A large number of spatial knowledge graphs (SKGs) are available from spatially enriched knowledge bases, e.g., DBpedia and YAGO2. This provides a great chance to understand valuable information about the regions surrounding us. However, it is hard to comprehend SKGs due to the explosively growing volume and the complication of the graph structures. Thus we study the problem of similar region search (SRS), which is an easy-to-use but effective way to explore spatial data. The effectiveness of SRS highly depends on how to measure the region similarity. However, existing approaches cannot make use of the rich information contained in SKGs thus may lead to incorrect results. In this paper, we propose a spatial knowledge representation learning method for region similarity, namely SKRL4RS. SKRL4RS firstly encodes the spatial entities of an SKG into a vector space to make it easier to extract useful features. Then regions are represented by 3-D tensors using the spatial entity embeddings together with geographical information. Finally, region tensors are fed into the conventional triplet network to learn the feature vectors of regions. The region similarity measure learned by SKRL4RS can capture the hierarchical types, semantic relatedness, and relative locations of spatial entities inside a region. Experimental results on two real-world datasets show that our SKRL4RS outperforms the state-of-the-art by a significant margin in terms of the accuracy of measuring region similarity.
|Title of host publication||CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management|
|Publisher||Association for Computing Machinery|
|Number of pages||10|
|Publication status||Published - 2019 Nov 3|
|Event||28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China|
Duration: 2019 Nov 3 → 2019 Nov 7
|Name||International Conference on Information and Knowledge Management, Proceedings|
|Conference||28th ACM International Conference on Information and Knowledge Management, CIKM 2019|
|Period||19/11/3 → 19/11/7|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP; Ministry of Science, ICT & Future Planning) (No. NRF-2019R1A2B5B01070555).
© 2019 Association for Computing Machinery.
All Science Journal Classification (ASJC) codes
- Decision Sciences(all)
- Business, Management and Accounting(all)