SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting Gengchen Mai1, Krzysztof Janowicz1, Ling Cai1, Rui Zhu1, Blake Regalia1, Bo Yan2, Meilin Shi1, and Ni Lao3 1STKO Lab, University of California, Santa Barbara, CA, USA, 93106 fgengchen mai, janowicz, lingcai, ruizhu, regalia,
[email protected] 2LinkedIn Corporation, Mountain View, CA, USA, 94043
[email protected] 2SayMosaic Inc., Palo Alto, CA, USA, 94303
[email protected] Abstract Learning knowledge graph (KG) embeddings is an emerging technique for a variety of down- stream tasks such as summarization, link prediction, information retrieval, and question an- swering. However, most existing KG embedding models neglect space and, therefore, do not perform well when applied to (geo)spatial data and tasks. For those models that consider space, most of them 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 arXiv:2004.14171v1 [cs.DB] 25 Apr 2020 graph as well as a set of geographic query-answer pairs called DBGeo to evaluate the perfor- mance of SE-KGE in comparison to multiple baselines. Evaluation results show that SE-KGE outperforms these baselines on the DBGeo dataset for geographic logic query answering task.