Skip header Section
ACM Books
Skip Bibliometrics Section
Geospatial Data Science: A Hands-on Approach for Building Geospatial Applications Using Linked Data TechnologiesJune 2023
- Editor:
- Manolis Koubarakis
Skip Abstract Section
Abstract
This introductory textbook teaches the simple development of geospatial applications based on the principles and software tools of geospatial data science. It introduces a new generation of geospatial technologies that have emerged from the development of the Semantic Web and the Linked Data paradigm, and shows how data scientists can use them to build environmental applications easily.
Geospatial data science is the science of collecting, organizing, analyzing, and visualizing geospatial data. Since around 2010, there has been extensive work in the area of geospatial data science using semantic technologies and linked data, from researchers in the areas of the Semantic Web, Geospatial Databases and Geoinformatics. The main results of this research have been the publication of the OGC standard GeoSPARQL and the implementation of a number of linked data tools supporting this standard. Up to now, there has been no textbook that enables someone to teach this material to undergraduate or graduate students.
The material of the book is developed in a tutorial style and it is appropriate for an introductory course on the subject. This can be an advanced undergraduate course or a graduate course offered by Computer Science or GIS faculty. It is a hands-on approach and every chapter contains exercises that help students master the material.
The book is accompanied by a Web site: https://ai.di.uoa.gr/geospatial-data-science-book/index.html where solutions to some of the exercises are given together with supplementary material such as datasets and code. Most of the material in the book has been tried in the “Knowledge Technologies” course taught by the editor in the Department of Informatics and Telecommunications of the National and Kapodistrian University of Athens since 2012.
Book Downloads
Skip Table Of Content Section
prefatory
Preface
Pages xxiii–xxviiihttps://doi.org/10.1145/3581906.3581907
chapter
chapter
chapter
Prefixes
Pages 291–292https://doi.org/10.1145/3581906.3581925
bibliography
Bibliography
Pages 293–312https://doi.org/10.1145/3581906.3581926
Authors’ Biography/Index
- J. Abhayaratna, N. Car, T. Homburg, F. Knibbe, and L. van den Brink. 2020a. OGC GeoSPARQL SWG Charter. https://portal.ogc.org/files/93345.Google Scholar
- J. Abhayaratna, L. Van Den Brink, N. Car, R. Atkinson, T. Homburg, F. Knibbe, K. McGlinn, A. Wagner, M. Bonduel, M. Holten Rasmussen, and F. Thiery. 2020b. OGC Benefits of Representing Spatial Data Using Semantic and Graph Technologies. OGC White Paper, Open Geospatial Consortium, Wayland, MA. https://docs.ogc.org/wp/19-078r1/19-078r1.html.Google Scholar
- E. Acheson, S. D. Sabbata, and R. S. Purves. 2017. A quantitative analysis of global gazetteers: Patterns of coverage for common feature types. Comput. Environ. Urban Syst. 64, 309–320. DOI: .Google Scholar
Cross Ref
- D. Ahlers. 2013. Assessment of the accuracy of GeoNames gazetteer data. In Proceedings of the 7th Workshop on Geographic Information Retrieval. ACM, 74–81. DOI: .Google Scholar
- A. F. Ahmed, M. A. Sherif, and A. N. Ngomo. 2018. RADON2—A buffered-intersection matrix computing approach to accelerate link discovery over geo-spatial RDF knowledge bases: OAEI2018 results. In International Workshop on Ontology Matching. 197–204. http://www.dit.unitn.it/∼pavel/om2018/papers/oaei18_paper13.pdf.Google Scholar
- D. Allemang, J. A. Hendler, and F. Gandon. 2020. Semantic Web for the Working Ontologist—Effective Modeling for Linked Data, RDFS, and OWL (3rd. ed.). Morgan Kaufmann.Google Scholar
- A. Andrejev. 2016. Semantic Web Queries over Scientific Data. Ph.D. Thesis.Google Scholar
- G. Antoniou, P. Groth, F. van Harmelen, and R. Hoekstra. 2012. A Semantic Web Primer (3rd. ed.). MIT Press. ISBN 978-0-262-01828-9.Google Scholar
- A. Artale, D. Calvanese, R. Kontchakov, and M. Zakharyaschev. 2009. The DL-lite family and relations. J. Artif. Intell. Res. 36, 1–69. DOI: .Google Scholar
- R. Atkinson and N. J. Car. 2019. The Profiles Vocabulary. https://www.w3.org/TR/dx-prof/.Google Scholar
- S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z. G. Ives. 2007. DBpedia: A nucleus for a Web of open data. In The Semantic Web, 6th International Semantic Web Conference, 2nd Asian Semantic Web Conference, ISWC 2007 + ASWC 2007, Busan, Korea, November 11–15, 2007, Vol. 4825: Lecture Notes in Computer Science. Springer, 722–735. DOI: .Google Scholar
- H. Augustin, M. Sudmanns, D. Tiede, S. Lang, and A. Baraldi. 2019. Semantic Earth observation data cubes. Data 4, 3, 102. DOI: .Google Scholar
- F. Baader, I. Horrocks, C. Lutz, and U. Sattler. 2017. An Introduction to Description Logic. Cambridge University Press. ISBN 978-0-521-69542-8. DOI: .Google Scholar
- S. Batsakis and E. G. M. Petrakis. 2010. SOWL: Spatio-temporal representation, reasoning and querying over the Semantic Web. In Proceedings of the 6th International Conference on Semantic Systems. ACM, 1–9. DOI: .Google Scholar
- D. Beckett, T. Berners-Lee, E. Prud’hommeaux, G. Carothers, and L. Machina. 2014. Turtle: Terse RDF Triple Language. W3C Recommendation February 25, 2014. https://www.w3.org/TR/turtle/.Google Scholar
- K. Bereta and M. Koubarakis. 2016. Ontop of geospatial databases. In Proceedings of the Semantic Web—ISWC 2016—15th International Semantic Web Conference, Part I, Kobe, Japan, October 17–21, 2016, Vol. 9981: Lecture Notes in Computer Science. Springer, 37–52. DOI: .Google Scholar
- K. Bereta, P. Smeros, and M. Koubarakis. 2013. Representation and querying of valid time of triples in linked geospatial data. In Proceedings of the Semantic Web: Semantics and Big Data, 10th International Conference, ESWC 2013, Montpellier, France, May 26–30, 2013, Vol. 7882: Lecture Notes in Computer Science. Springer, 259–274. DOI: .Google Scholar
- K. Bereta, H. Caumont, E. Goor, M. Koubarakis, D. Pantazi, G. Stamoulis, S. Ubels, V. Venus, and F. Wahyudi. 2018. From Copernicus big data to big information and big knowledge: A demo from the Copernicus App Lab project. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22–26, 2018. ACM, 1911–1914. DOI: .Google Scholar
- K. Bereta, H. Caumont, U. Daniels, E. Goor, M. Koubarakis, D. Pantazi, G. Stamoulis, S. Ubels, V. Venus, and F. Wahyudi. 2019a. The Copernicus App Lab project: Easy access to Copernicus data. In Advances in Database Technology—22nd International Conference on Extending Database Technology, EDBT 2019, Lisbon, Portugal, March 26–29, 2019. OpenProceedings.org, 501–511.Google Scholar
- K. Bereta, G. Xiao, and M. Koubarakis. 2019b. Ontop-spatial: Ontop of geospatial databases. J. Web Semant. 58, 100514. DOI: .Google Scholar
- D. Bilidas, T. Ioannidis, N. Mamoulis, and M. Koubarakis. 2022. Strabo 2: distributed management of massive geospatial RDF datasets. In U. Sattler, A. Hogan, C. M. Keet, V. Presutti, J. P. A. Almeida, H. Takeda, P. Monnin, G. Pirrò, and C. d’Amato (Eds.), The Semantic Web - ISWC 2022 - 21st International Semantic Web Conference, Virtual Event, October 23-27, 2022, Proceedings, Vol. 13489: Lecture Notes in Computer Science, Springer, 411–427. DOI: .Google Scholar
- C. Bizer, J. Lehmann, G. Kobilarov, S. Auer, C. Becker, R. Cyganiak, and S. Hellmann. 2009. DBpedia—A crystallization point for the Web of Data. J. Web Semant. 7, 3, 154–165. DOI: .Google Scholar
- J. Blower, D. Clifford, P. Goncalves, and M. Koubarakis. 2014. The MELODIES project: Integrating diverse data using Linked Data and cloud computing. In Proceedings of the 2014 Conference on Big Data from Space (BiDS ’14), November 12–14, 2014, Frascati. University of Reading, 244–247. http://centaur.reading.ac.uk/38554/.Google Scholar
- H. Boley, G. Hallmark, M. Kifer, A. Paschke, A. Polleres, and D. Reynolds. RIF Core Dialect (2nd. ed.). W3C Recommendation February 5, 2013. http://www.w3.org/TR/rif-core/.Google Scholar
- K. D. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor. 2008. Freebase: A collaboratively created graph database for structuring human knowledge. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, Vancouver, BC, Canada, June 10–12, 2008. ACM, 1247–1250. DOI: .Google Scholar
- B. Bondaruk, S. Roberts, and C. Robertson. May. 2020. Assessing the state of the art in discrete global grid systems: OGC criteria and present functionality. Geomatica 74, 1, 9–30. DOI: .Google Scholar
- A. Both, D. Diefenbach, K. Singh, S. Shekarpour, D. Cherix, and C. Lange. 2016. Qanary—A methodology for vocabulary-driven open question answering systems. In H. Sack, E. Blomqvist, M. d’Aquin, C. Ghidini, S. P. Ponzetto, and C. Lange (Eds.), Proceedings of the Semantic Web. Latest Advances and New Domains—13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Vol. 9678: Lecture Notes in Computer Science. Springer, 625–641. DOI: .Google Scholar
- A. Both, K. Singh, D. Diefenbach, and I. Lytra. 2017. Rapid engineering of QA systems using the light-weight Qanary architecture. In J. Cabot, R. D. Virgilio, and R. Torlone (Eds.), Proceedings of the Web Engineering—17th International Conference, ICWE 2017, Rome, Italy, June 5–8, 2017, Vol. 10360: Lecture Notes in Computer Science. Springer, 544–548. DOI: .Google Scholar
- R. J. Brachman and H. J. Levesque. 2004. Knowledge Representation and Reasoning. Elsevier. ISBN 978-1-55860-932-7. DOI: .Google Scholar
- R. Brachman, D. L. McGuinness, P. Patel-Schneider, L. Resnick, and A. Borgida. 1991. Living with CLASSIC: When and how to use a KL-ONE-like language. In J. Sowa (Ed.), Principles of Semantic Networks: Explorations in the Representation of Knowledge. Morgan-Kaufmann, 401–456. DOI: .Google Scholar
- L. V. Brink, C. Portele, and P. Vretanos. 2010. Geography Markup Language (GML) Simple Features Profile. Open Geospatial Consortium Standard. http://portal.opengeospatial.org/files/?artifact_id=39853.Google Scholar
- A. Brodt, D. Nicklas, and B. Mitschang. 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. ACM, 33–42. DOI: .Google Scholar
- J. Broekstra, A. Kampman, and F. Van Harmelen. 2002. Sesame: A generic architecture for storing and querying RDF and RDF schema. In International Semantic Web Conference, Vol. 2342: Lecture Notes in Computer Science. Springer, 54–68. DOI: .Google Scholar
- S. Burgstaller, W. Angermair, F. Niggemann, S. Migdall, H. Bach, I. Vlahopoulos, D. Savva, P. Smeros, G. Stamoulis, K. Bereta, and M. Koubarakis. 2017. LEOpatra: A mobile application for smart fertilization based on linked data. In Proceedings of the 8th International Conference on Information and Communication Technologies in Agriculture, Food and Environment, Chania, Crete, Greece, September 21–24, 2017, 160–171. https://ceur-ws.org/Vol-2030/HAICTA_2017_paper17.pdf.Google Scholar
- D. Calvanese, B. Cogrel, S. Komla-Ebri, R. Kontchakov, D. Lanti, M. Rezk, M. Rodriguez-Muro, and G. Xiao. 2017. Ontop: Answering SPARQL queries over relational databases. Semant. Web 8, 3, 471–487. DOI: .Google Scholar
- N. J. Car and T. Homburg. 2022. GeoSPARQL 1.1: Motivations, details and applications of the decadal update to the most important geospatial LOD standard. ISPRS Int. J. Geo-Inf. 11, 2, 117. DOI: .Google Scholar
- W. Chen. 2014. Parameterized spatial SQL translation for geographic question answering. In Proceedings of the 2014 IEEE International Conference on Semantic Computing, Newport Beach, CA. IEEE, 23–27. DOI: .Google Scholar
- W. Chen, E. Fosler-Lussier, N. Xiao, S. Raje, R. Ramnath, and D. Sui. 2013. A synergistic framework for geographic question answering. In Proceedings of the 2013 IEEE International Conference on Semantic Computing, Irvine, CA. IEEE, 94–99. DOI: .Google Scholar
- K. Chentout and A. A. Vaisman. 2013. Adding spatial support to R2RML mappings. In Y. T. Demey and H. Panetto (Eds.), Proceedings of the Move to Meaningful Internet Systems: OTM 2013 Workshops—Confederated International Workshops: OTM Academy, OTM Industry Case Studies Program, ACM, EI2N, ISDE, META4eS, ORM, SeDeS, SINCOM, SMS, and SOMOCO 2013, Graz, Austria, September 9–13, 2013, Vol. 8186: Lecture Notes in Computer Science. Springer, 398–407. DOI: .Google Scholar
- P. Christen. 2012. Data Matching—Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Data-Centric Systems and Applications. Springer. DOI: .Google Scholar
- V. Christophides, V. Efthymiou, and K. Stefanidis. 2015. Entity Resolution in the Web of Data. Synthesis Lectures on the Semantic Web: Theory and Technology. Morgan & Claypool. DOI: .Google Scholar
- V. Christophides, V. Efthymiou, T. Palpanas, G. Papadakis, and K. Stefanidis. 2021. An overview of end-to-end entity resolution for big data. ACM Comput. Surv. 53, 6, 1–42. DOI: .Google Scholar
- E. Clementini and P. Di Felice. 1995. A comparison of methods for representing topological relationships. Inf. Sci. Appl. 3, 3, 149–178. DOI: .Google Scholar
- E. Clementini, P. D. Felice, and P. van Oosterom. 1993. A small set of formal topological relationships suitable for end-user interaction. In Proceedings of the Advances in Spatial Databases, Third International Symposium, SSD ’93, Singapore, June 23–25, 1993, Vol. 692: Lecture Notes in Computer Science. Springer, 277–295. DOI: .Google Scholar
- A. G. Cohn and J. Renz. 2008. Qualitative spatial representation and reasoning. Found. Artif. Intell. 3, 551–596. DOI: .Google Scholar
- A. G. Cohn, S. Li, W. Liu, and J. Renz. 2014. Reasoning about topological and cardinal direction relations between 2-dimensional spatial objects. J. Artif. Intell. Res. 51, 493–532. DOI: .Google Scholar
- S. Das, S. Sundara, and R. Cyganiak. 2012. R2RML: RDB to RDF Mapping Language. W3C Recommendation September 27, 2012. http://www.w3.org/TR/r2rml/.Google Scholar
- A. de León, V. Saquicela, L. M. V. Blázquez, B. Villazón-Terrazas, F. Priyatna, and Ó. Corcho. 2010. Geographical linked data: A Spanish use case. In A. Paschke, N. Henze, and T. Pellegrini (Eds.), Proceedings the 6th International Conference on Semantic Systems, I-SEMANTICS 2010, Graz, Austria, September 1–3, 2010. ACM International Conference Proceeding Series. ACM, 1–3. DOI: .Google Scholar
- C. Debruyne, A. Meehan, É. Clinton, L. McNerney, A. Nautiyal, P. Lavin, and D. O’Sullivan. 2017. Ireland’s authoritative geospatial Linked Data. In Proceedings of the Semantic Web—ISWC 2017: 16th International Semantic Web Conference, Part II, Vienna, Austria, October 21–25, 2017, Vol. 10588: Lecture Notes in Computer Science. Springer, 66–74. DOI: .Google Scholar
- R. Dechter. 2003. Constraint Processing. Elsevier Morgan Kaufmann. ISBN 978-1-55860-890-0. .Google Scholar
- J. Devlin, M. Chang, K. Lee, and K. Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Vol. 1 (Long and Short Papers), Minneapolis, MN, June 2–7, 2019. Association for Computational Linguistics, 4171–4186. DOI: .Google Scholar
- D. Diefenbach, V. López, K. D. Singh, and P. Maret. 2018. Core techniques of question answering systems over knowledge bases: A survey. Knowl. Inf. Syst. 55, 3, 529–569. DOI: .Google Scholar
- L. Ding, G. Xiao, A. Pano, C. Stadler, and D. Calvanese. 2021. Towards the next generation of the LinkedGeoData project using virtual knowledge graphs. J. Web Semant. 71, 100662. DOI: .Google Scholar
- J. Dittrich and B. Seeger. 2000. Data redundancy and duplicate detection in spatial join processing. In Proceedings of the 16th International Conference on Data Engineering (ICDE ’00). IEEE, 535–546. DOI: .Google Scholar
- X. L. Dong and D. Srivastava. 2015. Big Data Integration. Synthesis Lectures on Data Management. Morgan & Claypool.Google Scholar
- A. Dsouza, N. Tempelmeier, and E. Demidova. 2021a. Towards neural schema alignment for OpenStreetMap and knowledge graphs. In Proceedings of the Semantic Web—ISWC 2021: 20th International Semantic Web Conference, ISWC 2021, Virtual Event, October 24–28, 2021, Vol. 12922: Lecture Notes in Computer Science. Springer-Verlag, Berlin, 56–73. ISBN 978-3-030-88360-7. DOI: .Google Scholar
- A. Dsouza, N. Tempelmeier, R. Yu, S. Gottschalk, and E. Demidova. 2021b. WorldKG: A world-scale geographic knowledge graph. In CIKM ’21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1–5, 2021. ACM, 4475–4484. DOI: .Google Scholar
- M. Dubey, S. Dasgupta, A. Sharma, K. Höffner, and J. Lehmann. 2016. AskNow: A framework for natural language query formalization in SPARQL. In H. Sack, E. Blomqvist, M. d’Aquin, C. Ghidini, S. P. Ponzetto, and C. Lange (Eds.), Proceedings of the Semantic Web. Latest Advances and New Domains—13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29–June 2, 2016, Vol. 9678: Lecture Notes in Computer Science. Springer, 300–316. DOI: .Google Scholar
- M. J. Egenhofer. 1989. A formal definition of binary topological relationships. In Proceedings of the Foundations of Data Organization and Algorithms, 3rd International Conference, FODO 1989, Paris, France, June 21–23, 1989, Vol. 367: Lecture Notes in Computer Science. Springer, 457–472. DOI: .Google Scholar
- M. J. Egenhofer. 2002. Toward the Semantic Geospatial Web. In ACM-GIS 2002, Proceedings of the Tenth ACM International Symposium on Advances in Geographic Information Systems, McLean, VA (near Washington, DC), November 8–9, 2002. ACM, 1–4. DOI: .Google Scholar
- M. Egenhofer and J. Herring. 1991. Categorizing Binary Topological Relations between Regions, Lines and Points in Geographic Databases. Technical Report. University of Maine, Orono. https://scholar.google.com/citations?user=vIIBTUAAAAAJ&hl=en.Google Scholar
- A. K. Elmagarmid, P. G. Ipeirotis, and V. S. Verykios. 2007. Duplicate record detection: A survey. IEEE Trans. Knowl. Data Eng. 19, 1, 1–16. DOI: .Google Scholar
- H. B. Enderton. 2001. A Mathematical Introduction to Logic (2nd. ed.). Academic Press. ISBN 978-0-12-238452-3. DOI: .Google Scholar
- D. Espinoza-Molina and M. Datcu. 2013. Earth-observation image retrieval based on content, semantics, and metadata. IEEE Trans. Geosci. Remote. Sens. 51, 11, 5145–5159. DOI: .Google Scholar
- D. Espinoza-Molina, C. Nikolaou, C. O. Dumitru, K. Bereta, M. Koubarakis, G. Schwarz, and M. Datcu. 2015. Very-high-resolution SAR images and linked open data analytics based on ontologies. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8, 4, 1696–1708. DOI: .Google Scholar
- O. Etzioni, M. Banko, and M. J. Cafarella. 2006. Machine reading. In Proceedings of the Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, Boston, MA, July 16–20, 2006. AAAI Press, 1517–1519.Google Scholar
- P. Ferragina and U. Scaiella. 2010. TAGME: On-the-fly annotation of short text fragments (by Wikipedia entities). In J. Huang, N. Koudas, G. J. F. Jones, X. Wu, K. Collins-Thompson, and A. An (Eds.), Proceedings of the 19th ACM Conference on Information and Knowledge Management, CIKM 2010, Toronto, Ontario, Canada, October 26–30, 2010. ACM, 1625–1628. DOI: .Google Scholar
- R. Fikes and T. Kehler. 1985. The role of frame-based representation in reasoning. Commun. ACM 28, 9, 904–920. DOI: .Google Scholar
- N. V. Findler (Ed.). 1979. Associative Networks: The Representation and Use of Knowledge by Computers. Academic Press. ISBN 978-0-12-256380-5. DOI: .Google Scholar
- A. U. Frank. 1992. Qualitative spatial reasoning about distances and directions in geographic space. J. Vis. Lang. Comput. 3, 4, 343–371. DOI: .Google Scholar
- C. Freksa, J. van de Ven, and D. Wolter. 2018. Formal representation of qualitative direction. Int. J. Geogr. Inf. Sci. 32, 12, 2514–2534. DOI: .Google Scholar
- Z. Gantner, M. Westphal, and S. Woelfl. 2008. GQR—A fast reasoner for binary qualitative constraint calculi. In AAAI Workshop on Spatial and Temporal Reasoning. Association for the Advancement of Artificial Intelligence.Google Scholar
- G. Garbis, K. Kyzirakos, and M. Koubarakis. 2013. Geographica: A benchmark for geospatial RDF stores (long version). In Proceedings of the Semantic Web—ISWC 2013—12th International Semantic Web Conference, Part II, Sydney, NSW, Australia, October 21–25, 2013, Vol. 8219: Lecture Notes in Computer Science. Springer, 343–359. DOI: .Google Scholar
- D. García Álvarez, M. T. Camacho Olmedo, M. Paegelow, and J. Mas. June. 2022. Land Use Cover Datasets and Validation Tools. Validation Practices with QGIS. Springer. DOI: .Google Scholar
- L. Getoor and A. Machanavajjhala. 2012. Entity resolution: Theory, practice & open challenges. Proc. VLDB Endow. 5, 12, 2018–2019. DOI: .Google Scholar
- B. Glimm and C. Ogbuji. 2013. SPARQL 1.1 Entailment Regimes. W3C Recommendation March 21, 2013. http://www.w3.org/TR/sparql11-entailment/.Google Scholar
- R. K. Goyal. April. 2000. Similarity Assessment for Cardinal Directions between Extended Spatial Objects. Ph.D. Dissertation.Google Scholar
- B. C. Grau, I. Horrocks, Y. Kazakov, and U. Sattler. 2008. Modular reuse of ontologies: Theory and practice. J. Artif. Intell. Res. 31, 273–318. DOI: .Google Scholar
- A. J. G. Gray, C. A. Goble, and R. Jimenez. 2017. Bioschemas: From potato salad to protein annotation. In N. Nikitina, D. Song, A. Fokoue, and P. Haase (Eds.), In Proceedings of the ISWC 2017 Posters & Demonstrations and Industry Tracks co-located with 16th International Semantic Web Conference (ISWC 2017), Vienna, Austria, October 23–25, 2017, Vol. 1963. CEUR Workshop Proceedings, CEUR-WS.org. http://ceur-ws.org/Vol-1963/paper579.pdf.Google Scholar
- M. Grigni, D. Papadias, and C. H. Papadimitriou. 1995. Topological inference. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI 95, Vol. 2, Montréal Québec, Canada, August 20–25, 1995. Morgan Kaufmann, 901–907. http://ijcai.org/Proceedings/95-1/Papers/116.pdf.Google Scholar
- T. R. Gruber. 1993. A translation approach to portable ontology specifications. Knowl. Acquis. 5, 2, 199–220. DOI: . See also http://www-ksl.stanford.edu/kst/what-is-an-ontology.html.Google Scholar
- R. Grütter, R. S. Purves, and L. Wotruba. 2017. Evaluating topological queries in Linked Data using DBpedia and GeoNames in Switzerland and Scotland. Trans. GIS. 21, 1, 114–133. DOI: .Google Scholar
- C. Gutiérrez and J. F. Sequeda. 2021. Knowledge graphs. Commun. ACM 64, 3, 96–104. DOI: .Google Scholar
- A. Guttman. 1984. R-trees: A dynamic index structure for spatial searching. In Proceedings of the Annual Meeting (SIGMOD ’84), Boston, MA, June 18–21, 1984. ACM, 47–57. DOI: .Google Scholar
- E. Hamzei. 2021. Place-Related Question Answering: From Questions to Relevant Answers. Ph.D. Dissertation.Google Scholar
- E. Hamzei, M. Tomko, and S. Winter. 2022. Translating place-related questions to GeoSPARQL queries. In Proceedings of the Web Conference (WWW). ACM, 902–911. DOI: .Google Scholar
- T. Heath and C. Bizer. 2011. Linked Data: Evolving the Web into a Global Data Space. Synthesis Lectures on the Semantic Web. Morgan & Claypool. DOI: .Google Scholar
- J. M. Hellerstein, J. F. Naughton, and A. Pfeffer. 1995. Generalized search trees for database systems. In Proceedings of the 21st International Conference on Very Large Data Bases (VLDB ’95), Zurich, Switzerland, September 11–15, 1995. Morgan Kaufmann, 562–573.Google Scholar
- A. Hernich, C. Lutz, A. Ozaki, and F. Wolter. 2015. Schema.org as a description logic. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25–31, 2015. AAAI Press, 3048–3054. http://ijcai.org/Abstract/15/430.Google Scholar
- J. R. Herring. 2010. OpenGIS Implementation Standard for Geographic information—Simple Feature Access—Part 2: SQL Option. Open Geospatial Consortium Standard. http://portal.opengeospatial.org/files/?artifact_id=25354.Google Scholar
- J. R. Herring. 2011. OpenGIS Implementation Standard for Geographic information—Simple Feature Access—Part 1: Common Architecture. Open Geospatial Consortium Standard. http://portal.opengeospatial.org/files/?artifact_id=25355.Google Scholar
- P. Hitzler, M. Krötzsch, and S. Rudolph. 2010. Foundations of Semantic Web Technologies. Chapman and Hall/CRC Press. ISBN 9781420090505. DOI: .Google Scholar
- P. Hitzler, M. Krötzsch, B. Parsia, P. F. Patel-Schneider, and S. Rudolph. 2012. OWL 2 Web Ontology Language Primer (2nd. ed.). W3C Recommendation. https://www.w3.org/TR/owl2-primer/.Google Scholar
- V. T. Ho, Y. Ibrahim, K. Pal, K. Berberich, and G. Weikum. 2019. Qsearch: Answering quantity queries from text. In Proceedings of the Semantic Web—ISWC 2019—18th International Semantic Web Conference, Part I, Auckland, New Zealand, October 26–30, 2019, Vol. 11778: Lecture Notes in Computer Science. Springer, 237–257. DOI: .Google Scholar
- J. Hoffart, F. M. Suchanek, K. Berberich, E. Lewis-Kelham, G. de Melo, and G. Weikum. 2011. YAGO2: Exploring and querying world knowledge in time, space, context, and many languages. In S. Srinivasan, K. Ramamritham, A. Kumar, M. P. Ravindra, E. Bertino, and R. Kumar (Eds.), Proceedings of the 20th International Conference on World Wide Web, WWW 2011, Hyderabad, India, March 28–April 1, 2011 (Companion Volume). ACM, 229–232. DOI: .Google Scholar
- J. Hoffart, F. M. Suchanek, K. Berberich, and G. Weikum. 2013. YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia. Artif. Intell. 194, 28–61. DOI: .Google Scholar
- K. Höffner, S. Walter, E. Marx, R. Usbeck, J. Lehmann, and A. N. Ngomo. 2017. Survey on challenges of question answering in the Semantic Web. Semant. Web. 8, 6, 895–920. DOI: .Google Scholar
- A. Hogan. 2020. The Web of Data. Springer. DOI: .Google Scholar
- A. Hogan, E. Blomqvist, M. Cochez, C. d’Amato, G. de Melo, C. Gutiérrez, J. E. L. Gayo, S. Kirrane, S. Neumaier, A. Polleres, R. Navigli, A.-C. N. Ngomo, S. M. Rashid, A. Rula, L. Schmelzeisen, J. F. Sequeda, S. Staab, and A. Zimmermann. 2020. Knowledge graphs. ACM Comput. Surv. 54, 4, 1–37. DOI: .Google Scholar
- T. Homburg, S. Staab, and D. Janke. 2020. GeoSPARQL+: Syntax, semantics and system for integrated querying of graph, raster and vector data. In Proceedings of the Semantic Web—ISWC 2020—19th International Semantic Web Conference, Part I, Athens, Greece, November 2–6, 2020, Vol. 12506: Lecture Notes in Computer Science. Springer, 258–275. DOI: .Google Scholar
- M. Horridge and P. F. Patel-Schneider. 2012. OWL 2 Web Ontology Language Manchester Syntax (2nd. ed.). W3C Working Group Note December 11, 2012. http://www.w3.org/TR/owl2-manchester-syntax/.Google Scholar
- T. Imieliński and W. Lipski, Jr. 1984. Incomplete information in relational databases. J. ACM 31, 4, 761–791. ISSN 0004-5411. DOI: .Google Scholar
- T. Ioannidis, G. Garbis, K. Kyzirakos, K. Bereta, and M. Koubarakis. 2021. Evaluating geospatial RDF stores using the benchmark Geographica 2. J. Data Semant. 10, 3–4, 189–228. DOI: .Google Scholar
- R. Isele and C. Bizer. 2012. Learning expressive linkage rules using genetic programming. Proc. VLDB Endow. 5, 11, 1638–1649. DOI: .Google Scholar
- R. Isele, A. Jentzsch, and C. Bizer. 2011. Efficient multidimensional blocking for link discovery without losing recall. In Fourteenth International Workshop on the Web and Databases WebDB, Athens, Greece, June 12, 2011.Google Scholar
- K. Jänich. 1984. Topology. Springer-Verlag, New York. ISBN 9781461270188. Translated by Levy, S.Google Scholar
- K. Janowicz, P. Hitzler, W. Li, D. Rehberger, M. Schildhauer, R. Zhu, C. Shimizu, C. K. Fisher, L. Cai, G. Mai, J. Zalewski, L. Zhou, S. Stephen, S. G. Estrecha, B. D. Mecum, A. Lopez-Carr, A. Schroeder, D. Smith, D. J. Wright, S. Wang, Y. Tian, Z. Liu, M. Shi, A. D’Onofrio, Z. Gu, and K. Currier. 2022. Know, know where, KnowWhereGraph: A densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence. AI Mag. 43, 1, 30–39. DOI: .Google Scholar
- P. C. Kanellakis, G. M. Kuper, and P. Z. Revesz. 1995. Constraint query languages. J. Comput. Syst. Sci. 51, 1, 26–52. DOI: .Google Scholar
- H. Kang, V. Sehgal, and L. Getoor. 2007. GeoDDupe: A novel interface for interactive entity resolution in geospatial data. In 11th International Conference on Information Visualization, IV 2007, July 4–6, 2007, Zürich, Switzerland. IEEE, 489–496. DOI: .Google Scholar
- N. Karalis, G. M. Mandilaras, and M. Koubarakis. 2019. Extending the YAGO2 knowledge graph with precise geospatial knowledge. In Proceedings of the Semantic Web—ISWC 2019—18th International Semantic Web Conference, Part II, Auckland, New Zealand, October 26–30, 2019, Vol. 11779: Lecture Notes in Computer Science. Springer, 181–197. DOI: .Google Scholar
- G. Karvounarakis, A. Magkanaraki, S. Alexaki, V. Christophides, D. Plexousakis, M. Scholl, and K. Tolle. 2004. RQL: A functional query language for RDF. In The Functional Approach to Data Management. Springer, 435–465. DOI: .Google Scholar
- A. Kiryakov, D. Ognyanov, and D. Manov. 2005. OWLIM—A pragmatic semantic repository for OWL. In International Conference on Web Information Systems Engineering, Vol. 3807: Lecture Notes in Computer Science. Springer, 182–192. DOI: .Google Scholar
- H. Knublauch and D. Kontokostas. 2017. Shapes Constraint Language (SHACL). World Wide Web Consortium Recommendation July 20, 2017. https://www.w3.org/TR/shacl/.Google Scholar
- M. Koubarakis and K. Kyzirakos. 2010. Modeling and querying metadata in the semantic sensor web: The model stRDF and the query language stSPARQL. In Proceedings of the Semantic Web: Research and Applications, 7th Extended Semantic Web Conference, ESWC 2010, Part I, Heraklion, Crete, Greece, May 30–June 3, 2010, Vol. 6088: Lecture Notes in Computer Science. Springer, 425–439. DOI: .Google Scholar
- M. Koubarakis, C. Kontoes, and S. Manegold. 2013. Real-time wildfire monitoring using scientific database and linked data technologies. In Proceedings of the Joint 2013 EDBT/ICDT Conferences, EDBT ’13, Genoa, Italy, March 18–22, 2013. ACM, 649–660. DOI: .Google Scholar
- M. Koubarakis, K. Kyzirakos, C. Nikolaou, G. Garbis, K. Bereta, R. Dogani, S. Giannakopoulou, P. Smeros, D. Savva, G. Stamoulis, G. Vlachopoulos, S. Manegold, C. Kontoes, T. Herekakis, I. Papoutsis, and D. Michail. September. 2016. Managing big, linked, and open Earth-observation data: Using the TELEIOS/LEO software stack. IEEE Geosci. Remote Sens. Mag. 4, 3, 23–37. DOI: .Google Scholar
- M. Koubarakis, K. Bereta, G. Papadakis, D. Savva, and G. Stamoulis. 2017. Big, linked geospatial data and its applications in Earth observation. IEEE Internet Comput. 21, 4, 87–91. DOI: .Google Scholar
- M. Koubarakis, K. Bereta, D. Bilidas, K. Giannousis, T. Ioannidis, D. Pantazi, G. Stamoulis, J. Dowling, S. Haridi, V. Vlassov, L. Bruzzone, C. Paris, T. Eltoft, T. Krämer, A. Charalambidis, V. Karkaletsis, S. Konstantopoulos, T. Kakantousis, M. Datcu, C. O. Dumitru, F. Appel, H. Bach, S. Migdall, N. Hughes, D. Arthurs, and A. Fleming. 2019. From Copernicus big data to extreme Earth analytics. In Advances in Database Technology—22nd International Conference on Extending Database Technology, EDBT 2019, Lisbon, Portugal, March 26–29, 2019. OpenProceedings.org, 690–693. DOI: .Google Scholar
- G. M. Kuper, L. Libkin, and J. Paredaens (Eds.). 2000. Constraint Databases. Springer. ISBN 3-540-66151-4. DOI: .Google Scholar
- K. Kyzirakos, M. Karpathiotakis, and M. Koubarakis. 2012. Strabon: A semantic geospatial DBMS. In Proceedings of the Semantic Web—ISWC 2012—11th International Semantic Web Conference, Part I, Boston, MA, November 11–15, 2012, Vol. 7649: Lecture Notes in Computer Science. Springer, 295–311. DOI: .Google Scholar
- K. Kyzirakos, M. Karpathiotakis, G. Garbis, C. Nikolaou, K. Bereta, I. Papoutsis, T. Herekakis, D. Michail, M. Koubarakis, and C. Kontoes. 2014. Wildfire monitoring using satellite images, ontologies and linked geospatial data. J. Web Semant. 24, 18–26. DOI: .Google Scholar
- K. Kyzirakos, D. Savva, I. Vlachopoulos, A. Vasileiou, N. Karalis, M. Koubarakis, and S. Manegold. October. 2018. GeoTriples: Transforming geospatial data into RDF graphs using R2RML and RML mappings. J. Web Semant. 52–53, 16–32. DOI: .Google Scholar
- J. Lehmann, S. Athanasiou, A. Both, A. Garcia-Rojas, G. Giannopoulos, D. Hladky, J. J. L. Grange, A. N. Ngomo, M. A. Sherif, C. Stadler, M. Wauer, P. Westphal, and V. Zaslawski. 2015a. Managing geospatial linked data in the GeoKnow project. In The Semantic Web in Earth and Space Science. Current Status and Future Directions. IOS Press, 51–78. DOI: .Google Scholar
- J. Lehmann, R. Isele, M. Jakob, A. Jentzsch, D. Kontokostas, P. N. Mendes, S. Hellmann, M. Morsey, P. van Kleef, S. Auer, and C. Bizer. 2015b. DBpedia—A large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web. 6, 2, 167–195. DOI: .Google Scholar
- D. B. Lenat, R. V. Guha, K. Pittman, D. Pratt, and M. Shepherd. 1990. CYC: Toward programs with common sense. Commun. ACM 33, 8, 30–49. DOI: .Google Scholar
- H. Li, E. Hamzei, I. Majic, H. Hua, J. Renz, M. Tomko, M. Vasardani, S. Winter, and T. Baldwin. 2021. Neural factoid geospatial question answering. J. Spat. Inf. Sci. 23, 65–90. DOI: .Google Scholar
- G. Ligozat. 1998. Reasoning about cardinal directions. J. Vis. Lang. Comput. 9, 1, 23–44. DOI: .Google Scholar
- W. Liu and S. Li. 2011. Reasoning about cardinal directions between extended objects: The NP-hardness result. Artif. Intell. 175, 18, 2155–2169. DOI: .Google Scholar
- W. Liu, S. Li, and J. Renz. 2009. Combining RCC-8 with qualitative direction calculi: Algorithms and complexity. In IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, CA, July 11–17, 2009. AAAI Press, 854–859. http://ijcai.org/Proceedings/09/Papers/146.pdf.Google Scholar
- W. Liu, X. Zhang, S. Li, and M. Ying. 2010. Reasoning about cardinal directions between extended objects. Artif. Intell. 174, 12–13, 951–983. DOI: .Google Scholar
- P. A. Longley, M. F. Goodchild, D. J. Maguire, and D. W. Rhind. 2015. Geographic Information Science and Systems (4th. ed.). John Wiley & Sons. ISBN 1118676955, 9781118676950.Google Scholar
- V. López, E. Motta, and V. S. Uren. 2006. PowerAqua: Fishing the Semantic Web. In Y. Sure and J. Domingue (Eds.), Proceedings of the Semantic Web: Research and Applications, 3rd European Semantic Web Conference, ESWC 2006, Budva, Montenegro, June 11–14, 2006, Vol. 4011: Lecture Notes in Computer Science. Springer, 393–410. DOI: .Google Scholar
- C. Lutz and M. Miličić. 2007. A tableau algorithm for description logics with concrete domains and general TBoxes. J. Autom. Reason. 38, 1–3, 227–259. ISSN 0168-7433. DOI: .Google Scholar
- F. Mahdisoltani, J. Biega, and F. M. Suchanek. 2015. YAGO3: A knowledge base from multilingual Wikipedias. In 7th Biennial Conference on Innovative Data Systems Research (CIDR 2015), Asilomar, CA, January 4–7, 2015.Google Scholar
- M. D. Mahecha, F. Gans, G. Brandt, R. Christiansen, S. E. Cornell, N. Fomferra, G. Kraemer, J. Peters, P. Bodesheim, G. Camps-Valls, J. F. Donges, W. Dorigo, L. M. Estupinan-Suarez, V. H. Gutierrez-Velez, M. Gutwin, M. Jung, M. C. Londoño, D. G. Miralles, P. Papastefanou, and M. Reichstein. 2020. Earth system data cubes unravel global multivariate dynamics. Earth Syst. Dyn. 11, 201–234. DOI: .Google Scholar
- G. Mai, K. Janowicz, R. Zhu, L. Cai, and N. Lao, 2021. Geographic question answering: Challenges, uniqueness, classification, and future directions. AGILE GIScience Ser. 2, 8. DOI: .Google Scholar
- D. Maling. 1992. Coordinate Systems and Map Projections (2nd. ed.). Pergamon Press. ISBN 978-0-08-037233-4. DOI: .Google Scholar
- G. Mandilaras and M. Koubarakis. 2021. Scalable transformation of big geospatial data into linked data. In 20th International Semantic Web Conference (ISWC 2021), Vol. 12922: Lecture Notes in Computer Science. Springer, 480–495. DOI: .Google Scholar
- G. Mandilaras, D.-A. Pantazi, M. Koubarakis, N. Hughes, A. Everett, and A. Kiærbech. 2020. Ice monitoring with ExtremeEarth. In Proceedings of LASCAR 2nd Workshop on Large Scale RDF Analytics 2020. Collocated with ESCW 2020. https://earthanalytics.eu/publications/ExtremeEarthIceDemo.pdf.Google Scholar
- G. M. Mandilaras, G. Papadakis, L. Gagliardelli, G. Simonini, E. Thanos, G. Giannakopoulos, S. Bergamaschi, T. Palpanas, M. Koubarakis, A. Lara-Clares, and A. Fariña. 2021. Reproducible experiments on three-dimensional entity resolution with JedAI. Inf. Syst. 102, 101830. DOI: .Google Scholar
- G. A. Miller. 1995. WordNet: A lexical database for English. Commun. ACM 38, 11, 39–41. DOI: .Google Scholar
- R. Moratz and J. O. Wallgrün. 2012. Spatial reasoning with augmented points: Extending cardinal directions with local distances. J. Spat. Inf. Sci. 5, 1, 1–30. DOI: .Google Scholar
- B. Motik, B. Cuenca Grau, I. Horrocks, Z. Wu, A. Fokoue, and C. Lutz. 2012. OWL 2 Web Ontology Language Profiles (2nd. ed.). W3C Recommendation December 11, 2012. https://www.w3.org/TR/owl2-profiles/.Google Scholar
- A. Mukerjee and G. Joe. 1990. A qualitative model for space. In Proceedings of the 8th National Conference on Artificial Intelligence, Vol. 2, Boston, MA, July 29–August 3, 1990. AAAI Press, 721–727.Google Scholar
- M. Nentwig, M. Hartung, A. N. Ngomo, and E. Rahm. 2017. A survey of current link discovery frameworks. Semant. Web 8, 3, 419–436. DOI: .Google Scholar
- T. Neumann and G. Weikum. 2010. The RDF-3X engine for scalable management of RDF data. VLDB J. 19, 1, 91–113. DOI: .Google Scholar
- A. N. Ngomo. 2013. ORCHID—Reduction-ratio-optimal computation of geo-spatial distances for link discovery. In International Semantic Web Conference ISWC, Vol. 8218: Lecture Notes in Computer Science. Springer, 395–410. DOI: .Google Scholar
- A. N. Ngomo and S. Auer. 2011. LIMES—A time-efficient approach for large-scale link discovery on the Web of data. In IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16–22, 2011. AAAI Press, 2312–2317. DOI: .Google Scholar
- P. Nikitopoulos, A. Vlachou, C. Doulkeridis, and G. A. Vouros. 2021. Parallel and scalable processing of spatio-temporal RDF queries using Spark. GeoInformatica 25(4), 623–653. DOI: .Google Scholar
- C. Nikolaou and M. Koubarakis. 2016. Querying incomplete information in RDF with SPARQL. Artif. Intell. 237, 138–171. DOI: .Google Scholar
- C. Nikolaou, K. Dogani, K. Bereta, G. Garbis, M. Karpathiotakis, K. Kyzirakos, and M. Koubarakis. 2015. Sextant: Visualizing time-evolving linked geospatial data. J. Web Semant. 35, 35–52. DOI: .Google Scholar
- J. Nivre, M. de Marneffe, F. Ginter, Y. Goldberg, J. Hajic, C. D. Manning, R. T. McDonald, S. Petrov, S. Pyysalo, N. Silveira, R. Tsarfaty, and D. Zeman. 2016. Universal dependencies v1: A multilingual treebank collection. In N. Calzolari, K. Choukri, T. Declerck, S. Goggi, M. Grobelnik, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, and S. Piperidis (Eds.), Proceedings of the Tenth International Conference on Language Resources and Evaluation LREC 2016, Portorož, Slovenia, May 23–28, 2016. European Language Resources Association (ELRA).Google Scholar
- N. F. Noy and D. L. McGuinness. March. 2001. Ontology Development 101: A Guide to Creating Your First Ontology. Technical Report. Stanford University, Knowledge Systems Laboratory. https://protege.stanford.edu/publications/ontology_development/ontology101.pdf.Google Scholar
- N. F. Noy, Y. Gao, A. Jain, A. Narayanan, A. Patterson, and J. Taylor. 2019. Industry-scale knowledge graphs: Lessons and challenges. Commun. ACM 62, 8, 36–43. DOI: .Google Scholar
- G. Papadakis. 2020. Entity Resolution Benchmark Dataset. https://data.mendeley.com/datasets/4whpm32y47.Google Scholar
- G. Papadakis and T. Palpanas. 2018. Web-scale, schema-agnostic, end-to-end entity resolution. In The Web Conference (WWW), Lyon, France. IW3C2.Google Scholar
- D. Papadias, Y. Theodoridis, T. K. Sellis, and M. J. Egenhofer. 1995. Topological relations in the world of minimum bounding rectangles: A study with R-trees. ACM SIGMOD Rec. 24, 2, 92–103. DOI: .Google Scholar
- G. Papadakis, J. Svirsky, A. Gal, and T. Palpanas. 2016. Comparative analysis of approximate blocking techniques for entity resolution. Proc. VLDB Endow. 9, 9, 684–695. DOI: .Google Scholar
- G. Papadakis, L. Tsekouras, E. Thanos, G. Giannakopoulos, T. Palpanas, and M. Koubarakis. 2017. JedAI: The force behind entity resolution. In The Semantic Web: ESWC 2017 Satellite Events, ESWC 2017, Vol. 10577: Lecture Notes in Computer Science. Springer, 161–166. DOI: .Google Scholar
- G. Papadakis, L. Tsekouras, E. Thanos, G. Giannakopoulos, T. Palpanas, and M. Koubarakis. 2018. The return of JedAI: End-to-end entity resolution for structured and semi-structured data. Proc. VLDB Endow. 11, 12, 1950–1953. DOI: .Google Scholar
- G. Papadakis, L. Tsekouras, E. Thanos, G. Giannakopoulos, T. Palpanas, and M. Koubarakis. 2019. Domain- and structure-agnostic end-to-end entity resolution with JedAI. ACM SIGMOD Rec. 48, 4, 30–36. DOI: .Google Scholar
- G. Papadakis, E. Ioannou, and T. Palpanas. 2020a. Entity resolution: Past, present and yet-to-come. In Proceedings of the 23rd International Conference on Extending Database Technology (EDBT). OpenProceedings.org, 647–650. DOI: http://dx.doi.org/10.5441/002/edbt.2020.85.Google Scholar
- G. Papadakis, G. Mandilaras, L. Gagliardelli, G. Simonini, E. Thanos, G. Giannakopoulos, S. Bergamaschi, T. Palpanas, and M. Koubarakis. 2020b. Three-dimensional entity resolution with JedAI. Inf. Syst. 93, 101565. DOI: .Google Scholar
- G. Papadakis, D. Skoutas, E. Thanos, and T. Palpanas. 2020c. Blocking and filtering techniques for entity resolution: A survey. ACM Comput. Surv. 53, 2, 1–42. DOI: .Google Scholar
- G. Papadakis, L. Tsekouras, E. Thanos, N. Pittaras, G. Simonini, D. Skoutas, P. Isaris, G. Giannakopoulos, T. Palpanas, and M. Koubarakis. 2020d. JedA3: Beyond batch, blocking-based entity resolution. In Proceedings of the 23rd International Conference on Extending Database Technology (EDBT). OpenProceedings.org. DOI: .Google Scholar
- G. Papadakis, E. Ioannou, E. Thanos, and T. Palpanas. 2021a. The Four Generations of Entity Resolution. Synthesis Lectures on Data Management. Morgan & Claypool.Google Scholar
- G. Papadakis, G. M. Mandilaras, N. Mamoulis, and M. Koubarakis. 2021b. Progressive, holistic geospatial interlinking. In Proceedings of the Web Conference 2021. ACM, 833–844. DOI: .Google Scholar
- G. Papadakis, G. Mandilaras, N. Mamoulis, and M. Koubarakis. 2022. Static and dynamic progressive geospatial interlinking. ACM Trans. Spat. Algorithms Syst. 8, 2, 1–41. DOI: .Google Scholar
- M. Papamichalopoulos, G. Papadakis, G. Mandilaras, M. D. Siampou, N. Mamoulis, and M. Koubarakis. 2022. Three-dimensional geospatial interlinking with JedAI-spatial. CoRR, abs/2205.01905. DOI: .Google Scholar
- C. Paris, G. Weikmann, and L. Bruzzone. 2020. Monitoring of agricultural areas by using Sentinel 2 image time series and deep learning techniques. In SPIE Remote Sensing Conference, Edinburgh, UK, September 21–24, 2020. Society of Photo-Optical Instrumentation Engineers. DOI: .Google Scholar
- K. Patroumpas, M. Alexakis, G. Giannopoulos, and S. Athanasiou. 2014. TripleGeo: An ETL tool for transforming geospatial data into RDF triples. In K. S. Candan, S. Amer-Yahia, N. Schweikardt, V. Christophides, and V. Leroy (Eds.), Proceedings of the Workshops of the EDBT/ICDT 2014 Joint Conference (EDBT/ICDT 2014), Athens, Greece, March 28, 2014, Vol. 1133. CEUR Workshop Proceedings. CEUR-WS.org, 275–278. http://ceur-ws.org/Vol-1133/paper-44.pdf.Google Scholar
- K. Patroumpas, D. Skoutas, G. M. Mandilaras, G. Giannopoulos, and S. Athanasiou. 2019. Exposing points of interest as linked geospatial data. In W. G. Aref, M. Bertolotto, P. Bouros, C. S. Jensen, A. Mahmood, K. Nørvåg, D. Sacharidis, and M. Sarwat (Eds.), Proceedings of the 16th International Symposium on Spatial and Temporal Databases, SSTD 2019, Vienna, Austria, August 19–21, 2019. ACM, 21–30. DOI: .Google Scholar
- S. Peroni. 2016. A simplified agile methodology for ontology development. In OWL: Experiences and Directions—Reasoner Evaluation. 13th International Workshop, OWLED 2016, and 5th International Workshop, ORE 2016, Bologna, Italy, November 20, 2016, Revised Selected Papers, Vol. 10161: Lecture Notes in Computer Science. Springer, 55–69. DOI: .Google Scholar
- M. S. Perry. 2008. A Framework to Support Spatial, Temporal and Thematic Analytics over Semantic Web Data. Ph.D. Dissertation. Department of Computer Science and Engineering, Wright State University. https://corescholar.libraries.wright.edu/etd_all/247/.Google Scholar
- M. Perry and J. Herring. 2012. GeoSPARQL—A Geographic Query Language for RDF Data. Open Geospatial Consortium, OGC 11-052r4. http://www.opengis.net/doc/IS/geosparql/1.0.Google Scholar
- M. Perry, J. Herring, N. J. Car, T. Homburg, and S. J. D. Cox. 2021. OGC GeoSPARQL—A Geographic Query Language for RDF Data: GeoSPARQL 1.1 Draft. Technical Report. https://opengeospatial.github.io/ogc-geosparql/.Google Scholar
- C. Portele. 2007. Geography Markup Language. Open Geospatial Consortium Standard. https://www.ogc.org/standards/gml.Google Scholar
- D. Punjani, K. Singh, A. Both, M. Koubarakis, I. Angelidis, K. Bereta, T. Beris, D. Bilidas, T. Ioannidis, N. Karalis, C. Lange, D. Pantazi, C. Papaloukas, and G. Stamoulis. 2018. Template-based question answering over linked geospatial data. In R. S. Purves and C. B. Jones (Eds.), Proceedings of the 12th Workshop on Geographic Information Retrieval, GIR@SIGSPATIAL 2018, Seattle, WA, November 6, 2018. ACM, 1–10. DOI: .Google Scholar
- D. Punjani, M. Iliakis, T. Stefou, K. Singh, A. Both, M. Koubarakis, I. Angelidis, K. Bereta, T. Beris, D. Bilidas, T. Ioannidis, N. Karalis, C. Lange, D. Pantazi, C. Papaloukas, and G. Stamoulis. 2020. Template-based question answering over linked geospatial data. CoRR, abs/2007.07060. https://arxiv.org/abs/2007.07060.Google Scholar
- P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang. 2016. SQuAD: 100, 000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, TX, November 1–4, 2016. Association for Computational Linguistics, 2383–2392. DOI: .Google Scholar
- D. A. Randell, Z. Cui, and A. G. Cohn. 1992. A spatial logic based on regions and connection. In Proceedings of the 3rd International Conference on Principles of Knowledge Representation and Reasoning (KR ’92), Cambridge, MA, October 25–29, 1992. Morgan Kaufmann, 165–176.Google Scholar
- J. Renz. 1998. A canonical model of the region connection calculus. In Proceedings of the Sixth International Conference on Principles of Knowledge Representation and Reasoning (KR ’98), Trento, Italy, June 2–5, 1998. Morgan Kaufmann, 330–341.Google Scholar
- J. Renz and B. Nebel. 1999. On the complexity of qualitative spatial reasoning: A maximal tractable fragment of the region connection calculus. Artif. Intell. 108, 1–2, 69–123. DOI: .Google Scholar
- J. Renz and B. Nebel. 2001. Efficient methods for qualitative spatial reasoning. J. Artif. Intell. Res. 15, 289–318. DOI: .Google Scholar
- J. Renz and B. Nebel. 2007. Qualitative spatial reasoning using constraint calculi. In Handbook of Spatial Logics. Springer, 161–215. DOI: .Google Scholar
- P. Z. Revesz. 2010. Introduction to Databases—From Biological to Spatio-Temporal. Texts in Computer Science. Springer. ISBN 978-1-84996-094-6. DOI: .Google Scholar
- M. Riechert and J. Blower. 2015. The CoverageJSON Format Specification. https://covjson.org/spec/.Google Scholar
- P. Rigaux, M. Scholl, and A. Voisard. 2002. Spatial Databases—With Applications to GIS. Elsevier. ISBN 978-1-55860-588-6. DOI: .Google Scholar
- F. Rossi, P. van Beek, and T. Walsh (Eds.). 2006. Handbook of Constraint Programming. Foundations of Artificial Intelligence, Vol. 2. Elsevier. ISBN 978-0-444-52726-4. DOI: .Google Scholar
- R. S. Roy and A. Anand. 2021. Question Answering for the Curated Web: Tasks and Methods in QA Over Knowledge Bases and Text Collections. Synthesis Lectures on Information Concepts, Retrieval, and Services. Morgan & Claypool. DOI: .Google Scholar
- G. M. Santipantakis, C. Doulkeridis, A. Vlachou, and G. A. Vouros. 2020. Integrating data by discovering topological and proximity relations among spatiotemporal entities. In G. A. Vouros, G. L. Andrienko, C. Doulkeridis, N. Pelekis, A. Artikis, A. Jousselme, C. Ray, J. M. C. Garcia, and D. Scarlatti (Eds.), Big Data Analytics for Time-Critical Mobility Forecasting, From Raw Data to Trajectory-Oriented Mobility Analytics in the Aviation and Maritime Domains. Springer, 155–179. DOI: .Google Scholar
- T. Saveta, I. Fundulaki, G. Flouris, and A. N. Ngomo. 2018. SPgen: A benchmark generator for spatial link discovery tools. In Proceedings of the Semantic Web—ISWC 2018—17th International Semantic Web Conference, Part I, Monterey, CA, October 8–12, 2018, Vol. 11136: Lecture Notes in Computer Science. Springer, 408–423. DOI: .Google Scholar
- A. Schrijver. 1998. Theory of Linear and Integer Programming. Wiley-Interscience Series on Discrete Mathematics and Optimization. John Wiley & Sons.Google Scholar
- J. Sequeda and O. Lassila. 2021. Designing and Building Enterprise Knowledge Graphs. Synthesis Lectures on Data, Semantics, and Knowledge, Vol. 11, No. 1. Morgan & Claypool.Google Scholar
- M. A. Sherif, K. Dreßler, P. Smeros, and A. N. Ngomo. 2017. Radon—Rapid discovery of topological relations. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI ’17). AAAI Press, 175–181. DOI: .Google Scholar
- K. Shvachko, H. Kuang, S. Radia, and R. Chansler. 2010. The Hadoop distributed file system. In M. G. Khatib, X. He, and M. Factor (Eds.), IEEE 26th Symposium on Mass Storage Systems and Technologies, MSST 2012, Lake Tahoe, NV, May 3–7, 2010. IEEE, 1–10. DOI: .Google Scholar
- M. D. Siampou, N. Karalis, and M. Koubarakis. May. 2022. Extending YAGO4 knowledge graph with geospatial knowledge. In Proceedings of the 5th International Workshop on Geospatial Linked Data at ESWC 2022. CEUR Workshop Proceedings. CEUR-WS.org.Google Scholar
- G. Simonini, G. Papadakis, T. Palpanas, and S. Bergamaschi. 2019. Schema-agnostic progressive entity resolution. IEEE Trans. Knowl. Data Eng. 31, 6, 1208–1221. DOI: .Google Scholar
- K. Singh, A. Both, D. Diefenbach, S. Shekarpour, D. Cherix, and C. Lange. 2016. Qanary—The fast track to creating a question answering system with linked data technology. In H. Sack, G. Rizzo, N. Steinmetz, D. Mladenic, S. Auer, and C. Lange (Eds.), The Semantic Web—ESWC 2016 Satellite Events, Heraklion, Crete, Greece, May 29–June 2, 2016, Revised Selected Papers, Vol. 9989: Lecture Notes in Computer Science. Springer, 183–188. DOI: .Google Scholar
- K. Singh, A. S. Radhakrishna, A. Both, S. Shekarpour, I. Lytra, R. Usbeck, A. Vyas, A. Khikmatullaev, D. Punjani, C. Lange, M. Vidal, J. Lehmann, and S. Auer. 2018. Why reinvent the wheel: Let’s build question answering systems together. In P. Champin, F. L. Gandon, M. Lalmas, and P. G. Ipeirotis (Eds.), Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018, Lyon, France, April 23–27, 2018. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 1247–1256. DOI: .Google Scholar
- S. Skiadopoulos and M. Koubarakis. 2001. Composing cardinal direction relations. In Advances in Spatial and Temporal Databases, 7th International Symposium, SSTD 2001, Redondo Beach, CA, July 12–15, 2001, Vol. 2121: Lecture Notes in Computer Science. Springer, Berlin, 299–320. DOI: .Google Scholar
- S. Skiadopoulos and M. Koubarakis. 2004. Composing cardinal direction relations. Artif. Intell. 152, 2, 143–171. DOI: .Google Scholar
- S. Skiadopoulos, C. Giannoukos, P. Vassiliadis, T. K. Sellis, and M. Koubarakis. 2004. Computing and handling cardinal direction information. In Advances in Database Technology—EDBT 2004, 9th International Conference on Extending Database Technology, Heraklion, Crete, Greece, March 14–18, 2004, Vol. 2992: Lecture Notes in Computer Science. Springer, 329–347. DOI: .Google Scholar
- S. Skiadopoulos, N. Sarkas, T. K. Sellis, and M. Koubarakis. 2007. A family of directional relation models for extended objects. IEEE Trans. Knowl. Data Eng. 19, 8, 1116–1130. DOI: .Google Scholar
- P. Smeros and M. Koubarakis. 2016. Discovering spatial and temporal links among RDF data. In Proceedings of the Workshop on Linked Data on the Web, LDOW 2016, Co-located with 25th International World Wide Web Conference (WWW 2016).Google Scholar
- C. Stadler, J. Lehmann, K. Höffner, and S. Auer. 2012. LinkedGeoData: A core for a web of spatial open data. Semant. Web 3, 4, 333–354. DOI: .Google Scholar
- K. Stefanidis, V. Efthymiou, M. Herschel, and V. Christophides. 2014. Entity resolution in the web of data. In Proceedings of the 23rd International Conference on World Wide Web (WWW ’14 Companion). ACM, 203–204. DOI: .Google Scholar
- M. Stocker, A. Seaborne, A. Bernstein, C. Kiefer, and D. Reynolds. 2008. SPARQL basic graph pattern optimization using selectivity estimation. In J. Huai, R. Chen, H. Hon, Y. Liu, W. Ma, A. Tomkins, and X. Zhang (Eds.), Proceedings of the 17th International Conference on World Wide Web, WWW 2008, Beijing, China, April 21–25, 2008. ACM, 595–604. DOI: .Google Scholar
- K. Stravoskoufos, E. G. M. Petrakis, N. Mainas, S. Batsakis, and V. Samoladas. 2016. SOWL QL: Querying spatio-temporal ontologies in OWL. J. Data Semant. 5, 4, 249–269. DOI: .Google Scholar
- F. M. Suchanek, G. Kasneci, and G. Weikum. 2007. YAGO: A core of semantic knowledge. In Proceedings of the 16th International Conference on World Wide Web, WWW 2007, Banff, Alberta, Canada, May 8–12, 2007. ACM, 697–706. DOI: .Google Scholar
- F. M. Suchanek, G. Kasneci, and G. Weikum. 2008. YAGO: A large ontology from Wikipedia and WordNet. J. Web Semant. 6, 3, 203–217. DOI: .Google Scholar
- J. Tandy, L. van den Brink, and P. Barnaghi. September. 2017. Spatial Data on the Web Best Practices. https://www.w3.org/TR/sdw-bp/.Google Scholar
- L. R. Tang and R. J. Mooney. 2001. Using multiple clause constructors in inductive logic programming for semantic parsing. In Machine Learning: EMCL 2001, 12th European Conference on Machine Learning, Freiburg, Germany, September 5–7, 2001, Vol. 2167: Lecture Notes in Computer Science. Springer, 466–477. DOI: .Google Scholar
- T. P. Tanon, G. Weikum, and F. M. Suchanek. 2020. YAGO 4: A reason-able knowledge base. In The Semantic Web—17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31–June 4, 2020, Vol. 12123: Lecture Notes in Computer Science. Springer, 583–596. DOI: .Google Scholar
- C. Unger, L. Bühmann, J. Lehmann, A. N. Ngomo, D. Gerber, and P. Cimiano. 2012. Template-based question answering over RDF data. In A. Mille, F. L. Gandon, J. Misselis, M. Rabinovich, and S. Staab (Eds.), Proceedings of the 21st World Wide Web Conference 2012, WWW 2012, Lyon, France, April 16–20, 2012. ACM, 639–648. DOI: .Google Scholar
- M. J. van Kreveld and I. Reinbacher. 2004. Good news: Partitioning a simple polygon by compass directions. Int. J. Comput. Geom. Appl. 14, 4–5, 233–259. DOI: .Google Scholar
- J. Volz, C. Bizer, M. Gaedke, and G. Kobilarov. 2009. Silk—A link discovery framework for the Web of Data. In Workshop on Linked Data on the Web, Vol. 538.Google Scholar
- D. Vrandecic and M. Krötzsch. 2014. Wikidata: A free collaborative knowledgebase. Commun. ACM 57, 10, 78–85. DOI: .Google Scholar
- W3C OWL Working Group. 2012. OWL 2 Web Ontology Language Document Overview (2nd. ed.). W3C Recommendation. http://www.w3.org/TR/2012/REC-owl2-overview-20121211/.Google Scholar
- G. Weikmann, C. Paris, and L. Bruzzone. 2021. TimeSen2Crop: A million labeled samples dataset of Sentinel 2 image time series for crop-type classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 4699–4708. DOI: .Google Scholar
- G. Weikum, X. L. Dong, S. Razniewski, and F. M. Suchanek. 2021. Machine knowledge: Creation and curation of comprehensive knowledge bases. Found. Trends Databases 10, 2–4, 108–490. DOI: http://doi.org/10.1561/1900000064.Google Scholar
- M. Wessel and R. Möller. 2009. Flexible software architectures for ontology-based information systems. J. Appl. Log. 7, 1, 75–99. Special Issue: Empirically Successful Computerized Reasoning. ISSN 1570-8683. http://www.sciencedirect.com/science/article/pii/S157086830700064X. DOI: .Google Scholar
- S. E. Whang, D. Marmaros, and H. Garcia-Molina. 2013. Pay-as-you-go entity resolution. IEEE Trans. Knowl. Data Eng. 25, 5, 1111–1124. DOI: .Google Scholar
- G. Xiao, D. Calvanese, R. Kontchakov, D. Lembo, A. Poggi, R. Rosati, and M. Zakharyaschev. 2018. Ontology-based data access: A survey. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. AAAI Press, 5511–5519. DOI: .Google Scholar
- M. Yasunaga, H. Ren, A. Bosselut, P. Liang, and J. Leskovec. 2021. QA-GNN: Reasoning with language models and knowledge graphs for question answering. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6–11, 2021. Association for Computational Linguistics, 535–546. DOI: .Google Scholar
- E. M. G. Younis, C. B. Jones, V. Tanasescu, and A. I. Abdelmoty. 2012. Hybrid geo-spatial query methods on the Semantic Web with a spatially-enhanced index of DBpedia. In N. Xiao, M. Kwan, M. F. Goodchild, and S. Shekhar (Eds.), Geographic Information Science—7th International Conference, GIScience 2012, Columbus, OH, September 18–21, 2012, Vol. 7478: Lecture Notes in Computer Science, Springer, 340–353. DOI: .Google Scholar
- M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauly, M. J. Franklin, S. Shenker, and I. Stoica. 2012. Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In Proceedings of the 9th USENIX Symposium on Networked Systems Design and Implementation, NSDI. USENIX Association, 15–28.Google Scholar
- M. Zaharia, R. S. Xin, P. Wendell, T. Das, M. Armbrust, A. Dave, X. Meng, J. Rosen, S. Venkataraman, M. J. Franklin, A. Ghodsi, J. Gonzalez, S. Shenker, and I. Stoica. 2016. Apache Spark: A unified engine for big data processing. Commun. ACM 59, 11, 56–65. DOI: .Google Scholar
- J. M. Zelle and R. J. Mooney. 1996. Learning to parse database queries using inductive logic programming. In Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference, AAAI 96, IAAI 96, Portland, OR, August 4–8, 1996, Vol. 2. AAAI Press, 1050–1055.Google Scholar
- R. Zhu, S. S. Ambrose, L. Zhou, C. Shimizu, L. Cai, G. Mai, K. Janowicz, P. Hitzler, and M. Schildhauer. 2021. Environmental observations in knowledge graphs. In DaMaLOS 2021 @ ISWC.Google Scholar
- K. Zimmermann and C. Freksa. 1996. Qualitative spatial reasoning using orientation, distance, and path knowledge. Appl. Intell. 6, 1, 49–58. DOI: .Google Scholar
Cited By
-
Mitsios M, Punjani D, Abdollahi S, Gottschalk S, Tsalapati E, Demidova E and Koubarakis M (2025). Generating a Question Answering Dataset About Geographic Changes in a Knowledge Graph Knowledge Engineering and Knowledge Management, 10.1007/978-3-031-77792-9_28, (471-489),
-
Abdelmoty A, Muhajab H and Satoti A (2024). Spatial Semantics for the Evaluation of Administrative Geospatial Ontologies, ISPRS International Journal of Geo-Information, 10.3390/ijgi13080291, 13:8, (291)
- Kefalidis S, Punjani D, Tsalapati E, Plas K, Pollali M, Mitsios M, Tsokanaridou M, Koubarakis M and Maret P Benchmarking Geospatial Question Answering Engines Using the Dataset GeoQuestions1089 The Semantic Web – ISWC 2023, (266-284)
Index Terms
- Geospatial Data Science: A Hands-on Approach for Building Geospatial Applications Using Linked Data Technologies
Index terms have been assigned to the content through auto-classification.
Please enable JavaScript to view thecomments powered by Disqus.
Recommendations
Geospatial Technology Curriculum Development
The final project for students enrolled in Geospatial Technologies for the K-12 Classroom GEOG 321 is the development of a lesson for their future classroom. An obstacle to implementing geospatial technologies in public classrooms is lack of relevant ...
Science Bound: A success story for STEM Education
FIE '12: Proceedings of the 2012 IEEE Frontiers in Education Conference (FIE)At the 2005 Frontiers in Education Conference, members of Purdue University and Indiana University Purdue University Indianapolis (IUPUI) presented the results of the first year of the Purdue Science Bound program. Science Bound was created to give ...