Skip to main content
Springer Nature Link
Log in

Effective rule mining of sparse data based on transfer learning

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Rule mining is an important and challenging task in data mining. Although many state-of-art algorithms have been proposed on dense data, they are not effectively adaptive for sparse data, such as sparse heterogeneous networks. Transfer learning improves the performance of algorithms in the target domain by transferring knowledge from a similar source domain, which provides a feasible and effective method to solve the above challenge. In this paper, we propose a transfer learning-based algorithm to mine rules on sparse data effectively, named TL-ERMSD. The algorithm is capable of detecting the knowledge of a common structure as well as the rules and logics between the source and target domains. Then, rule transfer is carried out by establishing the mapping mechanism between the two domains. We conducted experiments over the heterogeneous network datasets, including the source domain dataset FB15K and the target domain dataset Yago2Sample. The results demonstrate that the proposed TL-ERMSD for rule mining has a significant advantage over the existing algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Barati, M., Bai, Q., Liu, Q.: Mining semantic association rules from RDF data. Knowledge-Based Systems 133, 183–196 (2017)

    Article  Google Scholar 

  2. Bordes, A., Glorot, X., Weston, J., Bengio, Y.: A semantic matching energy function for learning with multi-relational data - application to word-sense disambiguation. Machine Learning 94(2), 233–259 (2014)

    Article  MATH  Google Scholar 

  3. Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS, pp. 2787–2795 (2013)

  4. Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: AAAI, pp. 301–306 (2011)

  5. Cai, T., Li, J., Mian, A.S., Li, R., Sellis, T., Yu, J.X.: Target-aware holistic influence maximization in spatial social networks. IEEE Transactions on Knowledge and Data Engineering (2020). https://doi.org/10.1109/TKDE.2020.3003047

  6. Chen, J., Zhong, M., Li, J., Wang, D., Qian, T., Tu, H.: Effective deep attributed network representation learning with topology adapted smoothing. IEEE Transactions on Cybernetics (2021). https://doi.org/10.1109/TCYB.2021.3064092

    Article  Google Scholar 

  7. Chen, Y., Wang, D.Z., Goldberg, S.: Scalekb: scalable learning and inference over large knowledge bases. The VLDB Journal 25(6), 893–918 (2016)

    Article  Google Scholar 

  8. Dai, W., Yang, Q., Xue, G., Yu, Y.: Boosting for transfer learning. In: ICML 227, 193–200 (2007)

  9. Dehaspe, L., Toivonen, H.: Discovery of frequent DATALOG patterns. Data Mining and Knowledge Discovery 3(1), 7–36 (1999)

    Article  Google Scholar 

  10. Deng, S., Wang, B., Huang, S., Yue, C., Zhou, J., Wang, G.: Self-adaptive framework for efficient stream data classification on storm. IEEE Transactions on Systems, Man, and Cybernetics: Systems 50(1), 123–136 (2020)

    Article  Google Scholar 

  11. Du, J., Michalska, S., Subramani, S., Wang, H., Zhang, Y.: Neural attention with character embeddings for hay fever detection from twitter. Health Information Science and Systems 7(1), 21 (2019)

    Article  Google Scholar 

  12. Galárraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: Fast rule mining in ontological knowledge bases with AMIE\(+\). The VLDB Journal 24(6), 707–730 (2015)

    Article  Google Scholar 

  13. Galárraga, L.A., Teflioudi, C., Hose, K., Suchanek, F.M.: AMIE: association rule mining under incomplete evidence in ontological knowledge bases. In: WWW, pp. 413–422 (2013)

  14. Goethals, B., den Bussche, J.V.: Relational association rules: Getting warmer. In: Pattern Detection and Discovery, vol. 2447, pp. 125–139 (2002)

  15. Jenatton, R., Roux, N.L., Bordes, A., Obozinski, G.: A latent factor model for highly multi-relational data. In: NIPS, pp. 3176–3184 (2012)

  16. Kaur, T., Gandhi, T.K.: Automated brain image classification based on VGG-16 and transfer learning. In: ICIT, pp. 94–98 (2019)

  17. Lajus, J., Galárraga, L., Suchanek, F.M.: Fast and exact rule mining with AMIE 3. In: ESWC, vol. 12123, pp. 36–52 (2020)

  18. Li, J., Cai, T., Deng, K., Wang, X., Sellis, T., Xia, F.: Community-diversified influence maximization in social networks. Information Systems 92, 101522 (2020)

    Article  Google Scholar 

  19. Li, M., Zhang, Y., Shi, Q., Yang, X., Cui, Q., Li, L., Zhou, J.: Constraint-adaptive rule mining in large databases. In: DASFAA, vol. 12683, pp. 579–591 (2021)

  20. Li, Z., Wang, X., Li, J., Zhang, Q.: Deep attributed network representation learning of complex coupling and interaction. Knowledge-Based Systems 212, 106618 (2021)

    Article  Google Scholar 

  21. Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: AAAI, pp. 2181–2187 (2015)

  22. Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks. In: ICML, vol. 37, pp. 97–105 (2015)

  23. Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: ICML, vol. 70, pp. 2208–2217 (2017)

  24. Lu, G., Hao, Q., Kong, K., Yan, J., Li, H., Li, X.: Deep convolutional neural networks with transfer learning for neonatal pain expression recognition. In: ICNC-FSKD, pp. 251–256 (2018)

  25. Muggleton, S.: Inductive logic programming. In: ALT, pp. 42–62 (1990)

  26. Muggleton, S.: Inverse entailment and progol. New Generation Computing 13(3&4), 245–286 (1995)

    Article  Google Scholar 

  27. Nickel, M., Tresp, V., Kriegel, H.: A three-way model for collective learning on multi-relational data. In: ICML, pp. 809–816 (2011)

  28. Niu, S., Liu, Y., Wang, J., Song, H.: A decade survey of transfer learning (2010–2020). IEEE Transactions on Artificial Intelligence 1(2), 151–166 (2020)

    Article  Google Scholar 

  29. Omran, P.G., Wang, Z., Wang, K.: Knowledge graph rule mining via transfer learning. In: PAKDD, vol. 11441, pp. 489–500 (2019)

  30. Ortona, S., Meduri, V.V., Papotti, P.: Robust discovery of positive and negative rules in knowledge bases. In: ICDE, pp. 1168–1179 (2018)

  31. Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks and Learning Systems 22(2), 199–210 (2011)

    Article  Google Scholar 

  32. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22(10), 1345–1359 (2010)

    Article  Google Scholar 

  33. Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5, 239–266 (1990)

    Article  Google Scholar 

  34. Scharwächter, E., Müller, E., Donges, J.F., Hassani, M., Seidl, T.: Detecting change processes in dynamic networks by frequent graph evolution rule mining. In: ICDM, pp. 1191–1196 (2016)

  35. Shabtay, L., Fournier-Viger, P., Yaari, R., Dattner, I.: A guided fp-growth algorithm for mining multitude-targeted item-sets and class association rules in imbalanced data. Information Sciences 553, 353–375 (2021)

    Article  MATH  Google Scholar 

  36. Song, X., Li, J., Tang, Y., Zhao, T., Chen, Y., Guan, Z.: JKT: A joint graph convolutional network based deep knowledge tracing. Information Sciences 580, 510–523 (2021)

    Article  Google Scholar 

  37. Srinivasan, A., Faruquie, T.A., Joshi, S.: Data and task parallelism in ILP using mapreduce. Machine Learning 86(1), 141–168 (2012)

    Article  MATH  Google Scholar 

  38. Supriya, S.S., Wang, H., Zhang, Y.: Automated epilepsy detection techniques from electroencephalogram signals: a review study. Health Information Science and Systems 8(1), 33 (2020)

    Article  Google Scholar 

  39. Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: AAAI, pp. 1112–1119 (2014)

  40. Xu, Y., Pan, S.J., Xiong, H., Wu, Q., Luo, R., Min, H., Song, H.: A unified framework for metric transfer learning. IEEE Transactions on Knowledge and Data Engineering 29(6), 1158–1171 (2017)

    Article  Google Scholar 

  41. Xue, G., Zhong, M., Li, J., Chen, J., Zhai, C., Kong, R.: Dynamic network embedding survey. Neurocomputing 472, 212–223 (2022)

    Article  Google Scholar 

  42. Yang, Y., Guan, Z., Li, J., Zhao, W., Cui, J., Wang, Q.: Interpretable and efficient heterogeneous graph convolutional network. IEEE Transactions on Knowledge and Data Engineering (2020). https://doi.org/10.1109/TKDE.2021.3101356

  43. Yao, Y., Doretto, G.: Boosting for transfer learning with multiple sources. In: CVPR, pp. 1855–1862 (2010)

  44. Yin, J., Tang, M., Cao, J., Wang, H., You, M., Lin, Y.: Vulnerability exploitation time prediction: an integrated framework for dynamic imbalanced learning. World Wide Web (2021). https://doi.org/10.1007/s11280-021-00909-z

  45. Zelle, J.M., Mooney, R.J., Konvisser, J.B.: Combining top-down and bottom-up techniques in inductive logic programming. In: ICML, pp. 343–351 (1994)

  46. Zeng, Q., Patel, J.M., Page, D.: Quickfoil: Scalable inductive logic programming. VLDB 8(3), 197–208 (2014)

    Google Scholar 

  47. Zhang, F., Wang, Y., Liu, S., Wang, H.: Decision-based evasion attacks on tree ensemble classifiers. World Wide Web 23(5), 2957–2977 (2020)

    Article  Google Scholar 

  48. Zhang, W., Paudel, B., Wang, L., Chen, J., Zhu, H., Zhang, W., Bernstein, A., Chen, H.: Iteratively learning embeddings and rules for knowledge graph reasoning. In: WWW, pp. 2366–2377 (2019)

  49. Zhao, F., Sun, H., Jin, L., Jin, H.: Structure-augmented knowledge graph embedding for sparse data with rule learning. Computer Communications 159, 271–278 (2020)

    Article  Google Scholar 

  50. Zheng, R., Zhang, L., Jin, H.: Pneumoconiosis identification in chest x-ray films with cnn-based transfer learning. CCF Transactions on High Performance Computing 3(2), 186–200 (2021)

    Article  Google Scholar 

Download references

Acknowledgements

The work is supported by the National Natural Science Foundation of China (Grant No. 61972077), LiaoNing Revitalization Talents Program (Grant No. XLYC2007079), Postdoctoral Research Foundation of China (Grant No. 2021M690397) and the Science and Technology Plan Project of Shen Fu Reform and Innovation demonstration Zone in 2021 (Big Data Deep Analysis Platform for New Energy Vehicles). Boyang Li is the corresponding author.

Author information

Authors and Affiliations

  1. Northeastern University, Shenyang, China

    Yongjiao Sun

  2. Alibaba, Bejing, China

    Jiancheng Guo

  3. Beijing Institute of Technology, Beijing, China

    Boyang Li

  4. The University of Western Australia, Perth, Australia

    Nur Al Hasan Haldar

Authors
  1. Yongjiao Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jiancheng Guo
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Boyang Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Nur Al Hasan Haldar
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Boyang Li.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Additional information

This article belongs to the Topical Collection: Special Issue on Decision Making in Heterogeneous Network Data Scenarios and Applications

Guest Editors: Jianxin Li, Chengfei Liu, Ziyu Guan, and Yinghui Wu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, Y., Guo, J., Li, B. et al. Effective rule mining of sparse data based on transfer learning. World Wide Web 26, 461–480 (2023). https://doi.org/10.1007/s11280-022-01042-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-022-01042-1

Keywords