Abstract
Mining logical rules from a knowledge graph (KG) can reveal useful patterns for predicting facts, curating the KG, and identifying trends. However, many rule mining systems face challenges when working with numerical data because numerical predicates can take a large number of values, leading to a huge search space. In this work, we present REGNUM, a system that addresses this issue by generating rules with numerical constraints. REGNUM extends the body of rules mined from a KG by using supervised discretization of numerical values with decision trees to increase the confidence of the rules without sacrificing significance. Our experimental results show that the numerical rules have a higher overall quality than the parent rules and are effective at making better predictions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Membership to a class, can also be represented with a binary predicate, i.e., type(X, Y).
- 2.
Variables are represented using lowercase letters whereas capitalized letters denote constants.
- 3.
- 4.
References
Ahmadi, N., Lee, J., Papotti, P., Saeed, M.: Explainable fact checking with probabilistic answer set programming. CoRR abs/1906.09198 (2019)
Aumann, Y., Lindell, Y.: A statistical theory for quantitative association rules. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 1999, pp. 261–270. Association for Computing Machinery, New York (1999). https://doi.org/10.1145/312129.312243
Betz, P., Meilicke, C., Stuckenschmidt, H.: Supervised knowledge aggregation for knowledge graph completion. In: Groth, P., et al. (eds.) ESWC 2022. LNCS, vol. 13261, pp. 74–92. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06981-9_5
Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. Taylor & Francis, Milton Park (1984)
Bühmann, L., Lehmann, J., Westphal, P.: Dl-learner-a framework for inductive learning on the semantic web. J. Web Semant. 39, 15–24 (2016)
Cohen, W.W.: Fast effective rule induction. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 115–123. Morgan Kaufmann (1995)
Dehaspe, L., Toironen, H.: Discovery of Relational Association Rules. In: Džeroski, S., Lavrač, N. (eds.) Relational Data Mining, pp. 189–208. Springer, Heidelberg (2001). https://doi.org/10.1007/978-3-662-04599-2_8
Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: IJCAI (1993)
Galárraga, L., Razniewski, S., Amarilli, A., Suchanek, F.M.: Predicting completeness in knowledge bases. In: de Rijke, M., Shokouhi, M., Tomkins, A., Zhang, M. (eds.) Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM 2017, Cambridge, United Kingdom, 6–10 February 2017, pp. 375–383. ACM (2017)
Galárraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: Fast rule mining in ontological knowledge bases with AMIE+. VLDB J. 24(6), 707–730 (2015)
Galárraga, L.A., Preda, N., Suchanek, F.M.: Mining rules to align knowledge bases. In: Proceedings of the 2013 Workshop on Automated Knowledge Base Construction, AKBC@CIKM 2013, San Francisco, California, USA, 27–28 October 2013, pp. 43–48. ACM (2013)
García-Durán, A., Niepert, M.: KBLRN: end-to-end learning of knowledge base representations with latent, relational, and numerical features. In: Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI) (2018)
Gesese, G.A., Alam, M., Sack, H.: LiterallyWikidata - a benchmark for knowledge graph completion using literals. In: Hotho, A., et al. (eds.) ISWC 2021. LNCS, vol. 12922, pp. 511–527. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-88361-4_30
Hühn, J., Hüllermeier, E.: FURIA: an algorithm for unordered fuzzy rule induction. Data Mining Knowl. Discov. 19(3), 293–319 (2009). https://doi.org/10.1007/s10618-009-0131-8
Jaramillo, I.F., Garzás, J., Redchuk, A.: Numerical association rule mining from a defined schema using the VMO algorithm. Appl. Sci. 11(13), 6154 (2021). https://doi.org/10.3390/app11136154
Khajeh Nassiri, A., Pernelle, N., Saïs, F., Quercini, G.: Generating referring expressions from RDF knowledge graphs for data linking. In: The Semantic Web – ISWC 2020 (2020)
Lajus, J., Galárraga, L., Suchanek, F.: Fast and exact rule mining with AMIE 3. In: Harth, A., et al. (eds.) ESWC 2020. LNCS, vol. 12123, pp. 36–52. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49461-2_3
Meilicke, C., Chekol, M.W., Fink, M., Stuckenschmidt, H.: Reinforced anytime bottom up rule learning for knowledge graph completion. arXiv preprint arXiv:2004.04412 (2020)
Meilicke, C., Chekol, M.W., Ruffinelli, D., Stuckenschmidt, H.: Anytime bottom-up rule learning for knowledge graph completion. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, pp. 3137–3143 (7 2019)
Minaei-Bidgoli, B., Barmaki, R., Nasiri, M.: Mining numerical association rules via multi-objective genetic algorithms. Inf. Sci. 233, 15–24 (2013). https://doi.org/10.1016/j.ins.2013.01.028. https://www.sciencedirect.com/science/article/pii/S0020025513001072
Muggleton, S.: Learning from positive data. In: Muggleton, S. (ed.) ILP 1996. LNCS, vol. 1314, pp. 358–376. Springer, Heidelberg (1997). https://doi.org/10.1007/3-540-63494-0_65
Navas-Palencia, G.: Optimal binning: mathematical programming formulation abs/2001.08025 (2020). http://arxiv.org/abs/2001.08025
Ortona, S., Meduri, V.V., Papotti, P.: Robust discovery of positive and negative rules in knowledge bases. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE), pp. 1168–1179 (2018)
Ortona, S., Meduri, V.V., Papotti, P.: Rudik: rule discovery in knowledge bases. Proc. VLDB Endow. 11(12), 1946–1949 (2018)
Sadeghian, A., Armandpour, M., Ding, P., Wang, D.Z.: DRUM: End-to-End Differentiable Rule Mining on Knowledge Graphs. Curran Associates Inc., Red Hook (2019)
Salleb-Aouissi, A., Vrain, C., Nortet, C.: Quantminer: a genetic algorithm for mining quantitative association rules. In: IJCAI, pp. 1035–1040 (2007)
Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. In: ACM SIGMOD Conference (1996)
Wang, P.W., Stepanova, D., Domokos, C., Kolter, J.Z.: Differentiable learning of numerical rules in knowledge graphs. In: International Conference on Learning Representations (2020). https://openreview.net/forum?id=rJleKgrKwS
Yang, F., Yang, Z., Cohen, W.W.: Differentiable learning of logical rules for knowledge base reasoning. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)
Zeng, Q., Patel, J.M., Page, D.: Quickfoil: scalable inductive logic programming. Proc. VLDB Endow. 8(3), 197–208 (2014)
Acknowledgements
This work has been supported by the project PSPC AIDA: 2019-PSPC-09 funded by BPI-France.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Khajeh Nassiri, A., Pernelle, N., Saïs, F. (2023). REGNUM: Generating Logical Rules with Numerical Predicates in Knowledge Graphs. In: Pesquita, C., et al. The Semantic Web. ESWC 2023. Lecture Notes in Computer Science, vol 13870. Springer, Cham. https://doi.org/10.1007/978-3-031-33455-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-33455-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-33454-2
Online ISBN: 978-3-031-33455-9
eBook Packages: Computer ScienceComputer Science (R0)