledge Graph Embeddings for Link Prediction
Wang, M., Qiu, L., & Wang, X. (2021) · Symmetry · doi:10.3390/sym13030485 · Cited by 212 · see citing works
INK: knowledge graph embeddings for node classification
Steenwinckel, B., Vandewiele, G., Weyns, M., Agozzino, T., Turck, F. D., & Ongenae, F. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-021-00806-z · Cited by 35 · see citing works
Explainable knowledge graph embeddings for industrial process monitoring & control
Weyns, M., Blyau, T., Steenwinckel, B., De Turck, F., Van Hoecke, S., & Ongenae, F. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103242 · Cited by 7 · see citing works
- Authors
- Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., & Liu, S.
- Affiliations
- State Key Laboratory of Intelligent Technology and Systems (CN)
- Samsung (South Korea) (KR)
- Venue
- Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
- Published
- 2015
- Pages
- 705-714
- OpenAlex
- W1426956448
- Cited by
- 657
- References
- 1
- Citations by year
- 2026: 10 · 2025: 40 · 2024: 56 · 2023: 75 · 2022: 54 · 2021: 82 · 2020: 84 · 2019: 112 · 2018: 76 · 2017: 35 · 2016: 26 · 2015: 6 · 2012: 1
- FWCI
- 26.86
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (973 Program, 61133012, 61303075)
- Samsung
- National Key Research and Development Program of China (2014CB340501)
- 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
- entity embeddings, relation embeddings, path reliability, resource allocation, semantic composition, knowledge base completion
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 ›
Are you an author, or do you see a problem with this page? Request a review
