Skip to main content
SpringerLink
Log in

Information networks fusion based on multi-task coordination

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

Information networks provide a powerful representation of entities and the relationships between them. Information networks fusion is a technique for information fusion that jointly reasons about entities, links and relations in the presence of various sources. However, existing methods for information networks fusion tend to rely on a single task which might not get enough evidence for reasoning. In order to solve this issue, in this paper, we present a novel model called MC-INFM (information networks fusion model based on multi-task coordination). Different from traditional models, MC-INFM casts the fusion problem as a probabilistic inference problem, and collectively performs multiple tasks (including entity resolution, link prediction and relation matching) to infer the final result of fusion. First, we define the intra-features and the inter-features respectively and model them as factor graphs, which can provide abundant evidence to infer. Then, we use conditional random field (CRF) to learn the weight of each feature and infer the results of these tasks simultaneously by performing the maximum probabilistic inference. Experiments demonstrate the effectiveness of our proposed model.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Zhang J. Social network fusion and mining: a survey. 2018, arXiv preprint arXiv:1804.09874

  2. Namata G, Kok S, Getoor L. Collective graph identification. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011, 87–95

  3. Lacoste-Julien S, Palla K, Davies A, Kasneci G, Graepel T. SIGMa: simple greedy matching for aligning large knowledge bases. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012, 572–580

  4. Suchanek F, Abiteboul S, Senellart P. PARIS: probabilistic alignment of relations, instances, and schema. Proceedings of the VLDB Endowment, 2011, 5(3): 157–168

    Article  Google Scholar 

  5. Niu F, Re C, Doan A, Shavlik J. Tuffy: scaling up statistical inference in Markov logic networks using an RDBMS. Proceedings of the VLDB Endowment, 2011, 4(6): 373–384

    Article  Google Scholar 

  6. Lao N, Mitchell T, Cohen W. Random walk inference and learning in a large scale knowledge base. In: Proceedings of Conference on Empirical Methods in Natural Language Processing. 2011, 27–31

  7. Kong X, Zhang J, Yu P. Inferring anchor links across multiple heterogeneous social networks. In: Proceedings of ACM International Conference on Information and Knowledge Management. 2013, 179–188

  8. Koutra D, Tong H, Lubensky D. Big-align: fast bipartite graph alignment. In: Proceedings of International Conference on Data Mining. 2013, 389–398

  9. Zafarani R, Liu H. Connecting users across social media sites: a behavioral-modeling approach. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2013, 41–49

  10. Zhang J, Shao W, Wang S, Kong X, Yu P. PNA: partial network alignment with generic stable matching. In: Proceedings of IEEE International Conference on Information Reuse and Integration. 2015, 166–173

  11. Zhang J, Yu P. Integrated anchor and social link predictions across partially aligned social networks. In: Proceedings of International Joint Conference on Artificial Intelligence. 2015, 1620–1626

  12. Zhang J, Yu P. Multiple anonymized social networks alignment. In: Proceedings of International Conference on Data Mining. 2015, 599–608

  13. Zhang J, Yu P. PCT: partial co-alignment of social networks. In: Proceedings of International World Wide Web Conference. 2016, 749–759

  14. Zhang J, Kong X, Yu P. Predicting social links for new users across aligned heterogeneous social networks. In: Proceedings of International Conference on Data Mining. 2013, 1289–1294

  15. Zhang J, Kong X, Yu P. Transfer heterogeneous links across location-based social networks. In: Proceedings of ACM International Conference on Web Search and Data Mining. 2014, 303–312

  16. Zhang J. Link prediction across heterogeneous social networks: a survey. Dissertation, University of Illinois at Chicago, US. 2014

    Google Scholar 

  17. Zhang J, Yu P, Zhou Z. Meta-path based multi-network collective link prediction. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2014, 1286–1295

  18. Richardson M, Domingos P. Markov logic networks. Machine Learning, 2006, 62: 107–136

    Article  Google Scholar 

  19. Lafferty J, McCallum A, Pereira F. Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of International Conference on Machine Learning. 2001, 282–289

  20. Zhou T, Lv L, Zhang Y. Predicting missing links via local information. The European Physical Journal B, 2009, 71(4): 623–630

    Article  Google Scholar 

  21. Lv L, Zhou T. Link prediction in complex networks: a survey. Physica A: Statistical Mechanics and its Applications, 2011, 390: 1150–1170

    Article  Google Scholar 

  22. Lee J Y, Tukhvatov R. Evaluations of similarity measures on VK for link prediction. Data Science and Engineering, 2018, 3(3): 277–289

    Article  Google Scholar 

  23. Hasan M, Chaoji V, Salem S, Zaki M. Link prediction using supervised learning. In: Proceedings of SIAM International Conference on Data Mining. 2006

  24. Aditya K, Menon A, Elkan C. Link prediction via matrix factorization. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 2011, 437–452

  25. Dunlavy D, Kolda T, Acar E. Temporal link prediction using matrix and tensor factorizations. ACM Transactions on Knowledge Discovery from Data, 2011, 5(2): 10

    Article  Google Scholar 

  26. Tang J, Gao H, Hu X, Liu H. Exploiting homophily effect for trust prediction. In: Proceedings of ACM International Conference on Web Search and Data Mining. 2013, 53–62

  27. Yates A, Etzioni O. Unsupervised methods for determining object and relation synonyms on the web. Journal of Artificial Intelligence Research, 2009, 34(1): 255–296

    Article  Google Scholar 

  28. Dong X, Srivastava D. Knowledge curation and knowledge fusion: challenges, models and applications. In: Proceedings of the ACM SIGMDD International Conference on Management of Data. 2015, 2063–2066

  29. Galarraga L, Heitz G. Canonicalizing open knowledge bases. In: Proceedings of ACM International Conference on Information and Knowledge Management. 2014, 1679–1688

  30. Cohen W, Ravikumar P, Fienberg S. A comparison of string distance metrics for name-matching tasks. In: Proceedings of International Joint Conference on Artificial Intelligence. 2003, 73–78

  31. Chen Y, Wang D. Knowledge expansion over probabilistic knowledge bases. In: Proceedings of International Conference on Management of Data. 2014, 649–660

  32. Rossi R J. Mathematical Statistics: an Introduction to Likelihood Based Inference. New York: John Wiley & Sons, 2018

    Book  Google Scholar 

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

  34. Suchanek F, Kasneci G, Weikum G. Yago: a core of semantic knowledge. In: Proceedings of International World Wide Web Conference. 2007, 697–706

  35. Lehmann J, Isele R, Jakob M, Jentzsch A, Kontokostas D, et al. DBpedia — a large-scale, multilingual knowledge base extracted from Wikipedia. Semantic Web, 2013, 6(2): 167–195

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Key R&D Program of China (2018YFB1003404) and the National Natural Science Foundation of China (Grant Nos. 61672142, U1435216, 61602103).

Author information

Authors and Affiliations

  1. School of Computer Science & Engineering, Northeastern University, Shenyang, 110004, China

    Dong Li, Derong Shen, Yue Kou & Tiezheng Nie

  2. School of Information, Liaoning University, Shenyang, 110036, China

    Dong Li

Authors
  1. Dong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Derong Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yue Kou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tiezheng Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Derong Shen.

Additional information

Dong LI is PhD candidate. He received his Master degree in Computer Technology from Northeastern University, China in 2008. His research interests include social networks analysis and data mining.

Derong Shen received her PhD degree in Computer Software and Theory from Northeastern University, China in 2004. Currently, she is a professor in the School of Computer Science & Engineering, Northeastern University, China. Her research interests include social networks analysis and data integration. She is a member of senior CCF, IEEE, and ACM.

Yue Kou received her PhD degree in Computer Software and Theory from Northeastern University, China in 2009. Currently, she is an associate professor in the School of Computer Science & Engineering, Northeastern University, China. Her research interests include social networks analysis and data mining. She is a member of CCF, IEEE, and ACM.

Tiezheng Nie received his PhD degree in Computer Software and Theory from Northeastern University, China in 2009. Currently, he is an associate professor in the School of Computer Science & Engineering, Northeastern University, China. His research interests include data integration and data mining. He is a member of CCF, IEEE, and ACM.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, D., Shen, D., Kou, Y. et al. Information networks fusion based on multi-task coordination. Front. Comput. Sci. 15, 154608 (2021). https://doi.org/10.1007/s11704-020-9195-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-020-9195-9

Keywords

Search

Navigation