Semantic Annotation and Spatio-Temporal Search of Open Datasets

DOI

10.2991/978-94-6463-046-6_84How to use a DOI?

Keywords

Open Government Data; Semantic Annotation; Dataset Search; Metadata; Ontology

Abstract

In today’s big data era with the rapid development of the Internet, information and data are exploding, and this huge amount of data is available for us to process and use. There are 193 local government data open platforms in China, and the number of open datasets has reached more than 300,000. Reuse of these datasets is particularly critical to the development of social economy, politics, and production. Accurately finding the required datasets has become a new research hot spot. Improving the Web search discoverability of datasets has become one of the key initiatives to promote data flow and build a data ecosystem. Google’s one-stop search engine fills the gap in dataset search, but there are two limitations: incomplete collection of datasets and the lack of spatial and temporal search. Therefore, this paper constructs a Spatio-temporal ontology based on the situation of the domestic open data platform and temporal and spatial properties. The dataset is semantically annotated based on the Spatio-temporal ontology. The paper crawl the annotated structured data and store it in CSV files, and then build a Neo4j dataset search system to conduct the cross-platform search of the dataset through temporal or spatial information. The research in this paper helps improving the discoverability, interconnectivity, and reusability of Spatio-temporal datasets, and promotes the formation of the data sharing ecosystem for China’s open government data. It also helps the cross-border flow of open government data and its integration into the international data ecosystem, and has great reference value for the development of the search engine for open datasets in China.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Xiaofeng Yan
AU  - Jun Zhai
AU  - Yalin Zhou
AU  - Jia Chen
PY  - 2022
DA  - 2022/12/17
TI  - Semantic Annotation and Spatio-Temporal Search of Open Datasets
BT  - Proceedings of the 2022 2nd International Conference on Computer Technology and Media Convergence Design (CTMCD 2022)
PB  - Atlantis Press
SP  - 737
EP  - 747
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-046-6_84
DO  - 10.2991/978-94-6463-046-6_84
ID  - Yan2022
ER  -