Entity Alignment Method of Points of Interest for Internet Location-Based Services

Abstract

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.