The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Relational retrieval using a combination of path-constrained random walks
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Citations
Cites
Introduction to Statistical Relational Learning
(2007) · doi:10.7551/mitpress/7432.001.0001 · Cited by 1,643
Cited by
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070843 · Cited by 2,947
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
A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities
Zhou, X., & Zafarani, R. (2021) · ACM Computing Surveys · doi:10.1145/3395046 · Cited by 1,184
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
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
Modeling Relation Paths for Representation Learning of Knowledge Bases
Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., & Liu, S. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1082 · Cited by 657
Knowledge 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
“Some methods are based on observable features and employ techniques such as Rule Mining [17][16][37][24] or the Path Ranking Algorithm [31][32] to identify missing triples in the graph.”
MethodSimplE Embedding for Link Prediction in Knowledge Graphs
Kazemi, S. M., & Poole, D. (2018) · arXiv · doi:10.48550/arxiv.1802.04868 · Cited by 476
“versa. SimplE takes advantage of the inverse of relations to address the independence of the two vectors for each entity in CP. 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. Model Definitio…”
MethodRepresentation learning for dynamic graphs: A survey
Kazemi, S. M., Goel, R., Jain, K., Kobyzev, I., Sethi, A., Forsyth, P., & Poupart, P. (2019) · arXiv · doi:10.48550/arxiv.1905.11485 · Cited by 338
Diachronic Embedding for Temporal Knowledge Graph Completion
Goel, R., Kazemi, S. M., Brubaker, M., & Poupart, P. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5815 · Cited by 282
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
Modeling Relation Paths for Knowledge Graph Completion
Shen, Y., Ding, N., Zheng, H.-T., Li, Y., & Yang, M. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2970044 · Cited by 153
(2014) · Cited by 66
Structure Learning via Parameter Learning
Wang, W. Y., Mazaitis, K., & Cohen, W. W. (2014) · Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management · doi:10.1145/2661829.2662022 · Cited by 47
Self-citationContext-aware Path Ranking for Knowledge Base Completion
Mazumder, S., & Liu, B. (2017) · Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/166 · Cited by 35
Modeling Paths for Explainable Knowledge Base Completion
Stadelmaier, J., & Padó, S. (2019) · Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w19-4816 · Cited by 12
Implicit Relation Linking for Question Answering over Knowledge Graph
Zhao, Y., Huang, J., Hu, W., Chen, Q., Qiu, X., Huo, C., & Ren, W. (2022) · Findings of the Association for Computational Linguistics: ACL 2022 · doi:10.18653/v1/2022.findings-acl.312 · Cited by 5
Capturing Semantic and Syntactic Information for Link Prediction in Knowledge Graphs
Wang, C., Yan, M., Yi, C., & Sha, Y. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-30793-6_38 · Cited by 4
Approaches for cleaning assertion errors in knowledge graphs
Sommer, D., & Serles, U. (2026) · Journal of Web Semantics · doi:10.1016/j.websem.2026.100888
Metadata
- Authors
- Lao, N., & Cohen, W. W.
- Venue
- Machine Learning
- Published
- 2010-07-22
- Volume
- 81
- Issue
- 1
- Pages
- 53-67
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2029249040
- Cited by
- 614
- References
- 1
- FWCI
- 22.59
- Type
- journal article
- Language
- en
- Open access
- bronze
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
