As a tech company, Grab has expanded from transportation to food delivery, aiming to serve Southeast Asia with hyperlocalized applications. Information about places as transportation destinations can help to improve our knowledge about places as restaurants, so long as the spatial entity resolution problem between these datasets can be solved. In this project, we attempted to recognize identical place entities from databases of Points-of-Interest (POI) and GrabFood restaurants, using their spatial and textual attributes, i.e., latitude, longitude, place name, and street address. Distance metrics were calculated for these attributes and fed to tree-based classifiers. POI-restaurant matching was conducted separately for Singapore, Philippines, Indonesia, and Malaysia. Experimental estimates demonstrate that a matching POI can be found for over 35% of restaurants in these countries. As part of these estimates, test datasets were manually created, and RandomForest, AdaBoost, Gradient Boosting, and XGBoost perform well, with most accuracy, precision, and recall scores close to or higher than 90% for matched vs. unmatched classification. To the authors' knowledge, there are no previous published scientific papers devoted to matching of spatial entities for the Southeast Asia region.

翻译：作为一家科技公司，Grab已从交通出行拓展至餐饮外卖领域，致力于通过超本地化应用服务东南亚地区。若能解决不同数据集间的空间实体匹配问题，交通目的地信息可有效提升对餐厅位置相关知识的认知。在本项目中，我们尝试利用空间与文本属性（即经纬度、地点名称、街道地址），从兴趣点（POI）数据库与GrabFood餐厅数据库中识别相同的地点实体。针对这些属性计算距离指标后，将其输入基于树的分类器。我们分别对新加坡、菲律宾、印度尼西亚和马来西亚开展了POI-餐厅匹配实验。实验评估表明，在这些国家中，超过35%的餐厅可找到匹配的POI。作为评估的一部分，人工创建了测试数据集，RandomForest、AdaBoost、Gradient Boosting和XGBoost算法表现优异，在匹配与非匹配分类任务中的准确率、精确率和召回率大多接近或超过90%。据作者所知，此前尚无公开出版的学术论文专门研究东南亚地区的空间实体匹配问题。