{"title":"Transforming Geospatial Data into RDF","authors":"George M. Mandilaras","doi":"10.1145/3581906.3581916","DOIUrl":"https://doi.org/10.1145/3581906.3581916","url":null,"abstract":"produced according to the geospatial ontologies of Chapter 5. However, there is a lot of legacy geospatial data that it not available in RDF; they might be stored in spatially enabled relational databases (e.g., PostGIS) or files (e.g., shapefiles). This chapter discusses how to transform geospatial data sources from their original for mats (e.g., shapefiles) into RDF. The tool GeoTriples developed by the authors1 is used in the examples. Transforming Geospatial Data into RDF","PeriodicalId":433742,"journal":{"name":"Geospatial Data Science","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115871156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Geospatial Ontologies","authors":"C. Nikolaou","doi":"10.1145/3581906.3581912","DOIUrl":"https://doi.org/10.1145/3581906.3581912","url":null,"abstract":"introduces two high-level geospatial ontologies: the ontology of the query language GeoSPARQL and the geospatial part of Schema.org. We also briefly present some geospatial vocabularies that have predated these two more recent proposals","PeriodicalId":433742,"journal":{"name":"Geospatial Data Science","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132406876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Geospatial Data Modeling","authors":"Manolis Koubarakis","doi":"10.1145/3581906.3581909","DOIUrl":"https://doi.org/10.1145/3581906.3581909","url":null,"abstract":"mathematical","PeriodicalId":433742,"journal":{"name":"Geospatial Data Science","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126858804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Question Answering Engines for Geospatial Knowledge Graphs","authors":"D. Punjani, Eleni Tsalapati","doi":"10.1145/3581906.3581922","DOIUrl":"https://doi.org/10.1145/3581906.3581922","url":null,"abstract":"Question answering (QA) has been a hot topic of research in Natural Language Processing [Rajpurkar et al. 2016] and Knowledge Graphs for a number of years [Höffner et al. 2017, Diefenbach et al. 2018, Roy and Anand 2021]. There has been a lot of work on the construction of training datasets, the design of effective QA algo rithms, and the benchmarking of implemented systems. Very recently, work has concentrated on QA systems integrating ideas from Language Models, Knowledge Graphs, and Neural Networks [Yasunaga et al. 2021]. Question answering techniques have also been adopted by industry. They are used in all well-known search engines (e.g., Google and Bing), digital assistants (e.g., Siri, Alexa, and Google Assistant), and chatbots such as BlenderBot 2.0 of Facebook.1 The need for geospatial question answering engines arises in many practical situ ations. For example, a tourist visiting Athens would like to know which museums have parks nearby so that she can rest in the shade after visiting the museum. Or when driving toward Ancient Olympia, a tourist might want to know which Greek restaurant along the way is more popular with locals. Finally, a question answering engine might drive the implementation of a geography tutoring system by being able to answer questions such as “What is the tallest mountain in Greece?” or “What countries border Greece and are these countries members of the European Union?” Question Answering Engines for Geospatial Knowledge Graphs","PeriodicalId":433742,"journal":{"name":"Geospatial Data Science","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126572843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Putting It All Together: A Data Science Pipeline for Linked Earth Observation Data","authors":"Manolis Koubarakis","doi":"10.1145/3581906.3581923","DOIUrl":"https://doi.org/10.1145/3581906.3581923","url":null,"abstract":"The previous chapters argued that Semantic Web and Linked Data technologies are the right tools for developing geospatial applications. Using a hands-on approach, we presented the state-of-the-art models and languages for linked geospatial data together with innovative tools that implement them. Since 2010, we have used these tools to implement Earth Observation (EO) applications following the data science pipeline that is shown in Figure 16.1 [Koubarakis et al. 2016]. The following sections explain how. Putting It All Together: A Data Science Pipeline for Linked Earth Observation Data","PeriodicalId":433742,"journal":{"name":"Geospatial Data Science","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126730976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Geospatial Ontology-based Data Access","authors":"K. Bereta","doi":"10.1145/3581906.3581918","DOIUrl":"https://doi.org/10.1145/3581906.3581918","url":null,"abstract":"11.1 Ontology-based Data Access The OBDA paradigm [Xiao et al. 2018] proposes the creation of virtual RDF graphs on top of relational databases using ontologies and mappings. Given a database schema S, an ontology O, and a set of mappings M, an OBDA specification is a triple P = (O, M, S). Then, an OBDA instance (P, D) is defined given the OBDA specifica tion P and the database D that follows the database schema S. Mappings encode how relational data get mapped into RDF terms. A virtual RDF graph VGM,D of the database instance D is produced if we apply the mappings M to D. Then, if [[Q]](P,D) is the result of the evaluation of the SPARQL query Q over the OBDA instance (P, D), this is equivalent to [[Q]](P,VG(M,D)). The W3C standard language for encoding mappings of relational data is R2RML [Das et al. 2012], which was briefly introduced in Chapter 9. As an example, we can map a relational table named Student with columns id and name into RDF with the following R2RML mapping:","PeriodicalId":433742,"journal":{"name":"Geospatial Data Science","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123707057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Linked Geospatial Data","authors":"D. Pantazi","doi":"10.1145/3581906.3581913","DOIUrl":"https://doi.org/10.1145/3581906.3581913","url":null,"abstract":"6.1 The Global Administrative Areas Dataset The Global Administrative Areas (GADM) dataset contains information about the administrative divisions of all countries of the world together with the geographic boundaries of these divisions. This dataset is publicly available as shapefiles or geodatabase files.1 In Figure 6.1, we show the ontology of the GADM dataset. The GADM ontology is an INSPIRE-compliant ontology because it conforms to the INSPIRE require ments and recommendations.2 The main class is gadmo:AdministrativeUnit. The object properties of an instance of the class gadmo:AdministrativeUnit are geo:hasGeometryandgadmo:hasUpperLevelUnit.The property geo:hasGeometry indicates where an area is located while gadmo:hasUpperLevelUnit indicates which is the upper administrative unit of the current instance of the class. The data properties of the class gadmo:AdministrativeUnit are gadmo:hasName, gadmo:country, gadmo:hasNationalLevel, gadmo:hasBeginLifespanVersion,","PeriodicalId":433742,"journal":{"name":"Geospatial Data Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116446935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Geospatial Knowledge Graphs","authors":"Nikolaos Karalis, Eleni Tsalapati","doi":"10.1145/3581906.3581921","DOIUrl":"https://doi.org/10.1145/3581906.3581921","url":null,"abstract":"and Reasoning, Natural Language Processing, Machine Learning, and others. In addition to this historical survey, interesting recent surveys on knowledge graphs are Hogan et al. [2020] and Weikum et al. [2021]. The idea of large ontologies and KGs goes back to the seminal projects CYC [Lenat et al. 1990] and WordNet [Miller 1995]. The first modern KGs appeared around 2007 with the development of DBpedia [Auer et al. 2007], YAGO [Suchanek et al. 2007] and Freebase [Bollacker et al. 2008]. Google bought Freebase in 2010 and used it to build the Google KG, which today powers its search engine. As a result, when we ask Google “What is the height of Mount Olympus” today, we get the pre cise answer 2,917 meters, instead of links to Web pages where the answer could be found as we would get in the past. But KGs do not just power today’s search engines; they play an important role in many other large industries [Noy et al. 2019, Sequeda and Lassila 2021]. In this chapter, we are interested in knowledge graphs that contain rich geospa tial knowledge that can be used to answer geospatial questions such as “Which river crosses the city of Larissa?” or “What countries border Greece to the north?” or “What is the distance between Athens and Salonika?” The next chapter dis cusses the problem of answering geospatial questions such as these over geospatial knowledge graphs. Geospatial Knowledge Graphs","PeriodicalId":433742,"journal":{"name":"Geospatial Data Science","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133463205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Geospatial RDF Stores","authors":"T. Ioannidis","doi":"10.1145/3581906.3581920","DOIUrl":"https://doi.org/10.1145/3581906.3581920","url":null,"abstract":"In this section, we present all the geospatial RDF stores that have been devel oped over time, paying more attention to recent systems. In particular, we follow a modular presentation of the systems, taking into consideration factors such as, whether it is a proof-of-concept system, which geospatial vocabularies are sup ported, and the level of support for the most prominent vocabulary, the Open","PeriodicalId":433742,"journal":{"name":"Geospatial Data Science","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114665470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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