Abstract
Rules or constraints can be used to clean a knowledge base, or find new facts which should have been included. Recently there are many efforts on automatically mining rules from large scale knowledge bases. However, these rules usually contain no constants. In practice, we often need some detailed rules, for example, rules restricted to a special country or a special profession. One major challenge of appending constants lies in that there are large amount of constants, each of which can generate a new rule. Moreover, we have to choose appropriate granularity in order to trade off between the applicability and precision (or support and confidence in traditional rule mining terminology). In this paper, we propose a Spark based solution to mine rules with constants, a taxonomy based approach to control the granularity, and several techniques to improve the efficiency. We also conduct extensive experiments to evaluate the efficiency and effectiveness of our solution with comparison with the state of the art works.
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Notes
- 1.
Yago3 has more than 10 million facts, and Freebase has about 2.4 billion facts.
- 2.
We will define this in Sect. 2.3.
- 3.
We will explain the two metrics soon.
- 4.
In this paper, without otherwise specified, the default knowledge base is Yago3.
- 5.
For ease of illustration, both Algorithms 2 and 3 are tailored for the second rule type.
- 6.
- 7.
References
Mahdisoltani, F., Biega, J., Suchanek, F.M.: YAGO3: a knowledge base from multilingual wikipedias. In: Seventh Biennial Conference on Innovative Data Systems Research, CIDR 2015, Asilomar, CA, USA, 4–7 January 2015, Online Proceedings (2015)
Niu, F., Zhang, C., Ré, C., Shavlik, J.W.: DeepDive: web-scale knowledge-base construction using statistical learning and inference. In: Proceedings of the Second International Workshop on Searching and Integrating New Web Data Sources, Istanbul, Turkey, 31 August 2012, pp. 25–28 (2012)
Shin, J., Wu, S., Wang, F., De Sa, C., Zhang, C., Ré, C.: Incremental knowledge base construction using DeepDive. PVLDB 8, 1310–1321 (2015)
Chen, Y., Wang, D.Z., Goldberg, S.: ScaLeKB: scalable learning and inference over large knowledge bases. VLDB J. 25, 893–918 (2016)
Lao, N., Mitchell, T., Cohen, W.W.: Random walk inference and learning in a large scale knowledge base. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 529–539 (2011)
Chen, Y., Wang, D.Z.: Knowledge expansion over probabilistic knowledge bases. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 649–660 (2014)
Chu, X., Ilyas, I.F., Papotti, P.: Discovering denial constraints. Proc. VLDB Endow. 6, 1498–1509 (2013)
Galárraga, L.A., Teflioudi, C., Hose, K., Suchanek, F.: AMIE: association rule mining under incomplete evidence in ontological knowledge bases. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 413–422 (2013)
Fan, W., Geerts, F., Li, J., Xiong, M.: Discovering conditional functional dependencies. IEEE Trans. Knowl. Data Eng. 23, 683–698 (2011)
Chen, Y., Goldberg, S., Wang, D.Z., Johri, S.S.: Ontological pathfinding: mining first-order knowledge from large knowledge bases. In: Proceedings of the 2016 International Conference on Management of Data, pp. 835–846 (2016)
Quinlan, J.R.: Learning logical definitions from relations. Mach. Learn. 5, 239–266 (1990)
Muggleton, S., De Raedt, L.: Inductive logic programming: theory and methods. J. Log. Program. 19, 629–679 (1994)
Frnkranz, J., Gamberger, D., Lavrac, N.: Foundations of Rule Learning. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-540-75197-7
Savasere, A., Omiecinski, E., Navathe, S.B.: An efficient algorithm for mining association rules in large databases. In: Proceedings of the 21st International Conference on Very Large Data Bases, pp. 432–444 (1995)
Galárraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: Fast rule mining in ontological knowledge bases with AMIE
. VLDB J. 24, 707–730 (2015)Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994, Proceedings of 20th International Conference on Very Large Data Bases, 12–15 September 1994, Santiago de Chile, Chile, pp. 487–499 (1994)
Zeng, Q., Patel, J.M., Page, D.: QuickFOIL: scalable inductive logic programming. Proc. VLDB Endow. 8, 197–208 (2014)
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51, 107–113 (2008)
Yan, W.P., Larson, P.: Eager aggregation and lazy aggregation. VLDB 31(12), 345–357 (1995)
Harinarayan, V., Gupta, A.: Generalized projections: a powerful query-optimization technique (1995)
. VLDB J. 24, 707–730 (2015)Acknowledgments
This work was supported by the National Key Research & Develop Plan (No. 2016YFB1000702), National Science Foundation of China (No. 61602488), and Talent Training Fund at RUC.
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Wang, X., Zhang, J., Chen, J., Fan, J. (2018). Mining Rules with Constants from Large Scale Knowledge Bases. In: Trujillo, J., et al. Conceptual Modeling. ER 2018. Lecture Notes in Computer Science(), vol 11157. Springer, Cham. https://doi.org/10.1007/978-3-030-00847-5_38
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