• E. Hovy, R. Navigli, and S. P. Ponzetto. Collaboratively built semi-structured content and artificial intelligence: the story so far. Artif. Intell., 194:2–27, January 2013. doi:10.1016/j.artint.2012.10.002. [Bibtex]
  • G. Weikum, X. L. Dong, S. Razniewski, and F. M. Suchanek. Machine knowledge: creation and curation of comprehensive knowledge bases. Found. Trends Databases, 10(2-4):108–490, 2021. URL: https://doi.org/10.1561/1900000064, doi:10.1561/1900000064. [Bibtex]
  • J. Sequeda and O. Lassila. Designing and building enterprise knowledge graphs. Synthesis Lectures on Data, Semantics, and Knowledge, 11(1):1–165, 2021. [Bibtex]

KG Construction

  • J. Lehmann, R. Isele, M. Jakob, A. Jentzsch, D. Kontokostas, P. N. Mendes, S. Hellmann, M. Morsey, P. van Kleef, S. Auer, and C. Bizer. Dbpedia - A large-scale, multilingual knowledge base extracted from wikipedia. Semantic Web, 6(2):167–195, 2015. doi:10.3233/SW-140134. [Bibtex]  [PDF]
  • E. Muñoz, A. Hogan, and A. Mileo. Using linked data to mine rdf from wikipedia's tables. In Proceedings of the 7th ACM international conference on Web search and data mining, 533–542. ACM, 2014. [Bibtex]  [PDF]
  • J. Hoffart, F. M. Suchanek, K. Berberich, and G. Weikum. Yago2: a spatially and temporally enhanced knowledge base from wikipedia. Artif. Intell., 194:28–61, 2013. doi:10.1016/j.artint.2012.06.001. [Bibtex]
  • T. Mitchell, W. Cohen, E. Hruschka, P. Talukdar, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohamed, N. Nakashole, E. Platanios, A. Ritter, M. Samadi, B. Settles, R. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves, and J. Welling. Never-ending learning. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15). 2015. [Bibtex]  [PDF]
  • R. Navigli and S. P. Ponzetto. Babelnet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artificial Intelligence, 193:217–250, 2012. URL: https://www.sciencedirect.com/science/article/pii/S0004370212000793. [Bibtex]  [PDF]
  • S. Wu, L. Hsiao, X. Cheng, B. Hancock, T. Rekatsinas, P. Levis, and C. Ré. Fonduer: knowledge base construction from richly formatted data. In Proceedings of the 2018 International Conference on Management of Data, 1301–1316. 2018. URL: https://dl.acm.org/doi/abs/10.1145/3183713.3183729. [Bibtex]  [PDF]
  • X. Dong, E. Gabrilovich, G. Heitz, W. Horn, N. Lao, K. Murphy, T. Strohmann, S. Sun, and W. Zhang. Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14, 601–610. New York, NY, USA, 2014. ACM. doi:10.1145/2623330.2623623. [Bibtex]

KG refinement

  • H. Paulheim. Knowledge graph refinement: a survey of approaches and evaluation methods. Semantic web, 8(3):489–508, 2017. [Bibtex]  [PDF]
  • R. Xie, Z. Liu, J. Jia, H. Luan, and M. Sun. Representation learning of knowledge graphs with entity descriptions. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI'16, 2659–2665. AAAI Press, 2016. URL: http://dl.acm.org/citation.cfm?id=3016100.3016273. [Bibtex]  [PDF]
  • M. Cannaviccio, D. Barbosa, and P. Merialdo. Accurate fact harvesting from natural language text in wikipedia with lector. In Proceedings of the 19th International Workshop on Web and Databases, San Francisco, CA, USA, June 26, 2016, 9. ACM, 2016. doi:10.1145/2932194.2932203. [Bibtex]
  • R. Socher, D. Chen, C. D. Manning, and A. Ng. Reasoning with neural tensor networks for knowledge base completion. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 26, pages 926–934. Curran Associates, Inc., 2013. URL: http://papers.nips.cc/paper/5028-reasoning-with-neural-tensor-networks-for-knowledge-base-completion.pdf. [Bibtex]

Neural Methods for KG construction

  • M. E. Peters, M. Neumann, R. Logan, R. Schwartz, V. Joshi, S. Singh, and N. A. Smith. Knowledge enhanced contextual word representations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 43–54. Hong Kong, China, November 2019. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/D19-1005, doi:10.18653/v1/D19-1005. [Bibtex]
  • B. Ding, Q. Wang, B. Wang, and L. Guo. Improving knowledge graph embedding using simple constraints. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 110–121. Melbourne, Australia, July 2018. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/P18-1011, doi:10.18653/v1/P18-1011. [Bibtex]
  • M. Nickel, K. Murphy, V. Tresp, and E. Gabrilovich. A review of relational machine learning for knowledge graphs. Proceedings of the IEEE, 104(1):11–33, 2016. [Bibtex]  [PDF]
  • A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko. Translating embeddings for modeling multi-relational data. In Advances in neural information processing systems, 2787–2795. 2013. [Bibtex]  [PDF]
  • M. Nickel, V. Tresp, and H.-P. Kriegel. Factorizing yago: scalable machine learning for linked data. In Proceedings of the 21st International Conference on World Wide Web, WWW '12, 271–280. New York, NY, USA, 2012. ACM. URL: http://doi.acm.org/10.1145/2187836.2187874, doi:10.1145/2187836.2187874. [Bibtex]
  • S. Riedel, L. Yao, and A. McCallum. Modeling relations and their mentions without labeled text. Machine learning and knowledge discovery in databases, pages 148–163, 2010. [Bibtex]  [PDF]


  • Z. Kozareva and E. H. Hovy. A semi-supervised method to learn and construct taxonomies using the web. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP 2010, 9-11 October 2010, MIT Stata Center, Massachusetts, USA, A meeting of SIGDAT, a Special Interest Group of the ACL, 1110–1118. ACL, 2010. [Bibtex]  [PDF]
  • R. Snow, D. Jurafsky, and A. Y. Ng. Semantic taxonomy induction from heterogenous evidence. In N. Calzolari, C. Cardie, and P. Isabelle, editors, ACL 2006, 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, Sydney, Australia, 17-21 July 2006. The Association for Computer Linguistics, 2006. [Bibtex]  [PDF]
  • R. Snow, D. Jurafsky, and A. Y. Ng. Learning syntactic patterns for automatic hypernym discovery. In Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, NIPS 2004, December 13-18, 2004, Vancouver, British Columbia, Canada], 1297–1304. 2004. [Bibtex]  [PDF]
  • M. A. Hearst. Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th conference on Computational linguistics-Volume 2, 539–545. Association for Computational Linguistics, 1992. [Bibtex]  [PDF]

Relation identification

  • O. Levy, M. Seo, E. Choi, and L. Zettlemoyer. Zero-shot relation extraction via reading comprehension. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), 333–342. Vancouver, Canada, August 2017. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/K17-1034, doi:10.18653/v1/K17-1034. [Bibtex]
  • F. Mesquita, J. Schmidek, and D. Barbosa. Effectiveness and efficiency of open relation extraction. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 447–457. Association for Computational Linguistics, October 2013. [Bibtex]  [PDF]
  • M. Mintz, S. Bills, R. Snow, and D. Jurafsky. Distant supervision for relation extraction without labeled data. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2, ACL '09, 1003–1011. Stroudsburg, PA, USA, 2009. Association for Computational Linguistics. [Bibtex]  [PDF]

Named Entity Recognition/Disambiguation

  • Z. Guo and D. Barbosa. Robust named entity disambiguation with random walks. Semantic Web, 9(4):459–479, 2018. doi:10.3233/SW-170273. [Bibtex]  [PDF]
  • J. Hoffart, M. A. Yosef, I. Bordino, H. Furstenau, M. Pinkal, M. Spaniol, B. Taneva, S. Thater, and G. Weikum. Robust disambiguation of named entities in text. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, 782–792. 2011. URL: http://www.aclweb.org/anthology/D11-1072. [Bibtex]

Web Tables

  • M. Cannaviccio, D. Barbosa, and P. Merialdo. Towards annotating relational data on the web with language models. In Proceedings of The Web Conference 2018, 1307–1316. 2018. doi:10.1145/3178876.3186029. [Bibtex]  [PDF]
  • V. Efthymiou, O. Hassanzadeh, M. Rodriguez-Muro, and V. Christophides. Matching web tables with knowledge base entities: from entity lookups to entity embeddings. In International Semantic Web Conference, 260–277. Springer, 2017. [Bibtex]  [PDF]
  • D. Ritze, O. Lehmberg, and C. Bizer. Matching HTML tables to dbpedia. In Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics, 10. ACM, 2015. [Bibtex]  [PDF]

Question Answering

  • R. Das, M. Zaheer, S. Reddy, and A. McCallum. Question answering on knowledge bases and text using universal schema and memory networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 358–365. Vancouver, Canada, July 2017. Association for Computational Linguistics. URL: https://www.aclweb.org/anthology/P17-2057, doi:10.18653/v1/P17-2057. [Bibtex]
  • D. Savenkov and E. Agichtein. When a knowledge base is not enough: question answering over knowledge bases with external text data. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 235–244. ACM, 2016. doi:10.1145/2911451.2911536. [Bibtex]  [PDF]
  • M. Yahya, D. Barbosa, K. Berberich, Q. Wang, and G. Weikum. Relationship queries on extended knowledge graphs. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, San Francisco, CA, USA, February 22-25, 2016, 605–614. ACM, 2016. doi:10.1145/2835776.2835795. [Bibtex]  [PDF]
  • K. Xu, S. Reddy, Y. Feng, S. Huang, and D. Zhao. Question answering on freebase via relation extraction and textual evidence. arXiv preprint arXiv:1603.00957, 2016. [Bibtex]  [PDF]
  • L. He, M. Lewis, and L. Zettlemoyer. Question-answer driven semantic role labeling: using natural language to annotate natural language. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, September 17-21, 2015, 643–653. 2015. [Bibtex]  [PDF]
  • A. Fader, L. Zettlemoyer, and O. Etzioni. Open question answering over curated and extracted knowledge bases. In The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14, New York, NY, USA - August 24 - 27, 2014, 1156–1165. 2014. doi:10.1145/2623330.2623677. [Bibtex]
  • J. Berant, A. Chou, R. Frostig, and P. Liang. Semantic parsing on freebase from question-answer pairs. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013, 18-21 October 2013, Grand Hyatt Seattle, Seattle, Washington, USA, A meeting of SIGDAT, a Special Interest Group of the ACL, 1533–1544. ACL, 2013. URL: http://aclweb.org/anthology/D/D13/D13-1160.pdf. [Bibtex]