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Learning Region Similarity over Spatial Knowledge Graphs with Hierarchical Types and Semantic Relations

Published: 03 November 2019 Publication History

Abstract

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.

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cover image ACM Conferences
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management
November 2019
3373 pages
ISBN:9781450369763
DOI:10.1145/3357384
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 November 2019

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Author Tags

  1. deep learning
  2. entity embedding
  3. region similarity
  4. spatial knowledge graph

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  • National Research Foundation of Korea(NRF)

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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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  • (2023)News Recommendation Based on User Topic and Entity Preferences in Historical BehaviorInformation10.3390/info1402006014:2(60)Online publication date: 18-Jan-2023
  • (2023)A Spatial and Adversarial Representation Learning Approach for Land Use Classification with POIsACM Transactions on Intelligent Systems and Technology10.1145/362782414:6(1-25)Online publication date: 14-Nov-2023
  • (2023)Learning Region Similarities via Graph-Based Deep Metric LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.325380235:10(10237-10250)Online publication date: 1-Oct-2023
  • (2022)Estimating urban functional distributions with semantics preserved POI embeddingInternational Journal of Geographical Information Science10.1080/13658816.2022.204051036:10(1905-1930)Online publication date: 8-Mar-2022
  • (2020)News Recommendation with Topic-Enriched Knowledge GraphsProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3411932(695-704)Online publication date: 19-Oct-2020

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