{"type":"ScholarlyArticle","title":"Towards an Integrated View of Semantic Annotation for POIs with Spatial and Textual Information","authors":[{"name":"Dabin Zhang","slug":"dabin-zhang","given":"Dabin","family":"Zhang","affiliation":"School of Software , Shandong University"},{"name":"Ronghui Xu","slug":"ronghui-xu","given":"Ronghui","family":"Xu","affiliation":"School of Software , Shandong University"},{"name":"Weiming Huang","slug":"weiming-huang","given":"Weiming","family":"Huang","affiliation":"School of Computer Science and Engineering , Nanyang Technological University"},{"name":"Kai Zhao","slug":"kai-zhao","given":"Kai","family":"Zhao","affiliation":"Robinson College of Business , Georgia State University"},{"name":"Meng Chen","slug":"meng-chen","given":"Meng","family":"Chen","affiliation":"School of Software , Shandong University"}],"abstract":"Categories of Point of Interest (POI) facilitate location-based services from many aspects like location search and POI recommendation. However, POI categories are often incomplete and new POIs are being consistently generated, this rises the demand for semantic annotation for POIs, i.e., labeling the POI with a semantic category. Previous methods usually model sequential check-in information of users to learn POI features for annotation. However, users' check-ins are hardly obtained in reality, especially for those newly created POIs. In this context, we present a Spatial-Textual POI Annotation (STPA) model for static POIs, which derives POI categories using only the geographic locations and names of POIs. Specifically, we design a GCN-based spatial encoder to model spatial correlations among POIs to generate POI spatial embeddings, and an attention-based text encoder to model the semantic contexts of POIs to generate POI textual embeddings. We finally fuse the two embeddings and preserve multi-view correlations for semantic annotation. We conduct comprehensive experiments to validate the effectiveness of STPA with POI data from AMap. Experimental results demonstrate that STPA substantially outperforms several competitive baselines, which proves that STPA is a promising approach for annotating static POIs in map services.","doi":"10.24963/ijcai.2023/271","url":"https://ijcai.org/proceedings/2023/271","pdf":"https://ijcai.org/proceedings/2023/0271.pdf","markdown":"https://ijcai.org/proceedings/2023/271.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/271","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Data Mining","track":"Main Track","number":271,"pages":{"first":2441,"last":2449},"keywords":["Data Mining: DM: Mining spatial and/or temporal data"],"videos":[],"references":[{"position":1,"raw":"[Bing et al., 2022] Junxiang Bing, Meng Chen, Min Yang, Weiming Huang, Yongshun Gong, and Liqiang Nie. 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