Information networks fusion based on multi-task coordination

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.

Keywords

information networks fusion / multi-task coordination / conditional random field / inference

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

[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

[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 locationbased 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

[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

[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

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

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

[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

[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

[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

[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

RIGHTS & PERMISSIONS

2020 Higher Education Press