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A comprehensive overview of RDF for spatial and spatiotemporal data management

Published online by Cambridge University Press:  22 June 2021

Fu Zhang Open the ORCID record for Fu Zhang [Opens in a new window] ,
Qingzhe Lu ,
Zhenjun Du ,
Xu Chen  and
Chunhong Cao
Fu Zhang
Affiliation:
School of Computer Science and Engineering, Northeastern University, Shenyang, China; e-mails: [email protected], [email protected]
Qingzhe Lu
Affiliation:
School of Computer Science and Engineering, Northeastern University, Shenyang, China; e-mails: [email protected], [email protected]
Zhenjun Du
Affiliation:
SIASUN Robot & Automation CO., Ltd., Shenyang, China; e-mail: [email protected]
Xu Chen
Affiliation:
North Minzu University, Yinchuan, Ningxia, China; e-mail: [email protected]
Chunhong Cao
Affiliation:
School of Computer Science and Engineering, Northeastern University, Shenyang, China; e-mails: [email protected], [email protected] SIASUN Robot & Automation CO., Ltd., Shenyang, China; e-mail: [email protected]
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Abstract

Currently, a large amount of spatial and spatiotemporal RDF data has been shared and exchanged on the Internet and various applications. Resource Description Framework (RDF) is widely accepted for representing and processing data in different (including spatiotemporal) application domains. The effective management of spatial and spatiotemporal RDF data are becoming more and more important. A lot of work has been done to study how to represent, query, store, and manage spatial and spatiotemporal RDF data. In order to grasp and learn the main ideas and research results of spatial and spatiotemporal RDF data, in this paper, we provide a comprehensive overview of RDF for spatial and spatiotemporal data management. We summarize spatial and spatiotemporal RDF data management from several essential aspects such as representation, querying, storage, performance assessment, datasets, and management tools. In addition, the direction of future research and some comparisons and analysis are also discussed in depth.

Type
Review
Information
The Knowledge Engineering Review , Volume 36 , 2021 , e10
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Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Ali, W., Saleem, M., Yao, B., et al. 2020. Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey. arXiv preprint arXiv:2009.10331.Google Scholar
Allen, J. F. 1983. Maintaining knowledge about temporal intervals. Communications of the ACM 26(11), 832–843.CrossRefGoogle Scholar
AllegroGraph, h ttps://allegrograph.com Google Scholar
Analyti, A. & Pachoulakis, I. 2008. A survey on models and query languages for temporally annotated RDF. International Journal of Advanced Computer Science & Applications 1(3), 28–35.Google Scholar
Athanasiou, S., Bezati, L., Giannopoulos, G., Patoumpas, K. & Skoutas, D. 2012. GeoKnow - Making the web an exploratory for geospatial knowledge. Market and Research Overview.Google Scholar
Battle, R. & Kolas, D. 2012. Enabling the geospatial semantic web with parliament and GeoSPARQL. Semantic Web 3(4), 355–370.Google Scholar
Bellini, P. & Nesi, P. 2018. Performance assessment of RDF graph databases for smart city services. Journal of Visual Languages & Computing 45, 24–38.CrossRefGoogle Scholar
Bereta, K., Dogani, K., Garbis, G., et al. 2013. An implementation of a temporal and spatial extension of RDF and SPARQL on top of MonetDB-Phase II.Google Scholar
Bereta, K., Smeros, P. & Koubarakis, M. 2013. Representation and querying of valid time of triples in linked geospatial data. In ESWC 2013, 259–274.Google Scholar
Bereta, K., Xiao, G., Koubarakis, M., et al. 2016. Ontop-spatial: Geospatial data integration using GeoSPARQL-to-SQL translation. In Proceedings of the 15th International Semantic Web Conference, Posters & Demonstrations Track (ISWC).Google Scholar
Bereta, K., Xiao, G., Koubarakis, M. 2019. Ontop-spatial: Ontop of geospatial databases. Journal of Web Semantics 58, 100514.CrossRefGoogle Scholar
Berners-Lee, T., Hendler, J. & Lassila, O. 2001. The semantic web. Scientific American 284(5), 34–43.CrossRefGoogle Scholar
Bizer, C., Heath, T. & Berners-Lee, T. 2009. Linked data-the story so far. International Journal on Semantic Web and Information Systems 5(3), 1–22.Google Scholar
Brodt, A., Nicklas, D. & Mitschang, B. 2010. Deep integration of spatial query processing into native RDF triple stores. In Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, 33–42.Google Scholar
Cai, Z., Kalamatianos, G., Fakas, G. J., et al. 2020. Diversified spatial keyword search on RDF data. The VLDB Journal, 1–19.Google Scholar
Candan, K. S., Liu, H. & Suvarna, R. 2001. Resource description framework: Metadata and its applications. ACM SIGKDD Explorations Newsletter 3(1), 6–19.CrossRefGoogle Scholar
Chawla, T., Singh, G., Pilli, E. S., et al. 2020. Storage, partitioning, indexing and retrieval in big RDF frameworks: A survey. Computer Science Review 38, 100309.CrossRefGoogle Scholar
Chbeir, R., Amghar, Y. & Flory, A. 2003. Novel indexing method of relations between salient objects. Effective Databases for Text & Document Management, 174–182.CrossRefGoogle Scholar
Christodoulou, G. 2011. CHOROS: A reasoning and query engine for qualitative spatial information. Dissertion Thesis, Technical University of Crete, Greece.Google Scholar
Claramunt, C. 2020. Ontologies for geospatial information: Progress and challenges ahead. Journal of Spatial Information Science 2020(20), 35–41.Google Scholar
Clementini, E. & Di Felice, P. 1995. A comparison of methods for representing topological relationships. Information Sciences-Applications 3(3), 149–178.CrossRefGoogle Scholar
Cui, Z., Cohn, A. G. & Randell, D. A. 1993. Qualitative and topological relationships in spatial databases. In Advances in Spatial Databases.CrossRefGoogle Scholar
Date, C. J., Darwen, H. & Lorentzos, N. 2002. Temporal Data & The Relational Model. Elsevier.Google Scholar
Dorne, J., Aussenac-Gilles, N., Comparot, C., et al. 2020. LandCover2RDF: An API for computing the land cover of a geographical area and generating the RDF graph. European Semantic Web Conference. Springer, 73–78.Google Scholar
Egenhofer, M. & Herring, J. 1991. Categorizing binary topological relationships between regions, lines and points in geographic database. Technical report, Department of Surveying Engineering, University of Maine, Urono, ME.Google Scholar
Eom, S., Jin, X. & Lee, K. H. 2020. Efficient generation of spatiotemporal relationships from spatial data streams and static data. Information Processing & Management 57(3), 102205.CrossRefGoogle Scholar
Fellbaum, C. 1998. WordNet: An Electronic Lexical Database. MIT Press.CrossRefGoogle Scholar
Finkel, R. A. & Bentley, J. L. 1974. Quad trees a data structure for retrieval on composite keys. Acta Informatica 4(1), 1–9.CrossRefGoogle Scholar
Garbis, G., Kyzirakos, K. & Koubarakis, M. 2013. Geographica: A benchmark for geospatial RDF stores (long version). In Proceedings of the International Semantic Web Conference, Sydney, NSW, Australia, 343–359.Google Scholar
geometry2rdf Utility. h ttps://www.oeg-upm.net/index.php/en/technologies/151-geometry2rdf/index.html Google Scholar
GeoNames. http://www.geonames.org Google Scholar
GeoRDF. h ttps://www.w3.org/wiki/GeoRDF Google Scholar
Giannopoulos, G., Vitsas, N., Karagiannakis, N, et al. 2015. FAGI-gis: A tool for fusing geospatial RDF data. In European Semantic Web Conference. Springer, 2015, 51–57.Google Scholar
GML, Geography Markup Language. h ttps://www.ogc.org/standards/gml Google Scholar
GraphDB (Former OWLIM). https://www.ontotext.com/products/graphdb/ Google Scholar
Gür, N., Pedersen, T. B., Zimnyi, E., et al. 2018. A foundation for spatial data warehouses on the semantic web. Semantic Web 9(5), 557–587.CrossRefGoogle Scholar
Gutierrez, C., Hurtado, C. & Vaisman, A. 2005. Temporal RDF. In Proceedings of European Conference on Semantic Web. Springer, 93–107.Google Scholar
Gutierrez, C., Hurtado, C. & Vaisman, A. 2007. Introducing time into RDF. IEEE Transactions on Knowledge and Data Engineering 19(2), 207–218.CrossRefGoogle Scholar
Guttman, A. 1984. R-trees: A dynamic index structure for spatial searching. In Proceedings of the 1984 ACM SIGMOD International Conference on Management of Data, 47–57.Google Scholar
Hamdi, F., Abadie, N., Bucher, B. & Feliachi, A. 2014. Geomrdf: A geodata converter with a fine-grained structured representation of geometry in the web. In The 1st International Workshop on Geospatial Linked Data (GeoLD), 1–12.Google Scholar
Hoffart, J., Suchanek, F. M., Berberich, K., et al. 2013. YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia. Artificial Intelligence 194, 28–61.CrossRefGoogle Scholar
Huang, W., Raza, S. A., Mirzov, O., et al. 2019. Assessment and benchmarking of spatially enabled RDF stores for the next generation of spatial data infrastructure. ISPRS International Journal of Geo-Information 8(7), 310.CrossRefGoogle Scholar
Ioannidis, T., Garbis, G., Kyzirakos, K., et al. 2019. Evaluating geospatial RDF stores using the benchmark Geographica 2. arXiv preprint arXiv:1906.01933.Google Scholar
ISO 19125-1: 2004. Geographic information-simple feature access.Google Scholar
ISO 19156. Geographic information-observations and measurements.Google Scholar
ISO 19109: 2005. Geographic information-rules for application schema.Google Scholar
ISO 19107: 2003. Geographic information-spatial schema.Google Scholar
Jena. h ttp://jena.apache.org/ Google Scholar
Jin, X., Shin, S., Jo, E., et al. 2018. Collective keyword query on a spatial knowledge base. IEEE Transactions on Knowledge and Data Engineering 31(11), 2051–2062.CrossRefGoogle Scholar
Kolas, D. 2008. A benchmark for spatial semantic web systems. In International Workshop on Scalable Semantic Web Knowledge Base Systems.Google Scholar
Koubarakis, M., Kyzirakos, K., Nikolaou, B., et al. 2012. A data model and query language for an extension of RDF with time and space. Technical Report.CrossRefGoogle Scholar
Koubarakis, M. & Kyzirakos, K. 2010. Modeling and querying metadata in the semantic sensor web: The model stRDF and the query language stSPARQL. In Extended Semantic Web Conference. Springer, 425–439.Google Scholar
Kuper, G., Ramaswamy, S., Shim, K. & Su, J. 1998. A constraint-based spatial extension to SQL. In Proceedings of the 6th International Symposium on Advances in Geographic Information Systems.Google Scholar
Kyzirakos, K., Karpathiotakis, M. & Koubarakis, M. 2012. Strabon: A semantic geospatial DBMS. In International Semantic Web Conference. Springer, 295–311.Google Scholar
Kyzirakos, K., Savva, D., Vlachopoulos, I., et al. 2018. GeoTriples: Transforming geospatial data into RDF graphs using R2RML and RML mappings. Journal of Web Semantics 52, 16–32.CrossRefGoogle Scholar
Leeka, J., Bedathur, S., Bera, D., et al. 2017. STREAK: An efficient engine for processing top-k SPARQL queries with spatial filters. arXiv:1710.07411v1.Google Scholar
Leeka, J., Bedathur, S., Bera, D., et al. 2016. Quark-X: An efficient top-k processing framework for RDF quad stores. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 831–840.Google Scholar
Lehmann, J., Isele, R., Jakob, M., et al. 2015. DBpedia–A large-scale, multilingual knowledge base extracted from Wikipedia. Semantic Web 6(2), 167–195.Google Scholar
Liagouris, J., Mamoulis, N., Bouros, P., et al. 2014. An effective encoding scheme for spatial RDF data. In Proceedings of the VLDB Endowment 7(12), 1271–1282.CrossRefGoogle Scholar
LinkedGeoData, LGD. h ttp://linkedgeodata.org Google Scholar
Nandal, R. 2013. Spatio-temporal database and its models: A review. IOSR Journal of Computer Engineering 11(2), 91–100.CrossRefGoogle Scholar
Neumann, T. & Weikum, G. 2008. RDF-3x: A risc-style engine for RDF. PVLDB 1(1), 647–659.Google Scholar
Nikolaou, C. & Koubarakis, M. 2012. Querying linked geospatial data with incomplete information. In 5th International Terra Cognita Workshop - Foundations, Technologies and Applications of the Geospatial Web and in conjunction with the 11th International Semantic Web Conference.Google Scholar
Nikolaou, C. & Koubarakis, M. 2013. Incomplete information in RDF. In International Conference on Web Reasoning and Rule Systems, 138–152.Google Scholar
Nikitopoulos, P., Vlachou, A., Doulkeridis, C., et al. DiStRDF: Distributed spatio-temporal RDF Queries on Spark. In EDBT/ICDT Workshops, 125–132.Google Scholar
Nikitopoulos, P., Vlachou, A., Doulkeridis, C., et al. 2019. Parallel and scalable processing of spatio-temporal RDF queries using Spark. GeoInformatica, 1–31.Google Scholar
OGC. http://www.opengeospatial.org/ Google Scholar
OGC GeoSPARQL - A Geographic Query Language for RDF Data. 2012. OGC 11-052r4.Google Scholar
OGC 07-036, Geography Markup Language (GML) Encoding Standard, Version 3.2.1.Google Scholar
OpenGIS Implementation Specification for Geographic information - Simple feature access - Part 1: Common architecture (05-126, 06-103r3, 06-103r4), current version 1.2.1.Google Scholar
OpenGIS Implementation Specification for Geographic information - Simple feature access - Part 2: SQL option. 2010.Google Scholar
OpenStreetMap dataset. http://www.openstreetmap.org/ Google Scholar
Oracle. 2005. Oracle spatial resource description framework (RDF) 10g release 2.Google Scholar
OWL 2 Web Ontology Language Document Overview (Second Edition), W3C Recommendation 11 December 2012. https://www.w3.org/TR/owl2-overview/ Google Scholar
ÖZsu, M. T. 2016. A survey of RDF data management systems. Frontiers of Computer Science 10(3), 418–432.CrossRefGoogle Scholar
Pandey, V., van Renen, A., Kipf, A., et al. 2020. The case for learned spatial indexes. In 2nd International Workshop on Applied AI for Database Systems and Applications (AIDB 20), 1–9.Google Scholar
Papadias, D. & Theodoridis, Y. 1997. Spatial relations, minimum bounding rectangles, and spatial data structures. International Journal on Geographic Information Systems 11(2), 111–138.Google Scholar
Parliament. h ttps://github.com/SemWebCentral/parliament Google Scholar
Paton, N. W., Williams, M. H., Dietrich, K., et al. 2000. ESPA: A benchmark for vector spatial databases. In British National Conference on Databases, 81–101.Google Scholar
Patroumpas, K., Giannopoulos, G. & Athanasiou, S. 2014. Towards GeoSpatial semantic data management: Strengths, weaknesses, and challenges ahead. In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 301–310.Google Scholar
Patroumpas, K., Alexakis, M., Giannopoulos, G., et al. 2014. TripleGeo: An ETL tool for transforming geospatial data into RDF triples. In EDBT/ICDT Workshops, 275–278.Google Scholar
Pelekis, N., Theodoulidis, B., Kopanakis, I., et al. 2004. Literature review of spatio-temporal database models. Knowledge Engineering Review 19(3), 235–274.Google Scholar
Pérez, J., Arenas, M. & Gutierrez, C. 2006. Semantics and complexity of SPARQL. In ISWC 2006, 30–43.Google Scholar
Perry, M. 2008. A framework to support spatial, temporal and thematic analytics over semantic web data. PhD Thesis, Wright State University.Google Scholar
Perry, M., Jain, P. & Sheth, A. 2011. SPARQL-ST: Extending SPARQL to support spatiotemporal queries. Semantic Web & Beyond.CrossRefGoogle Scholar
Perry, M., Estrada, A. & Das, S., et al. 2015. Developing GeoSPARQL applications with oracle spatial and graph. In ISWC 2015, 57–61.Google Scholar
PostGIS. h ttps://postgis.net Google Scholar
Randell, D., Cui, Z. & Cohn, A. 1992. A spatial logic based on regions and connection. In Proceedings of the 3rd International Conference on Knowledge Representation and Reasoning (KR 1992), Cambridge, MA, 165–176.Google Scholar
Quoca, H. N. M., Serranob, M., Mauc, H. N., et al. 2019. A performance study of RDF stores for linked sensor data.Google Scholar
Rathee, S. & Yadav, A. 2013. Survey on spatio-temporal database and data models with relevant features. International Journal of Scientific and Research Publications 3(1), 1–5.Google Scholar
Raza, A. 2019. Comparison of geospatial support in RDF stores: Evaluation for ICOS carbon portal metadata. Master Thesis in Geographical Information Science.Google Scholar
RDF 1.1 Primer, W3C Recommendation.https://www.w3.org/TR/rdf11-mt/ Google Scholar
Renz, J. & Nebel, B. 2007. Qualitative spatial reasoning using constraint calculi. In Handbook of Spatial Logics. Springer, 161–215.Google Scholar
Revesz, P. Z. 2002. Introduction to Constraint Databases. Springer.Google Scholar
Ronzhin, S., Folmer, E., Lemmens, R., et al. 2019. Next generation of spatial data infrastructure: lessons from linked data implementations across Europe. International Journal of Spatial Data Infrastructures Research 14, 83–107.Google Scholar
Salas, J. & Harth, A. 2011. Finding spatial equivalences across multiple RDF datasets. In Proceedings of the Terra Cognita Workshop on Foundations, Technologies and Applications of the Geospatial Web, Bonn, Germany: CEUR, 114–126.Google Scholar
Salas, J., Harth, A., et al. 2011. Neo-Geo Vocabulary: Defining a shared RDF representation for GeoData. Public Draft, May 2011.Google Scholar
Santipantakis, G. M., Apostolos, G., Kostas, P., et al. 2020. SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources. Future Generation Computer Systems 110, 540–555.CrossRefGoogle Scholar
Saveta, T., Fundulaki, I., Flouris, G., et al. 2018. SPgen: A benchmark generator for spatial link discovery tools. In International Semantic Web Conference. Springer, Cham, 408–423.Google Scholar
Schneider, M. 2009. Spatial and spatio-temporal data models and languages. In Encyclopedia of Database Systems, Liu, L. & ÖZsu, M. T. (eds). Springer US, 2681–2685.Google Scholar
Sejdiu, G., Ermilov, I., Lehmann, J., et al. 2018. DistLODStats: Distributed computation of RDF dataset statistics. In International Semantic Web Conference, 206–222.Google Scholar
Sesame (Now is RDF4J Project).https://rdf4j.org Google Scholar
Sherif, M. A. M. 2016. Automating geospatial RDF dataset integration and enrichment. Universität Leipzig, 1–165.Google Scholar
Sheth, A. & Perry, M. 2008. Traveling the semantic web through space, time, and theme. IEEE Internet Computing 12(2), 81–86.CrossRefGoogle Scholar
Shi, J., Wu, D. & Mamoulis, N. 2016. Top-k relevant semantic place retrieval on spatial RDF data. In Proceedings of the 2016 International Conference on Management of Data, 1977–1990.Google Scholar
Shp2GeoSPARQL. https://github.com/jasaavedra/shp2geosparql Google Scholar
Simon, G. 2018. An Introduction to Geo Indexes and their performance characteristics.Google Scholar
Smeros, P. & Koubarakis, M. 2016. Discovering spatial and temporal links among RDF data. In WWW Workshop: Linked Data on the Web (LDOW). Google Scholar
Snodgrass, R. 7 Ahn, I. 1985. A taxonomy of time in databases. In Proceedings of ACM SIGMOD International Conference on Management of Data, 236–246.Google Scholar
SPARQL 1.1 Query Language W3C Recommendation. 21 March 2013. https://www.w3.org/TR/sparql11-query/ Google Scholar
Stadler, C., Martin, M., & Auer, S. 2014. Exploring the web of spatial data with facete. In Proceedings of the 23rd International Conference on World Wide Web, 175–178.Google Scholar
Stadler, C., Lehmann, J., Hffner, K., et al. 2012. Linkedgeodata: A core for a web of spatial open data. Semantic Web 3(4), 333–354.CrossRefGoogle Scholar
Stardog. h ttps://www.stardog.com Google Scholar
Taylor, K. & Parsons, E. 2015. Where is everywhere: Bringing location to the web. IEEE Internet Computing 19(2), 83–87.Google Scholar
Theocharidis, K., Liagouris, J., Mamoulis, N., et al. 2019. SRX: Efficient management of spatial RDF data. The VLDB Journal 28(5), 703–733.CrossRefGoogle Scholar
Tran, B.H., Aussenac-Gilles, N., Comparot, C., et al. 2020. Semantic integration of raster data for earth observation: An RDF dataset of territorial unit versions with their land cover. ISPRS International Journal of Geo-Information 9(9), 503, 1–20.Google Scholar
UlutaŞ Karakol, D., Kara, G., Ylmaz, C., et al. 2018. Semantic linking spatial RDF data to the web data sources. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.Google Scholar
uSeekM. h ttps://www.openhub.net/p/useekm Google Scholar
van den Brink, L., Janssen, P., Quak, W., et al. 2014. Linking spatial data: Automated conversion of geo-information models and GML data to RDF. International Journal of Spatial Data Infrastructures Research 9, 59–85.Google Scholar
van den Brink, L., Barnaghi, P., Tandy, J., et al. 2019. Best practices for publishing, retrieving, and using spatial data on the web. Semantic Web 10(1), 95–114.CrossRefGoogle Scholar
Vlachou, A., Doulkeridis, C., Glenis, A., et al. 2019. Efficient spatio-temporal RDF query processing in large dynamic knowledge bases. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 439–447.Google Scholar
Vaisman, A. & Chentout, K. 2019. Mapping spatiotemporal data to RDF: A SPARQL endpoint for Brussels. ISPRS International Journal of Geo-Information 8(8), 353.CrossRefGoogle Scholar
Virtuoso Universal Server. https://virtuoso.openlinksw.com Google Scholar
W3C GEO. 2003. http://www.w3.org/2003/01/geo/, W3C Semantic Web Interest Group.Google Scholar
W3C Geospatial Vocabulary, W3C Incubator Group Report, 23 October 2007. https://www.w3.org/2005/ Incubator/geo/XGR-geo-20071023/ Google Scholar
Wang, C. J., Ku, W. S. & Chen, H. 2012. Geo-store: A spatially-augmented SPARQL query evaluation system. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems, 562–565.Google Scholar
Wang, D., Zou, L., Feng, Y., et al. 2013. S-store: An engine for large RDF graph integrating spatial information. In DASFAA, 31–47.CrossRefGoogle Scholar
Wang, D., Zou, L. & Zhao, D. 2014. g $ ^{st} $ -Store: An engine for large RDF graph integrating spatiotemporal information. In Proceeding of the 17th International Conference on Extending Database Technology (EDBT 2014), 652–655.Google Scholar
Wang, D., Zou, L. & Zhao, D. 2017. gst-Store: querying large spatiotemporal RDF graphs. Data and Information Management 1(2), 84–103.CrossRefGoogle Scholar
Wiemann, S. & Bernard, L. 2016. Spatial data fusion in spatial data infrastructures using linked data. International Journal of Geographical Information Science 30(4), 613–636.CrossRefGoogle Scholar
Wu, D., Hou, C., Xiao, E., et al. 2020. Semantic region retrieval from spatial RDF data. In International Conference on Database Systems for Advanced Applications. Springer, 415–431.Google Scholar
Wu, D., Zhou, H., Shi, J. & Mamoulis, N. 2020. Top-k relevant semantic place retrieval on spatiotemporal RDF data. VLDB 29(4), 893–917.CrossRefGoogle Scholar
Xiao, Z., Huang, L. & Zhai, X. 2009. Spatial information semantic query based on SPARQL. In Proceedings of SPIE, 7492, October 2009.Google Scholar
Zhai, X., Huang, L. & Xiao, Z. 2010. Geo-spatial query based on extended SPARQL. In 2010 18th International Conference on Geoinformatics, 1–4.Google Scholar
Zhao, T., Zhang, C., Anselin, L., et al. 2015. A parallel approach for improving Geo-SPARQL query performance. International Journal of Digital Earth 8(5), 383–402.CrossRefGoogle Scholar
Zhang, C, Beetz, J. & de Vries, B. 2018. BimSPARQL: Domain-specific functional SPARQL extensions for querying RDF building data. Semantic Web 9(6), 829–855.CrossRefGoogle Scholar
Zhu, L., Li, N. & Bai, L. 2020. Algebraic operations on spatiotemporal data based on RDF. ISPRS International Journal of Geo-Information 9(2), 80.CrossRefGoogle Scholar
Zou, L., Mo, J., Chen, L., et al. 2011. gStore: Answering SPARQL queries via subgraph matching. Proceedings of the VLDB Endowment 4(8), 482–493.Google Scholar