In Internet applications, the description for the same point of interest (POI) entity for different location-based services (LBSs) is not completely identical. The POI entity information in a single LBS data source contains incomplete data and exhibits insufficient objectivity. Aligning and consolidating POI entities from various LBSs can provide users with more comprehensive, objective, and authoritative POI information. We herein propose a multi-attribute measurement-based entity alignment method for Internet LBSs to achieve POI entity alignment and data consolidation. This method is based on multi-attribute information (geographical information, text coincidence information, semantic information) of POI entities and is combined with different measurement methods to calculate the similarity of candidate entity pairs. Considering the demand for computational efficiency, the particle swarm optimization algorithm is used to train the model and optimize the weights of multi-attribute measurements. A consolidation strategy is designed for the LBS text data and user rating data from different sources to obtain more comprehensive and objective information. The experimental results show that, compared with other baseline models, the POI alignment method based on multi-attribute measurement performed the best. Using this method, the information of POI entities in multisource LBS can be integrated to serve netizens.

1. [1] H. Abdelhaq, C. Sengstock, and M. Gertz, “Eventweet: Online localized event detection from twitter,” Proc. of the VLDB Endowment, Vol.6, No.12, pp. 1326-1329, 2013.
2. [2] Z. Yao, Y. Fu, B. Liu et al., “POI recommendation: A temporal matching between POI popularity and user regularity,” 2016 IEEE 16th Int. Conf. on Data Mining (ICDM), pp. 549-558, 2016.
3. [3] W. Zhang and J. Gelernter, “Geocoding location expressions in Twitter messages: A preference learning method,” J. of Spatial Information Science, No.9, pp. 37-70, 2014.
4. [4] G. Mckenzie, K. Janowicz, S. Gao et al., “POI Pulse: A Multi-granular, Semantic Signature-Based Information Observatory for the Interactive Visualization of Big Geosocial Data,” Cartographica: the Int. J. for Geographic Information and Geovisualization, Vol.50, No.2, pp. 71-85, 2015.
5. [5] H. Köpcke and E. Rahm, “Frameworks for entity matching: A comparison,” Data & Knowledge Engineering, Vol.69, No.2, pp. 197-210, 2010.
6. [6] O. Peled, M. Fire, L. Rokach et al., “Entity matching in online social networks,” 2013 Int. Conf. on Social Computing, pp. 339-344, 2013.
7. [7] M. Nentwig, A. Groß, and E. Rahm, “Holistic Entity Clustering for Linked Data,” IEEE Int. Conf. on Data Mining Workshops (ICDMW), pp. 194-201, 2016.
8. [8] R. Santos, P. Murrieta-Flores, and B. Martins, “Learning to combine multiple string similarity metrics for effective toponym matching,” Int. J. of Digital Earth, Vol.11, No.9, pp. 913-938, 2017.
9. [9] T. Scheffler, R. Schirru, and P. Lehmann, “Matching points of interest from different social networking sites,” Annual Conf. on Artificial Intelligence, pp. 245-248, Springer, 2012.
10. [10] G. McKenzie, K. Janowicz, and B. Adams, “A weighted multi-attribute method for matching user-generated points of interest,” Cartography and Geographic Information Science, Vol.41, No.2, pp. 125-137, 2014.
11. [11] Z. H. Zhou, J. Wu, and W. Tang, “Ensembling neural networks: many could be better than all,” Artificial Intelligence, Vol.137, Nos.1-2, pp. 239-263, 2002.
12. [12] Y. Zhang and L. Yao, “Mining POI Alias from Microblog Conversations,” Pacific-Asia Conf. on Knowledge Discovery and Data Mining, Lecture Notes in Computer Science, Vol.10937, pp. 425-436, 2018.
13. [13] Y. Zhang, J. Huang, M. Deng et al., “Automated Matching of Multi-Scale Building Data Based on Relaxation Labelling and Pattern Combinations,” Int. J. of Geo-Information, Vol.8, No.1, Article No.38, 2019.
14. [14] J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proc. of IEEE Int. Conf. on Neural Networks, Vol.4, pp. 1942-1948, 1995.
15. [15] J. Devlin, M.-W. Chang, K. Lee et al., “BERT: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint, arXiv:1810.04805, 2018.
16. [16] A. Vaswani, N. Shazeer, N. Parmar et al., “Attention is all you need,” Advances in Neural Information Processing Systems, pp. 5998-6008, 2017.
17. [17] S. Li, Z. Zhao, R. Hu, W. Li, T. Liu, and X. Du, “Analogical Reasoning on Chinese Morphological and Semantic Relations,” Proc. of the 56th Annual Meeting of the Association for Computational Linguistics, Vol.2: Short Papers, pp. 138-143, 2018.