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Knowledge Mining: A Cross-disciplinary Survey

Machine Intelligence Research volume 19, pages 89–114 (2022)Cite this article

Abstract

Knowledge mining is a widely active research area across disciplines such as natural language processing (NLP), data mining (DM), and machine learning (ML). The overall objective of extracting knowledge from data source is to create a structured representation that allows researchers to better understand such data and operate upon it to build applications. Each mentioned discipline has come up with an ample body of research, proposing different methods that can be applied to different data types. A significant number of surveys have been carried out to summarize research works in each discipline. However, no survey has presented a cross-disciplinary review where traits from different fields were exposed to further stimulate research ideas and to try to build bridges among these fields. In this work, we present such a survey.

References

  1. U. Fayyad, G. Piatetsky-Shapiro, P. Smyth. From data mining to knowledge discovery in databases. AI Magazine, vol. 17, no. 3, pp. 37–54, 1996. DOI: https://doi.org/10.1609/aimag.v17i3.1230.

    Google Scholar 

  2. S. Riedel, L. M. Yao, A. McCallum, B. M. Marlin. Relation extraction with matrix factorization and universal schemas. In Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, ACL, Atlanta, USA, pp. 74–84, 2013.

    Google Scholar 

  3. A. S. d’Avila Garcez, K. Broda, D. M. Gabbay. Symbolic knowledge extraction from trained neural networks: A sound approach. Artificial Intelligence, vol. 125, no. 1–2, pp. 155–207, 2001. DOI: https://doi.org/10.1016/S0004-3702(00)00077-1.

    MathSciNet  MATH  Google Scholar 

  4. S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach, 3rd ed., Harlow, USA: Pearson Education, 2010.

    MATH  Google Scholar 

  5. D. Jurafsky, J. H. Martin. Speech and Language Processing, [Online], Available: https://web.stanford.edu/~jurafsky/slp3/ed3book_dec302020.pdf, 2021.

  6. T. Rocktäschel, S. Singh, S. Riedel. Injecting logical background knowledge into embeddings for relation extraction. In Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, ACL, Denver, USA, pp. 1119–1129, 2015. DOI: https://doi.org/10.3115/v1/N15-1118.

    Google Scholar 

  7. S. Hochreiter, J. Schmidhuber. Long short-term memory. Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. DOI: https://doi.org/10.1162/neco.1997.9.8.1735.

    Google Scholar 

  8. J. Devlin, M. W. Chang, K. Lee, K. Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), ACL, Minneapolis, USA, pp. 4171–4186, 2019. DOI: https://doi.org/10.18653/v1/N19-1423.

    Google Scholar 

  9. E. F. Tjong Kim Sang, F. De Meulder. Introduction to the CoNLL-2033 shared task: Language-independent named entity recognition. In Proceedings of the 7th Conference on Natural Language Learning at HLT-NAACL 2003, ACL, Edmonton, Canada, pp. 142–147, 2003.

    Google Scholar 

  10. E. Hovy, M. Marcus, M. Palmer, L. Ramshaw, R. Weischedel. OntoNotes: The 90% solution. In Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers, Association for Computational Linguistics, New York City, USA, pp. 57–60, 2006.

    Google Scholar 

  11. J. P. C. Chiu, E. Nichols. Named entity recognition with bidirectional LSTM-CNNs. Transactions of the Association for Computational Linguistics, vol. 4, pp. 357–370, 2016. DOI: https://doi.org/10.1162/tacl_a_00104.

    Google Scholar 

  12. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, P. Kuksa. Natural language processing (Almost) from scratch. The Journal of Machine Learning Research, vol. 12, pp. 2493–2537, 2011.

    MATH  Google Scholar 

  13. A. Passos, V. Kumar, A. McCallum. Lexicon infused phrase embeddings for named entity resolution. In Proceedings of the 18th Conference on Computational Natural Language Learning, ACL, Ann Arbor, USA, pp. 78–86, 2014. DOI: https://doi.org/10.3115/v1/W14-1609.

    Google Scholar 

  14. D. E. Appelt, J. R. Hobbs, J. Bear, D. J. Israel, M. Tyson. FASTUS: A finite-state processor for information extraction from real-world text. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, Morgan Kaufmann, Chambery, France, pp. 1172–1178, 1993.

    Google Scholar 

  15. T. Eftimov, B. K. Seljak, P. Korošec!. A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations. PLoS One, vol. 12, no. 6, Article number e0179488, 2017. DOI: https://doi.org/10.1371/journal.pone.0179488.

    Google Scholar 

  16. H. Isozaki, H. Kazawa. Efficient support vector classifiers for named entity recognition. In Proceedings of the 19th International Conference on Computational Linguistics, ACL, Taipei, China, pp. 1–7, 2002. DOI: https://doi.org/10.3115/1072228.1072282.

    Google Scholar 

  17. J. D. Lafferty, A. McCallum, F. C. N. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the 18th International Conference on Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, USA, pp. 282–289, 2001.

    Google Scholar 

  18. A. McCallum, W. Li. Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. In Proceedings of the 7th Conference on Natural Language Learning at HLT-NAACL 2003, ACL, Edmonton, Canada, pp. 188–191, 2003. DOI: https://doi.org/10.3115/1119176.1119206.

    Google Scholar 

  19. Z. H. Huang, W. Xu, K. Yu. Bidirectional LSTM-CRF models for sequence tagging. [Online], Avaiable: https://arxiv.org/abs/1508.01991, 2015.

  20. X. Z. Ma, E. Hovy. End-to-end sequence labeling via Bidirectional LSTM-CNNs-CRF. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL, Berlin, Germany, pp. 1064–1074, 2016. DOI: https://doi.org/10.18653/v1/P16-1101.

    Google Scholar 

  21. G. Lample, M. Ballesteros, S. Subramanian, K. Kawakami, C. Dyer. Neural architectures for named entity recognition. In Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, ACL, San Diego, USA, pp. 260–270, 2016. DOI: https://doi.org/10.18653/v1/N16-1030.

    Google Scholar 

  22. J. Hammerton. Named entity recognition with long short-term memory. In Proceedings of the 7th Conference on Natural Language Learning at HLT-NAACL 2003, ACL, Edmonton, Canada, pp. 172–175, 2003. DOI: https://doi.org/10.3155/1119176.1119202.

    Google Scholar 

  23. A. Akbik, D. Blythe, R. Vollgraf. Contextual string embeddings for sequence labeling. In Proceedings of the 27th International Conference on Computational Linguistics, ACL, Santa Fe, USA, pp. 1638–1649, 2018.

    Google Scholar 

  24. A. Akbik, T. Bergmann, R. Vollgraf. Pooled contextualized embeddings for named entity recognition. In Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), ACL, Minneapolis, USA, pp. 724–728, 2019. DOI: https://doi.org/10.18653/v1/N19-1078.

    Google Scholar 

  25. K. Liu, Y. Fu, C. Q. Tan, M. S. Chen, N. Y. Zhang, S. F. Huang, S. Gao. Noisy-labeled NER with confidence estimation. to Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, ACL, pp. 3437–3445, 2021. DOI: https://doi.org/10.18653/v1/2021.naacl-main.269.

  26. D. J. Zeng, K. Liu, Y. B. Chen, J. Zhao. Distant supervision for relation extraction via piecewise convolutional neural networks. In Proceedings of Conference on Empirical Methods in Natural Language Processing, ACL, Lisbon, Portugal, pp. 1753–1762, 2015. DOI: https://doi.org/10.18653/v1/D15-1203.

    Google Scholar 

  27. E. Sandhaus. The New York Times Annotated Corpus LDC2008T19. Philadelphia, USA, 2008. DOI: https://doi.org/10.35111/77ba-9x74.

  28. Y. H. Zhang, V. Zhong, D. Q. Chen, G. Angeli, C. D. Manning. Position-aware attention and supervised data improve slot filling. In Proceedings of Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Copenhagen, Denmark, pp. 35–45, 2017. DOI: https://doi.org/10.18653/v1/D17-1004.

    Google Scholar 

  29. H. Ji, R. Grishman, H. T. Dang, K. Griffitt, J. Ellis. Overview of the TAC knowledge base population track. In Proceedings of Text Analysis Conference, 2010.

  30. G. R Doddington, A Mitchell, M. A. Przybocki, L. A. Ramshaw, S. M. Strassel, R. M. Weischedel. The automatic content extraction (ACE) program — tasks, data, and evaluation. In Proceedings of the 4th International Conference on Language Resources and Evaluation, European Language Resources Association, Lisbon, Portugal, pp. 837–840, 2004.

    Google Scholar 

  31. S. M. Strassel, M. A. Przybocki, K. Peterson, Z. Y. Song, K. Maeda. Linguistic resources and evaluation techniques for evaluation of cross-document automatic content extraction. In Proceedings of the 6th International Conference on Language Resources and Evaluation, European Language Resources Association, Marrakech, USA, pp. 2706–2709, 2008.

    Google Scholar 

  32. I. Hendrickx, S. N. Kim, Z. Kozareva, P. Nakov, D. Séaghdha, S. Padó, M. Pennacchiotti, L. Romano, S. Szpakowicz. SemEval-2010 Task 8: Multi-way classification of semantic relations between pairs of nominals. In Proceedings of the 5th International Workshop on Semantic Evaluation, Association for Computational Linguistics, Uppsala, Sweden, pp. 33–38, 2010.

    Google Scholar 

  33. M. Banko, O. Etzioni. The tradeoffs between open and traditional relation extraction. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, ACL, Columbus, USA, pp. 28–36, 2008.

    Google Scholar 

  34. R. C. Bunescu, R. J. Mooney. Subsequence kernels for relation extraction. In Proceedings of the 18th International Conference on Neural Information Processing Systems, MIT Press, Vancouver, Canada, pp. 171–178, 2005.

    Google Scholar 

  35. G. D. Zhou, J. Su, J. Zhang, M. Zhang. Exploring various knowledge in relation extraction. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, ACL, Ann Arbor, USA, pp. 427–434, 2005. DOI: https://doi.org/10.3115/1219840.1219893.

    Google Scholar 

  36. A. Culotta, J. Sorensen. Dependency tree kernels for relation extraction. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, ACL, Barcelona, Spain, pp. 423–429, 2004. DOI: https://doi.org/10.3115/1218955.1219009.

    Google Scholar 

  37. Q. Li, H. Ji. Incremental joint extraction of entity mentions and relations. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL, Baltimore, USA, pp. 402–412, 2014.

    Google Scholar 

  38. M. Miwa, M. Bansal. End-to-end relation extraction using LSTMs on sequences and tree structures. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL, Berlin, Germany, pp. 1105–1116, 2016. DOI: https://doi.org/10.18653/v1/P16-1105.

    Google Scholar 

  39. B. W. Yu, Z. Y. Zhang, X. B. Shu, T. W. Liu, Y. B. Wang, B. Wang, S. J. Li. Joint extraction of entities and relations based on a novel decomposition strategy. In Proceedings of the 24th European Conference on Artificial Intelligence, Santiago de Compostela, Spain, pp. 2282–2289, 2020.

    Google Scholar 

  40. T. J. Fu, P. H. Li, W. Y. Ma. GraphRel: Modeling text as relational graphs for joint entity and relation extraction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, ACL, Florence, Italy, pp. 1409–1418, 2019. DOI: https://doi.org/10.18653/v1/P19-1136.

    Google Scholar 

  41. X. Y. Li, F. Yin, Z. J. Sun, X. Y. Li, A. Yuan, D. Chai, M. X. Zhou, J. W. Li. Entity-relation extraction as multi-turn question answering. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, ACL, Florence, Italy, pp. 1340–1350, 2019. DOI: https://doi.org/10.18653/v1/P19-1129.

    Google Scholar 

  42. I. Beltagy, K. Lo, A. Cohan. SciBERT: A pretrained language model for scientific text. In Proceedings of Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Association for Computational Linguistics, Hong Kong, China, pp. 3615–3620, 2019. DOI: https://doi.org/10.18653/v1/D19-1371.

    Google Scholar 

  43. H. Y. Zheng, R. Wen, X. Chen, Y. F. Yang, Y. Y. Zhang, Z. H. Zhang, N. Y. Zhang, B. Qin, X. Ming, Y. F. Zheng. PRGC: Potential relation and global correspondence based joint relational triple extraction. In 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), ACL, pp. 6225–6235, 2021. DOI: https://doi.org/10.18653/v1/2021.acl-long.486.

  44. T. Lai, H. Ji, C. X. Zhai, Q. H. Tran. Joint biomedical entity and relation extraction with knowledge-enhanced collective inference. In 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), ACL, pp. 6248–6260, 2021. DOI: https://doi.org/10.18653/v1/2021.acl-long.488.

  45. J. Wang, W. Lu. Two are better than one: Joint entity and relation extraction with table-sequence encoders. In Proceedings of Conference on Empirical Methods in Natural Language Processing, ACL, pp. 1706–1721, 2000. DOI: https://doi.org/10.18653/v1/2020.emnlp-main.133.

  46. M. Mintz, S. Bills, R. Snow, 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, ACL, Suntec, Singapore, pp. 1003–1011, 2009.

    Google Scholar 

  47. C. J. Xiao, Y. Yao, R. B. Xie, X. Han, Z. Y. Liu, M. S. Sun, F. Lin, L. Y. Lin. Denoising relation extraction from document-level distant supervision. In Proceedings of Conference on Empirical Methods in Natural Language Processing, ACL, pp. 3683–3688, 2020. DOI: https://doi.org/10.18653/v1/2020.emnlp-main.300.

  48. S. Riedel, L. M. Yao, A. McCallum. Modeling relations and their mentions without labeled text. In Proceedings of European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, Berlin, Germany, pp. 148–163, 2010. DOI: https://doi.org/10.1007/978-3-642-15939-8_10.

    Google Scholar 

  49. T. G. Dietterich, R. H. Lathrop, T. Lozano-Pérez. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, vol. 89, no. 1–2, pp. 31–71, 1997. DOI: https://doi.org/10.1016/S0004-3702(96)00034-3.

    MATH  Google Scholar 

  50. G. L. Ji, K. Liu, S. Z. He, J. Zhao. Distant supervision for relation extraction with sentence-level attention and entity descriptions. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, AAAI, San Francisco, USA, pp. 3060–3066, 2017.

    Google Scholar 

  51. Y. K. Lin, S. Q. Shen, Z. Y. Liu, H. B. Luan, M. S. Sun. Neural relation extraction with selective attention over instances. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL, Berlin, Germany, pp. 2124–2133, 2016. DOI: https://doi.org/10.18653/v1/P16-1200.

    Google Scholar 

  52. Z. X. Ye, Z. H. Ling. Distant supervision relation extraction with intra-bag and inter-bag attentions. In Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), ACL, Minneapolis, USA, pp. 2810–2819, 2019. DOI: https://doi.org/10.18653/v1/N19-1288.

    Google Scholar 

  53. G. Y. Wang, W. Zhang, R. X. Wang, Y. L. Zhou, X. Chen, W. Zhang, H. Zhu, H. J. Chen. Label-free distant supervision for relation extraction via knowledge graph embedding. In Proceedings of Conference on Empirical Methods in Natural Language Processing, ACL, Brussels, Belgium, pp. 2246–2255, 2018. DOI: https://doi.org/10.18653/v1/D18-1248.

    Google Scholar 

  54. A. Bordes, N. Usunier, A. Garcia-Durán, J. Weston, O. Yakhnenko. Translating embeddings for modeling multi-relational data. In Proceedings of the 26th International Conference on Neural Information Processing Systems Lake Tahoe, USA, pp. 2787–2795, 2013.

  55. Z. Wang, J. W. Zhang, J. L. Feng, Z. Chen. Knowledge graph embedding by translating on hyperplanes. In Proceedings of the 28th AAAI Conference on Artificial Intelligence, AAAI, Quebec City, Canada, pp. 1112–1119, 2014.

    Google Scholar 

  56. Y. K. Lin, Z. Y. Liu, M. S. Sun, Y. Liu, X. Zhu. Learning entity and relation embeddings for knowledge graph completion. In Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI, Austin, USA, pp. 2181–2187, 2015.

    Google Scholar 

  57. T. Hasegawa, S. Sekine, R. Grishman. Discovering relations among named entities from large corpora. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, ACL, Barcelona, Spain, pp. 415–422, 2004. DOI: https://doi.org/10.3115/1218955.1219008.

    Google Scholar 

  58. B. Rosenfeld, R. Feldman. Clustering for unsupervised relation identification. In Proceedings of the 16th ACM Conference on Information and Knowledge Management, Association for Computing Machinery, Lisbon, Portugal, pp. 411–418, 2007. DOI: https://doi.org/10.1145/1321440.1321499.

    Google Scholar 

  59. L. M. Yao, A. Haghighi, S. Riedel, A. McCallum. Structured relation discovery using generative models. In Proceedings of Conference on Empirical Methods in Natural Language Processing, ACL, Edinburgh, UK, pp. 1456–1466, 2011.

    Google Scholar 

  60. L. M. Yao, S. Riedel, A. McCallum. Unsupervised Relation discovery with sense disambiguation. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL, Jeju Island, Korea, pp. 712–720, 2012.

    Google Scholar 

  61. B. N. Min, S. M. Shi, R. Grishman, C. Y. Lin. Ensemble semantics for large-scale unsupervised relation extraction. In Proceedings of Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, ACL, Jeju Island, Korea, pp. 1027–1037, 2012.

    Google Scholar 

  62. D. Marcheggiani, I. Titov. Discrete-state variational autoencoders for joint discovery and factorization of relations. Transactions of the Association for Computational Linguistics, vol. 4, pp. 231–244, 2016. DOI: https://doi.org/10.1162/tacl_a_00095.

    Google Scholar 

  63. T. T. Tran, P. Le, S. Ananiadou. Revisiting unsupervised relation extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL, pp. 7498–7505, 2020. DOI: https://doi.org/10.18653/v1/2020.acl-main.669.

  64. L. B. Soares, N. FitzGerald, J. Ling, T. Kwiatkowski. Matching the blanks: Distributional similarity for relation learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, ACL, Florence, Italy, pp. 2895–2905, 2019. DOI: https://doi.org/10.18653/v1/P19-1279.

    Google Scholar 

  65. X. Han, T. Y. Gao, Y. K. Lin, H. Peng, Y. L. Yang, C. J. Xiao, Z. Y. Liu, P. Li, J. Zhou, M. S. Sun. More data, more relations, more context and more openness: A review and outlook for relation extraction. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, ACL, Suzhou, China, pp. 745–758, 2020.

    Google Scholar 

  66. A. Yates, M. Banko, M. Broadhead, M. Cafarella, O. Etzioni, S. Soderland. TextRunner: Open information extraction on the web. In Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics, ACL, Rochester, USA, pp. 25–26, 2007.

    Google Scholar 

  67. A. Fader, S. Soderland, O. Etzioni. Identifying relations for open information extraction. In Proceedings of Conference on Empirical Methods in Natural Language Processing, ACL, Edinburgh, UK, pp. 1535–1545, 2011.

    Google Scholar 

  68. L. Del Corro, R. Gemulla. ClausIE: Clause-based open information extraction. In Proceedings of the 22nd International Conference on World Wide Web, Association for Computing Machinery, Rio de Janeiro, Brazil, pp. 355–366, 2013. DOI: https://doi.org/10.1145/2488388.2488420.

    Google Scholar 

  69. G. Stanovsky, J. Michael, L. Zettlemoyer, I. Dagan. Supervised open information extraction. In Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), ACL, New Orleans, USA, pp. 885–895, 2018. DOI: https://doi.org/10.18653/v1/N18-1081.

    Google Scholar 

  70. Y. Ro, Y. Lee, P. Kang. Multi.2OIE: Multilingual open information extraction based on multi-head attention with BERT. In Proceedings of Findings of the Association for Computational Linguistics: EMNLP 2020, Association for Computational Linguistics, pp. 1107–1117, 2020. DOI: https://doi.org/10.18653/v1/2020.findings-emnlp.99.

  71. C. G. Wang, X. Liu, Z. Chen, H. Y. Hong, J. Tang, D. Song. Zero-shot information extraction as a unified text-to-triple translation. In Proceedings of 2021 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, pp. 1225–1238, 2021. DOI: https://doi.org/10.18653/v1/2021.emnlp-main.94.

    Google Scholar 

  72. R. D. Wu, Y. Yao, X. Han, R. B. Xie, Z. Y. Liu, F. Lin, L. Y. Lin, M. S. Sun. Open relation extraction: Relational knowledge transfer from supervised data to unsupervised data. In Proceedings of Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, ACL, Hong Kong, China, pp. 219–228, 2019. DOI: https://doi.org/10.18653/v1/D19-1021.

    Google Scholar 

  73. Y. L. Shen, X. Y. Ma, Z. Q. Tan, S. Zhang, W. Wang, W. M. Lu. Locate and label: A two-stage identifier for nested named entity recognition. In 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), ACL, pp. 2782–2794, 2021. DOI: https://doi.org/10.18653/v1/2021.acllong.216.

  74. T. Mikolov, I. Sutskever, K. Chen, G. Corrado, J. Dean. Distriauted representaions of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems Lake Tahoe, USA, pp. 3111–3119, 2013.

  75. J. Pennington, R. Socher, C. D. Manning. GloVe: Global vectors for word representation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, ACL, Doha, USA, pp. 1532–1543, 2014. DOI: https://doi.org/10.3115/v1/D14-1162.

    Google Scholar 

  76. A. Radford, K. Narasimhan, T. Salimans, I. Sutskever. Improving Language Understanding by Generative Pre-Training, [Online], Available: https://cdn.opknai.com/research-covers/language-unsupervised/language_understanding_paper.pdf, 2021.

  77. R. Colin, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Q. Zhou, W. Li. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, vol. 21, no. 140, pp. 1–67, 2020.

    MathSciNet  MATH  Google Scholar 

  78. W. Cui, X. Chen. Open rule induction. In Proceedings of the 35th Conference on Neural Information Processing Systems, 2021.

  79. J. W. Han, M. Kamber, J. Pei. Data Mining: Concepts and Techniques, 3rd ed., Berlin, Germany: Morgan Kaufmann Publishers, 2011.

    MATH  Google Scholar 

  80. J. Leskovec, A. Rajaraman, J. D. Ullman. Mining of Massive Datasets, [Online], Available: http://infolab.stanford.edu/~ullman/mmds/book.pdf, 2021.

  81. J. W. Han, Y. Z. Sun, X. F. Yan, P. S. Yu. Mining knowledge from data: An information network analysis approach. In Proceedings of the IEEE 28th International Conference on Data Engineering, IEEE, Arlington, USA, pp. 1214–1217, 2012. DOI: https://doi.org/10.1109/ICDE.2012.145.

    Google Scholar 

  82. P. N. Tan, M. Steinbach, A. Karpatne, V. Kumar. Introduction to Data Mining, 2nd ed., USA: Pearson, 2018.

    Google Scholar 

  83. R. Agrawal, R. Srikant. Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, pp. 487–499, 1994.

  84. J. W. Han, J. Pei, Y. W. Yin. Mining frequent patterns without candidate generation. ACM SIGMOD Record, vol. 29, no. 2, pp. 1–12, 2000. DOI: https://doi.org/10.1145/335191.335372.

    Google Scholar 

  85. M. J. Zaki. Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, vol. 12, no. 3, pp. 372–390, 2000. DOI: https://doi.org/10.1109/69.846291.

    Google Scholar 

  86. M. C. Liu, J. F. Qu. Mining high utility itemsets without candidate generation. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management, Association for Computing Machinery, Maui, USA, pp. 55–64, 2012. DOI: https://doi.org/10.1145/2396761.2396773.

    Google Scholar 

  87. Z. H. Deng, S. L. Lv. PrePost+: An efficient N-lists-based algorithm for mining frequent itemsets via Children-parent equivalence pruning. Expert Systems with Applications, vol. 42, no. 13, pp. 5424–5432, 2015. DOI: https://doi.org/10.1016/j.eswa.2015.03.004.

    Google Scholar 

  88. J. F. Qu, B. Hang, Z. Wu, Z. B. Wu, Q. Gu, B. Tang. Efficient mining of frequent itemsets using only one dynamic prefix tree. IEEE Access, vol. 8, pp. 183722–183735, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3029302.

    Google Scholar 

  89. R. Srikant, R. Agrawal. Mining generalized association rules. In Proceedings of the 21th International Conference on Very Large Data Bases, Zurich, Switzerland, pp. 407–419, 1995.

  90. J. W. Han, Y. J. Fu. Discovery of multiple-level association rules from large databases. In Proceedings of the 21th International Conference on Very Large Data Bases, Zurich, Swizerland, pp. 420–431, 1995.

  91. J. W. Han, J. Pei, Y. W. Yin, R. Y. Mao. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery, vol. 8, no. 1, pp. 53–87, 2004. DOI: https://doi.org/10.1023/B:DAMI.0000005258.31418.83.

    MathSciNet  Google Scholar 

  92. R. Agrawal, R. Srikant. Mining sequential patterns. In Proceedings of the 11th International Conference on Data Engineering, IEEE, Taipei, China, pp. 3–14, 1995. DOI: https://doi.org/10.1109/ICDE.1995.380415.

    Google Scholar 

  93. R. Srikant, R. Agrawal. Mining quantitative association rules in large relational tables. In Proceedings of ACM SIGMOD International Conference on Management of Data, ACM, Montreal, Canada, pp. 1–22, 1996. DOI: https://doi.org/10.1145/233269.233311.

    Google Scholar 

  94. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, A. I. Verkamo. Fast discovery of association rules. Advances in Knowledge Discovery and Data Mining, U. M. Fayyad, G. Piatetsky-Shapiro, Ed., Menlo Park, USA: American Association for Artificial Intelligence, pp. 307–328, 1996.

    Google Scholar 

  95. T. Fukuda, Y. Morimoto, S. Morishita, T. Tokuyama. Data mining using two-dimensional optimized association rules: Scheme, algorithms, and visualization. In Proceedings of ACM SIGMOD Conference on Management of Data, Association for Computing Machinery, Montreal, Canada, pp. 13–23, 1996.

    Google Scholar 

  96. B. Lent, A. Swami, J. Widom. Clustering association rules. to Proceedings of the 13th International Conference on Data Engineering, IEEE, Birmingham, UK, pp. 220–231, 1997. DOI: https://doi.org/10.1109/ICDE.1997.581756.

    Google Scholar 

  97. K. Yoda, T. Fukuda, Y. Morimoto, S. Morishita, T. Tokuyama. Computing optimized rectilinear regions for association rules. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, AAAI, Newport Beach, USA, pp. 96–103, 1997.

    Google Scholar 

  98. M. Kamber, J. W. Han, J. Y. Chiang. Metarule-guided mining of multi-dimensional association rules using data cubes. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, AAAI, Newport Beach, USA, pp. 207–210, 1997.

    Google Scholar 

  99. Y. Aumann, Y. Lindell. A statistical theory for quantitative association rules. Journal of Intelligent Information Systems, vol. 20, no. 3, pp. 255–283, 2003. DOI: https://doi.org/10.1023/A:1022812808206.

    Google Scholar 

  100. L. Q. Geng, H. J. Hamilton. Interestingness measures for data mining: A survey. ACM Computing Surveys, vol. 38, no. 3, Article number 9, 2006. DOI: https://doi.org/10.1145/1132960.32963.

    Google Scholar 

  101. J. Blanchard, F. Guillet, R. Gras, H. Briand. Using information-theoretic measures to assess association rule interestingness. In Proceedings of the 5th IEEE International Conference on Data Mining, IEEE, Houston, USA, pp. 66–73, 2005. DOI: https://doi.org/10.1109/ICDM.2005.149.

    Google Scholar 

  102. J. W. Han, H. Cheng, D. Xin, X. F. Yan. Frequent pattern mining: Current status and future directions. Data Mining and Knowledge Discovery, vol. 15, no. 1, pp. 55–86, 2007. DOI: https://doi.org/10.1007/s10618-006-0059-1.

    MathSciNet  Google Scholar 

  103. P. Founder-Viger, J. C. W. Lin, B. Vo, T. T. Chi, J. Zhang, H. B. Le. A survey of itemset mining. WIREs Data Mining and Knowledge Discovery, vol. 7, no. 4, Article number e1207, 2017. DOI: https://doi.org/10.1002/widm.1207.

    Google Scholar 

  104. C. C. Aggarwal, Y. Li, J. Y. Wang, J. Wang. Frequent pattern mining with uncertain data. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Paris, France, pp. 29–38, 2009. DOI: https://doi.org/10.1145/1557019.1557030.

    Google Scholar 

  105. W. S. Gan, J. C. W. Lin, P. Fournier-Viger, H. C. Chao, P. S. Yu. HUOPM: High-utility occupancy pattern mining. IEEE Transactions on Cybernetics, vol. 50, no. 3, pp. 1195–1208, 2020. DOI: https://doi.org/10.1109/TCYB.2019.2896267.

    Google Scholar 

  106. C. M. Chen, L. L. Chen, W. S. Gan, L. N. Qiu, W. P. Ding. Discovering high utility-occupancy patterns from uncertain data. Information Sciences, vol. 546, pp. 1208–1229, 2021. DOI: https://doi.org/10.1016/j.ins.2020.10.001.

    MathSciNet  Google Scholar 

  107. B. Vo, L. T. T. Nguyen, N. Bui, T. D. D. Nguyen, V. N. Huynh, T. P. Hong. An efficient method for mining closed potential high-utility itemsets. IEEE Access, vol. 8, pp. 31813–31822, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.2974104.

    Google Scholar 

  108. R. Andrews, J. Diederich, A. B. Tickle. Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems, vol. 8, no. 6, 373–389, 1995. DOI: https://doi.org/10.1016/0950-7051(96)81920-4.

    Google Scholar 

  109. V. I. S. Carmona. Experimental Analysis of Representation Learning Systems, Ph. D. dissertation. University College London, UK, 2018.

    Google Scholar 

  110. R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, D. Pedreschi. A survey of methods for explaining black box models. ACM Computing Surveys, vol. 51, no. 5, Article number 93, 2019. DOI: https://doi.org/10.1145/3236009.

    Google Scholar 

  111. S. Thrun. Extracting rules from artificial neural networks with distributed representations. In Proceedings of the 7th International Conference on Neural Information Processing Systems, Denver, USA, pp. 505–512, 1994.

  112. M. W. Craven, J. W. Shavlik. Extracting tree-structured representations of trained networks. In Proceedings of the 8th International Conference on Neural Information Processing Systems, Denver, USA, pp. 24–30, 1995.

  113. J. Huysmans, K. Dejaeger, C. Mues, J. Vanthienen, B. Baesens. An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decision Support Systems, vol. 51, no. 1, pp. 141–154, 2011. DOI: https://doi.org/10.1016/j.dss.2010.12.003.

    Google Scholar 

  114. A. A. Freitas. Comprehensible classification models: A position paper. ACM SIGKDD Explorations Newsletter, vol. 15, no. 1, pp. 1–10, 2013. DOI: https://doi.org/10.1145/2594473.2594475.

    Google Scholar 

  115. H. Jacobsson. Rule extraction from recurrent neural networks: A taxonomy and review. Neural Computation, vol. 17, no. 6, pp. 1223–1263, 2005. DOI: https://doi.org/10.1162/0899766053630350.

    MathSciNet  MATH  Google Scholar 

  116. L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone. Classification and Regression Trees, New York, USA: Wadsworth Int. Group, 1984.

    MATH  Google Scholar 

  117. M. T. Ribeiro, S. Singh, C. Guestrin. “Why Should I Trust You?”: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, San Francisco, USA, pp. 1135–1144, 2016. DOI: https://doi.org/10.1145/2939672.2939778.

    Google Scholar 

  118. P. Domingos. Knowledge discovery via multiple models. Intelligent Data Analysis, vol. 2, no. 3, pp. 187–202, 1998. DOI: https://doi.org/10.3233/IDA-1998-2303.

    Google Scholar 

  119. I. Sánchez, T. Rocktaschel, S. Riedel, S. Singh. Towards extracting faithful and descriptive representations of latent variable models. In Proceedings of AAAI Spring Syposium on Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches, AAAI, Stanford, California, USA, pp. 35–38, 2015.

    Google Scholar 

  120. I. Sanchez Carmona, S. Riedel. Extracting interpretable models from matrix factorization models. In Proceedings of International Conference on Cognitive Computation: Integrating Neural and Symbolic Approaches, Montreal, Canada, pp. 78–84, 2015.

  121. G. Peake, J. Wang. Explanation mining: Post hoc interpretability of latent factor models for recommendation systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM, London, United Kingdom, pp. 2060–2069, 2018. DOI: https://doi.org/10.1145/3219819.3220072.

    Google Scholar 

  122. A. C. Gusmão, A. H. C. Correia, G. De Bona, F. G. Cozman. interpreting embedding models of knowledge bases: A pedagogical approach. In Proceedings of ICML Workshop on Human Interpretability in Machine Learning, Stockholm, Sweden, pp. 79–86, 2018.

  123. S. B. Thrun. Extracting Provably Correct Rules from Artificial Neural Networks, Bonn, University of Bonn, Germany, 1993.

    Google Scholar 

  124. J. R. Zilke, E. L. Mencía, F. Janssen. DeepRED — rule extraction from deep neural networks. In Proceedings of the 19th International Conference on Discovery Science, Springer, Bari, Italy, pp. 457–473, 2016. DOI: https://doi.org/10.1007/978-3-319-46307-0_29.

    Google Scholar 

  125. M. Sato, H. Tsukimoto. Rule extraction from neural networks via decision tree induction. In Proceedings of International Joint Conference on Neural Networks, IEEE, Washington, USA, pp. 1870–1875, 2001. DOI: https://doi.org/10.1109/IJCNN.2001.938448.

    Google Scholar 

  126. R. Setiono, H. Liu. Understanding neural networks via rule extraction. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, Canada, pp. 480–485, 1995.

  127. B. S. Yang, W. T. Yih, X. D. He, J. F. Gao, L. Deng. Embedding entities and relations for learning and inference in knowledge bases. In Proceedings of the 3rd International Conference on Learning Representations, San Diego, USA, 2015.

  128. W. J. Murdoch, A. Szlam. Automatic rule extraction from long short term memory networks. In Proceedings of the 5th International Conference on Learning Representations, Toulon, France, 2017.

  129. S. M. Lundberg, S. I. Lee. A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, USA, pp. 4768–4777, 2017.

  130. S. Bang, P. T. Xie, H. Lee, W. Wu, E. Xing. Explaining a black-box by using a deep variational information bottle-neck approach. In Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI, pp. 11396–11404, 2021.

    Google Scholar 

  131. M. Pourvali, Y. C. Jin, C. Sheng, Y. Meng, L. Wang, M. S. Gorkovenko, C. J. Hu. Path-based visual explanation. In Proceedings of the 9th CCF International Conference on Natural Language Processing and Chinese Computing, Springer, Zhengzhou, China, pp. 454–466, 2020. DOI: https://doi.org/10.1007/978-3-030-60457-8_37.

    Google Scholar 

  132. A. Jacovi, Y. Goldberg. Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness? In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL, pp. 4198–4205, 2020. DOI: https://doi.org/10.18653/v1/2020.acl-main.386.

  133. J. Sippy, G. Bansal, D. S. Weld. Data staining: A method for comparing faithfulness of explainers. In Proceedings of ICML Workshop on Human Interpretability in Machine Learning, 2020.

  134. J. Bastings, S. Ebert, P. Zablotskaia, A. Sandholm, K. Filippova. “Will you find these shortcuts?” A protocol for evaluating the faithfulness of input salience methods for text classification, [Online], Available: https://arxiv.org/pdf/2111.07367.pdf, 2021.

  135. J. Yuan, O. Nov, E. Bertini. An exploration and validation of visual factors in understanding classification rule sets. In Proceedings of IEEE Visualization Conference, IEEE, New Orleans, USA, pp. 6–10, 2021. DOI: https://doi.org/10.1109/VIS49827.2021.9623303.

    Google Scholar 

  136. I. Lage, E. Chen, J. He, M. Narayanan, B. Kim, S. J. Gershman, F. Doshi-Velez. Human evaluation of models built for interpretability. In Proceedings of the 7th AAAI Conference on Human Computation and Crowdsourcing, AAAI Press, Stevenson, USA, pp. 59–67, 2019.

    Google Scholar 

  137. A. McCallum, D. Jensen. A Note on the Unification of Information Extraction and Data Mining using Conditional-Probability, Relational Models, [Online], Available: https://scholarworks.umass.edu/cs_faculty_pubs/42/, 2021.

  138. R. J. Mooney, R. Bunescu. Mining knowledge from text using information extraction. ACM SIGKDD Explorations Newsletter, vol. 7, no. 1, pp. 3–10, 2005. DOI: https://doi.org/10.1145/1089815.1089817.

    Google Scholar 

  139. Q. Z. Xie, X. Z. Ma, Z. H. Dai, E. Hovy. An interpretable knowledge transfer model for knowledge base completion. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL, Vancouver, Canada, pp. 950–962, 2017. DOI: https://doi.org/10.18653/v1/P17-1088.

    Google Scholar 

  140. L. A. Galárraga, C. Teflioudi, K. Hose, F. Suchanek. AMIE: Association rule mining under incomplete evidence in ontological knowledge bases. In Proceedings of the 22nd International Conference on World Wide Web, Association for Computing Machinery, Rio de Janeiro, Brazil, pp. 413–422, 2013. DOI: https://doi.org/10.1145/2488388.2488425.

    Google Scholar 

  141. S. Riedel, S. Singh, G. Bouchard, T. Rocktäschel, I. Sanchez. Towards two-way interaction with reading machines. In Proceedings of the 3rd International Conference on Statistical Language and Speech Processing, Springer, Budapest, Hungary, pp. 1–7, 2015. DOI: https://doi.org/10.1007/978-3-319-25789-1_1.

    Google Scholar 

  142. K. A. Kaufman, R. S. Michalski. From data mining to knowledge mining. Data Mining and Data Visualization, C. R. Rao, E. J. Wegman, J. L. Solka, Eds., Amsterdam, Netherlands: Elsevier, pp. 47–75, 2005. DOI: https://doi.org/10.1016/S0169-7161(04)24002-0.

    Google Scholar 

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  1. Lenovo Research, Beijing, 100094, China

    Yong Rui, Vicente Ivan Sanchez Carmona, Mohsen Pourvali, Yun Xing, Wei-Wen Yi, Hui-Bin Ruan & Yu Zhang

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Yong Rui received the B. Sc. degree in electrical engineering from Southeast University, China in 1991, the M. Sc. degree in electrical engineering from Tsinghua University, China in 1994, and the Ph. D. degree in electrical and computer engineering from University of Illinois at Urbana-Champaign (UIUC), USA in 1999. He is currently the Chief Technology Officer and Senior Vice President of Lenovo Group, China. He is a Fellow of ACM, IEEE, IAPR, China SPIE, CCF and CAAI, and a Foreign Member of Academia Europaea. He holds 70 patents, and is the recipient of the prestigious 2018 ACM SIGMM Technical Achievement Award and 2016 IEEE Computer Society Edward J. McCluskey Technical Achievement Award.

His research interests include multimedia, artificial intelligence, big data and knowledge mining.

Vicente Ivan Sanchez Carmona received the B. Eng. and M. Eng. degree in computer engineering from National Autonomous University of Mexico, Mexico in 2008 and 2011, and the Ph. D. degree in computer science from University College London, UK in 2018. He is currently a researcher in Lenovo’s AI Lab, China. He has served as a reviewer in different conferences such as AAAI, ACL, CoNLL, COLING, among others.

His research interests include artificial intelligence, behavioral science, cognitive science and human-computer interaction.

Mohsen Pourvali received the Ph. D. degree in computer science from Ca′ Foscari University of Venice, ltaly in 2017. During the Ph. D. period, he was working on text summarization and document enrichment. Currently, he is an advisory researcher at AI Lab in Lenovo. He is an experienced lecturer with a demonstrated history of teaching in universities.

His research interests include explainable artificial intelligence and knowledge graph, especially in domain adaptive information extraction.

Yun Xing received the B. Sc. degree in optical information science and technology from Beijing Institute of Technology, China in 2012, and the M. Eng. degree in electronics and optics from Polytech Orleans, France in 2016. Currently, he is a NLP researcher in AI Lab at Lenovo Research, China.

His research interests include natural language processing in machine learning and deep learning.

Wei-Wen Yi received the B. Eng. and M. Eng. degrees in information and communication engineering from Beijing University of Posts and Telecommunications, China in 2017 and 2020, respectively. Currently, she is a natural language processing researcher at Lenovo Research, China. She received the Best Paper Award of the EAI International Conference on Communications and Networking in China, China in 2018. She is a member of EAI and IEEE.

Her research interests include named entity recognition, relation extraction and entity linking.

Hui-Bin Ruan received the M. Sc. degrees in computer technology from Soochow University, China in 2020. Currently, she is a researcher in natural language process in Lenovo, China.

Her research interests include discourse parsing, text classification and entity linking.

Yu Zhang received the B. Eng. degree in human factors, B. Sc. (Minor) degree in applied mathematics and M. Sc. degree in engineering physics from Beihang University, China in 2008, 2008 and 2011. He is currently the technical assistant to chief technology officer at Lenovo, China, and a Ph. D. degree candidate in computer science at Southeast University, China.

His research interests include human computer interaction and human-centered AI.

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Rui, Y., Carmona, V.I.S., Pourvali, M. et al. Knowledge Mining: A Cross-disciplinary Survey. Mach. Intell. Res. 19, 89–114 (2022). https://doi.org/10.1007/s11633-022-1323-6

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Keywords

  • Knowledge mining
  • knowledge extraction
  • information extraction
  • association rule
  • interpretability