SE‐KGE: A location‐aware Knowledge Graph Embedding model for Geographic Question Answering and Spatial Semantic Lifting. Issue 3 (30th May 2020)

• Record Type: Journal Article
• Authors: Mai, Gengchen; Janowicz, Krzysztof; Cai, Ling; Zhu, Rui; Regalia, Blake; Yan, Bo; Shi, Meilin; Lao, Ni
• Is Part Of: Transactions in GIS. Volume 24:Issue 3(2020)
• Page Start: 623
• Page End: 655
• Publication Date: 2020-05-30
• Subjects: Geographic information systems -- Periodicals; 910.285
• DOI: 10.1111/tgis.12629
• Languages: English
• ISSNs: 1361-1682

Abstract

Abstract: Learning knowledge graph (KG) embeddings is an emerging technique for a variety of downstream tasks such as summarization, link prediction, information retrieval, and question answering. However, most existing KG embedding models neglect space and, therefore, do not perform well when applied to (geo)spatial data and tasks. Most models that do consider space primarily rely on some notions of distance. These models suffer from higher computational complexity during training while still losing information beyond the relative distance between entities. In this work, we propose a location‐aware KG embedding model called SE‐KGE . It directly encodes spatial information such as point coordinates or bounding boxes of geographic entities into the KG embedding space. The resulting model is capable of handling different types of spatial reasoning. We also construct a geographic knowledge graph as well as a set of geographic query–answer pairs called DBGeo to evaluate the performance of SE‐KGE in comparison to multiple baselines. Evaluation results show that SE‐KGE outperforms these baselines on the DBGeo data set for the geographic logic query answering task. This demonstrates the effectiveness of our spatially‐explicit model and the importance of considering the scale of different geographic entities. Finally, we introduce a novel downstream task called spatial semantic lifting which links an arbitrary location in the study area to entities in the KG via some relations. Evaluation on DBGeo shows that our model outperforms the baseline by a substantial margin.