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The Foundations for Provenance on the Web
Moreau, L. (2010) · Foundations and Trends® in Web Science · doi:10.1561/1800000010 · Cited by 168
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Unifying Large Language Models and Knowledge Graphs: A Roadmap
Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., & Wu, X. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3352100 · Cited by 1,376
Knowledge graph refinement: A survey of approaches and evaluation methods
Paulheim, H. (2016) · Semantic Web · doi:10.3233/sw-160218 · Cited by 849
Knowledge Enhanced Contextual Word Representations
Peters, M. E., Neumann, M., Logan, R., Schwartz, R., Joshi, V., Singh, S., & Smith, N. 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/d19-1005 · Cited by 661
StereoSet: Measuring stereotypical bias in pretrained language models
Nadeem, M., Bethke, A., & Reddy, S. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.416 · Cited by 643
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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
“Examples of notable KGs are FreeBase [7], WikiData [62], DBPedia [4], Yago [54] and – in industry – Google KG [52], Microso Satori [48] and Facebook Graph Search [53].”
BackgroundTemporal Relational Ranking for Stock Prediction
Feng, F., He, X., Wang, X., Luo, C., Liu, Y., & Chua, T.-S. (2019) · ACM Transactions on Information Systems · doi:10.1145/3309547 · Cited by 553
DocRED: A Large-Scale Document-Level Relation Extraction Dataset
Yao, Y., Ye, D., Li, P., Han, X., Lin, Y., Liu, Z., Liu, Z., Huang, L., Zhou, J., & Sun, M. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1074 · Cited by 550
Veale, M., & Binns, R. (2017) · Big Data & Society · doi:10.1177/2053951717743530 · Cited by 512
Veale, M., & Binns, R. (2017) · doi:10.31235/osf.io/ustxg · Cited by 512
Deriving Validity Time in Knowledge Graph
Leblay, J., & Chekol, M. W. (2018) · Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18 · doi:10.1145/3184558.3191639 · Cited by 365
Bulathwela, S., Pérez-Ortiz, M., Holloway, C., Cukurova, M., & Shawe-Taylor, J. (2024) · Sustainability · doi:10.3390/su16020781 · Cited by 269
Introduction: What is a knowledge graph?
Fensel, D., Şimşek, U., Angele, K., Huaman, E., Kärle, E., Panasiuk, O., Toma, I., Umbrich, J., & Wahler, A. (2020) · Knowledge Graphs · doi:10.1007/978-3-030-37439-6_1 · Cited by 244
Time-aware language models as temporal knowledge bases
Dhingra, B., Cole, J. R., Eisenschlos, J. M., Gillick, D., Eisenstein, J., & Cohen, W. W. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00459 · Cited by 212
Graph Retrieval-Augmented Generation: A Survey
Peng, B., Zhu, Y., Liu, Y., Bo, X., Shi, H., Hong, C., Zhang, Y., & Tang, S. (2026) · ACM Transactions on Information Systems · doi:10.1145/3777378 · Cited by 206
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
Assessing The Factual Accuracy of Generated Text
Goodrich, B., Rao, V., Liu, P. J., & Saleh, M. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330955 · Cited by 162
Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training
Agarwal, O., Ge, H., Shakeri, S., & Al-Rfou, R. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.278 · Cited by 161
A comprehensive survey on relation extraction: recent advances and new frontiers
Zhao, X., Deng, Y., Yang, M., Wang, L., Zhang, R., Cheng, H., Lam, W., Shen, Y., & Xu, R. (2024) · ACM Computing Surveys · doi:10.1145/3674501 · Cited by 168
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
Sentence Embedding Alignment for Lifelong Relation Extraction
Wang, H., Xiong, W., Yu, M., Guo, X., Chang, S., & Wang, W. Y. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1086 · Cited by 127
Rethinking search: making domain experts out of dilettantes
Metzler, D., Tay, Y., Bahri, D., & Najork, M. (2021) · ACM SIGIR Forum · doi:10.1145/3476415.3476428 · Cited by 112
Knowledge Graph Quality Management: a Comprehensive Survey
Xue, B., & Zou, L. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3150080 · Cited by 108
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Sciavolino, C., Zhong, Z., Lee, J., & Chen, D. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.496 · Cited by 98
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Longpre, S., Perisetla, K., Chen, A., Ramesh, N., DuBois, C., & Singh, S. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.565 · Cited by 101
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Tan, Q., Xu, L., Bing, L., Ng, H. T., & Aljunied, S. M. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.580 · Cited by 94
Luo, H., E, H., Tang, Z., Peng, S., Guo, Y., Zhang, W., Ma, C., Dong, G., Song, M., Lin, W., et al. (2024) · Findings of the Association for Computational Linguistics ACL 2024 · doi:10.18653/v1/2024.findings-acl.122 · Cited by 88
Knowledge Editing for Large Language Models: A Survey
Wang, S., Zhu, Y., Liu, H., Zheng, Z., Chen, C., & Li, J. (2025) · ACM Computing Surveys · doi:10.1145/3698590 · Cited by 79
Heinzerling, B., & Inui, K. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.153 · Cited by 78
A Survey on Temporal Knowledge Graph: Representation Learning and Applications
Cai, L., Mao, X., Zhou, Y., Long, Z., Wu, C., & Lan, M. (2024) · arXiv · doi:10.48550/arxiv.2403.04782 · Cited by 68
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Huszár, F., Ktena, S. I., O’Brien, C., Belli, L., Schlaikjer, A., & Hardt, M. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025334119 · Cited by 65
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Krishna, A., Riedel, S., & Vlachos, A. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00503 · Cited by 51
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Baek, J., Aji, A., & Saffari, A. (2023) · Proceedings of the First Workshop on Matching From Unstructured and Structured Data (MATCHING 2023) · doi:10.18653/v1/2023.matching-1.7 · Cited by 51
“, Poseidon) in Figure 1 in the Wikidata KG (Vrandecic and Krötzsch, 2014), there exist 60 triples, and most of them (e.”
BackgroundMethodInfluentialKnowledge graphs: Introduction, history, and perspectives
Chaudhri, V. K., Baru, C., Chittar, N., Dong, X. L., Genesereth, M., Hendler, J., Kalyanpur, A., Lenat, D. B., Sequeda, J., Vrandečić, D., et al. (2022) · AI Magazine · doi:10.1002/aaai.12033 · Cited by 71
Self-citationOn the Evolution of Knowledge Graphs: A Survey and Perspective
Jiang, X., Xu, C., Shen, Y., Sun, X., Tang, L., Wang, S., Chen, Z., Wang, Y., & Guo, J. (2023) · arXiv · doi:10.48550/arxiv.2310.04835 · Cited by 42
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Chen, W., Wang, X., & Wang, W. Y. (2021) · arXiv · doi:10.48550/arxiv.2108.06314 · Cited by 40
Sarkar, S., Gaur, M., Chen, L. K., Garg, M., & Srivastava, B. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1229805 · Cited by 38
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
OpenBiodiv: A Knowledge Graph for Literature-Extracted Linked Open Data in Biodiversity Science
Penev, L., Dimitrova, M., Senderov, V., Zhelezov, G., Georgiev, T., Stoev, P., & Simov, K. (2019) · Publications · doi:10.3390/publications7020038 · Cited by 34
Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED
Huang, Q., Hao, S., Ye, Y., Zhu, S., Feng, Y., & Zhao, D. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.432 · Cited by 28
Automated clinical knowledge graph generation framework for evidence based medicine.
Alam, F., Giglou, H. B., & Malik, K. M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120964 · Cited by 27
A Semantic Parsing and Reasoning-Based Approach to Knowledge Base Question Answering
Abdelaziz, I., Ravishankar, S., Kapanipathi, P., Roukos, S., & Gray, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i18.17988 · Cited by 26
Kraft, A., & Soulier, E. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658981 · Cited by 25
Entity Enabled Relation Linking
Pan, J. Z., Zhang, M., Singh, K., Harmelen, F. V., Gu, J., & Zhang, Z. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-30793-6_30 · Cited by 20
Hendawi, R., Alian, S., & Li, J. (2022) · JMIR Formative Research · doi:10.2196/35069 · Cited by 19
The Life Cycle of Knowledge in Big Language Models: A Survey
Cao, B., Lin, H., Han, X., & Sun, L. (2023) · arXiv · doi:10.48550/arxiv.2303.07616 · Cited by 18
The Life Cycle of Knowledge in Big Language Models: A Survey
Cao, B., Lin, H., Han, X., & Sun, L. (2024) · Machine Intelligence Research · doi:10.1007/s11633-023-1416-x · Cited by 18
Towards Temporal Knowledge Graph Embeddings with Arbitrary Time Precision
Leblay, J., Chekol, M. W., & Liu, X. (2020) · Proceedings of the 29th ACM International Conference on Information & Knowledge Management · doi:10.1145/3340531.3412028 · Cited by 15
Extracting Semantic Relations for Scholarly Knowledge Base Construction
Al-Zaidy, R. A., & Giles, C. L. (2018) · 2018 IEEE 12th International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc.2018.00017 · Cited by 15
Message Passing for Complex Question Answering over Knowledge Graphs
Vakulenko, S., Fernandez Garcia, J. D., Polleres, A., de Rijke, M., & Cochez, M. (2019) · Proceedings of the 28th ACM International Conference on Information and Knowledge Management · doi:10.1145/3357384.3358026 · Cited by 14
Quantifying gender bias towards politicians in cross-lingual language models
Stańczak, K., Ray Choudhury, S., Pimentel, T., Cotterell, R., & Augenstein, I. (2023) · PLOS ONE · doi:10.1371/journal.pone.0277640 · Cited by 13
El Morr, C., Maret, P., Muhlenbach, F., Dharmalingam, D., Tadesse, R., Creighton, A., Kundi, B., Buettgen, A., Mgwigwi, T., Dinca-Panaitescu, S., et al. (2021) · JMIR Formative Research · doi:10.2196/33335 · Cited by 12
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
Using knowledge graphs and behaviour trees for feedback-aware presentation agents,
Axelsson, N., & Skantze, G. (2020) · Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents · doi:10.1145/3383652.3423884 · Cited by 11
Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases
Cao, B., Lin, H., Han, X., Sun, L., Yan, L., Liao, M., Xue, T., & Xu, J. (2021) · arXiv · doi:10.48550/arxiv.2106.09231 · Cited by 10
Creating a Scholarly Knowledge Graph from Survey Article Tables
Oelen, A., Stocker, M., & Auer, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-64452-9_35 · Cited by 9
Completeness, Recall, and Negation in Open-world Knowledge Bases: A Survey
Razniewski, S., Arnaout, H., Ghosh, S., & Suchanek, F. (2024) · ACM Computing Surveys · doi:10.1145/3639563 · Cited by 8
TeCre: A Novel Temporal Conflict Resolution Method Based on Temporal Knowledge Graph Embedding
Ma, J., Zhou, C., Chen, Y., Wang, Y., Hu, G., & Qiao, Y. (2023) · Information · doi:10.3390/info14030155 · Cited by 7
Credible Intervals for Knowledge Graph Accuracy Estimation
Marchesin, S., & Silvello, G. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3725279 · Cited by 7
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
Building a large gene expression-cancer knowledge base with limited human annotations
Marchesin, S., Menotti, L., Giachelle, F., Silvello, G., & Alonso, O. (2023) · Database · doi:10.1093/database/baad061 · Cited by 4
Time Travel with the BiTemporal RDF Model
Tansel, A. U., Wu, D., & Wang, H.-T. (2025) · Mathematics · doi:10.3390/math13132109 · Cited by 3
REMem: Reasoning with Episodic Memory in Language Agent
Shu, Y., Jonnalagedda, S. P., Gao, X., Gutiérrez, B. J., Qi, W., Das, K., Sun, H., & Su, Y. (2026) · arXiv · doi:10.48550/arxiv.2602.13530 · Cited by 1
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Hase, P., Hofweber, T., Zhou, X., Stengel-Eskin, E., & Bansal, M. (2024) · arXiv · doi:10.48550/arxiv.2406.19354 · Cited by 1
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Barmettler, J., Bernstein, A., & Rossetto, L. (2025) · arXiv · doi:10.48550/arxiv.2504.07624 · Cited by 3
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Metadata
- Authors
- Vrandečić, D., & Krötzsch, M.
- Venue
- Communications of the ACM
- Published
- 2014-09-23
- Volume
- 57
- Issue
- 10
- Pages
- 78-85
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2080133951
- Cited by
- 3,342
- References
- 1
- FWCI
- 234.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
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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.
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.