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GeoSPARQL is an important standard for the geospatial linked data community, given that it defines a vocabulary for representing geospatial data in RDF, defines an extension to SPARQL for processing geospatial data, and provides support for both qualitative and quantitative spatial reasoning. However, what the community is missing is a comprehensiv...
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... results of the experiments with our benchmark and the systems listed in Table 1 are shown in Table 2 and in Figure 3, and are available online on the HOBBIT platform (Results on the HOBBIT platform: https://master.project-hobbit.eu/experiments /1612476122572, 1612477003063,1612476116049,1625421291667,1612477500164,1612661614510,161263753167 3,1612828110551,1612477849872 (accessed on 08.07.2021)). ...Context 2
... bottom three systems are explicitly not GeoSPARQL-compliant, but we included them in our experiments as baseline tests. As we can see, they all demonstrated compatibility with either two or with three extensions of the GeoSPARQL standard (Table 3), and scored 56.67% or 46.67% of the GeoSPARQL compliance score (Table 2). This, however, does not mean that the benchmark score should start at 56.67% or 46.67%, since a benchmarked RDF storage system may fail these tests too. ...Context 3
... bottom three systems are explicitly not GeoSPARQL-compliant, but we included them in our experiments as baseline tests. As we can see, they all demonstrated compatibility with either two or with three extensions of the GeoSPARQL standard (Table 3), and scored 56.67% or 46.67% of the GeoSPARQL compliance score (Table 2). This, however, does not mean that the benchmark score should start at 56.67% or 46.67%, since a benchmarked RDF storage system may fail these tests too. ...Citations
... In addition, this includes domain-specific research collections and Wikidata projects as part of the Collection Research Network [29] (p. 2), such as Roman Open Data (https://romanopendata.eu, accessed on 09 March 2022), Linked Open Samian Ware [30][31][32][33], Linked Ogham Data [34][35][36][37][38][39][40] (pp. [119][120][121][122][123][124][125][126][127], the Wiki-Project Prähistorische Keramik [41,42], and Linked Aegean Seals [43] within the Corpus of the Minoan and Mycenaean Seals (CMS) project (http://cmsheidelberg.uni-hd.de, accessed on 9 March 2023) using the iDAI.world ...
... In the field of archaeology, research on Digital Twins is on the rise, such as in graph-based data management for cultural heritage conservation with Digital Twins [112], Digital Twins and 3D documentation using multi-lens photogrammetric approaches [113], and SPARQLing Ogham Digital Twins as Linked Open Data [40] (pp. [119][120][121][122][123][124][125][126][127], [34] on the basis of the Ogham in 3D project, combining 3D capturing and EPIDOC XML modelling [114,115]. The following subsections describe geometric capturing (Section 2.4.1) and semantic archaeological modelling (Section 2.4.2) in the ARS3D project. ...
... For open graph-based provision under the FAIR principles, three community standards in particular are used in the field of cultural heritage: the CIDOC Conceptual Reference Model (CIDOC CRM) ontology, the Resource Description Framework (RDF) of the World Wide Web Consortium (W3C), and the Linked Open Data (LOD) Principles [119]. The Open Geospatial Consortium (OGC) standard GeoSPARQL has established itself in the field of geodata, enabling the semantic description of geodata using simple features and providing functions for querying, such as in PostGIS [120]. ...
In this paper, we introduce applications of Artificial Intelligence techniques, such as Decision Trees and Semantic Reasoning, for semi-automatic and semantic-model-based decision-making for archaeological feature comparisons. This paper uses the example of Roman African Red Slip Ware (ARS) and the collection of ARS at the LEIZA archaeological research institute. The main challenge is to create a Digital Twin of the ARS objects and artefacts using geometric capturing and semantic modelling of archaeological information. Moreover, the individualisation and comparison of features (appliqués), along with their visualisation, extraction, and rectification, results in a strategy and application for comparison of these features using both geometrical and archaeological aspects with a comprehensible rule set. This method of a semi-automatic semantic model-based comparison workflow for archaeological features on Roman ceramics is showcased, discussed, and concluded in three use cases: woman and boy, human–horse hybrid, and bears with local twists and shifts.
... A recent piece by Ioannidis, Garbis, Kyzirakos, Bereta, and Koubarakis (2021) provided a benchmark on geospatial RDF stores from a computational perspective; however, the RDF/triple stores they selected, such as Parliament and Strabon (Kyzirakos, Karpathiotakis, & Koubarakis, 2012), may not be the most active platforms, and the evaluation contains only a single type of semantic data repositories: the triple stores. Jovanovik, Homburg, and Spasić (2021) conducted a comprehensive review on the compatibility of various triple stores with GeoSPARQL (SPARQL with Geospatial functions; SPARQL: Simple Protocol and Rdf Query Language); however, computational performance is not evaluated. ...
... While recent literature has evaluated the availability and compatibility of semantic data repositories to support spatial queries, almost all focus on RDF triple stores (Ioannidis et al., 2021;Jovanovik et al., 2021;Raza, 2019) and very few have addressed this question from a computational performance perspective. In this paper, we provide a comprehensive analysis of a variety of semantic repository solutions, including RDF triple stores, property graph databases, and OBDA platforms in terms of their capabilities, community activeness, and computational efficiency for handling spatial-semantic queries. ...
Knowledge graph has become a cutting-edge technology for linking and integrating heterogeneous, cross-domain datasets to address critical scientific questions. As big data has become prevalent in today's scientific analysis, semantic data repositories that can store and manage large knowledge graph data have become critical in successfully deploying spatially explicit knowledge graph applications. This paper provides a comprehensive evaluation of the popular semantic data repositories and their computational performance in managing and providing semantic support for spatial queries. There are three types of semantic data repositories: (1) triple store solutions (RDF4j, Fuseki, GraphDB, Virtuoso), (2) property graph databases (Neo4j), and (3) an Ontology-Based Data Access (OBDA) approach (Ontop). Experiments were conducted to compare each repository's efficiency (e.g., query response time) in handling geometric, topological, and spatial-semantic related queries. The results show that Virtuoso achieves the overall best performance in both non-spatial and spatial-semantic queries. The OBDA solution, Ontop, has the second-best query performance in spatial and complex queries and the best storage efficiency, requiring the least data-to-RDF conversion efforts. Other triple store solutions suffer from various issues that cause performance bottlenecks in handling spatial queries, such as inefficient memory management and lack of proper query optimization.
... Conformance testing was performed with an updated version of an existing GeoSPARQL compliance benchmark test. [3]. ...
... Another touted benefit of DGGSes is their ability to represent both raster and vector spatial information in unified form, for a given spatial accuracy. Commercial companies exist internationally that specilise in raster and vector spatial data integration 2 via DGGS and some large technology companies are known to employ DGGS for large-scale spatial data operations 3 . ...
... We chose an extended version of the GeoSPARQL 1.0 compliance benchmark [3] to test for the compatibility of the given implementations. We added new sub-tests for the existing requirements in order to include the new DGGS literals in the testing. ...
We set out to determine the feasibility of implementing Discrete Global Grid System (DGGS) representations of geometry support in a GeoSPARQL-enabled triplestore, and test the GeoSPARQL compliance for it. The implementation is a variant of Apache Jena's existing GeoSPARQL support. Compliance is tested using an adapted implementation of the GeoSPARQL Compliance Benchmark testing system developed previously to test for GeoSPARQL 1.0 compliance. The benchmark results confirm that a majority of the functions which were set out to be implemented in the course of this paper were implemented correctly and points out possible future work for full compliance.
... To test whether the reference implementation and all following implementations fulfil the criteria that the given standard sets, compliance benchmarking can be used. [13] created the first compliance benchmark for GeoSPARQL 1.0 using the HOBBIT benchmarking platform [34]. Once an execution of the GeoSPARQL compliance benchmark is finished, it may produce a benchmark result in RDF (https: //github.com/hobbit-project/platform/issues/531, ...
... The GeoSPARQL implementation of the Apache Jena software library GeoSPARQL-Jena [38] provides, according to recent benchmarks [13], the only complete implementation of the GeoSPARQL 1.0 specification. In addition, GeoSPARQL-Jena has been extended in a prototypical use case to support raster data in [39]. ...
In 2012 the Open Geospatial Consortium published GeoSPARQL defining ``an RDF/OWL ontology for [spatial] information'', ``SPARQL extension functions'' for performing spatial operations on RDF data and ``RIF rules'' defining entailments to be drawn from graph pattern matching.
In the 8+ years since its publication, GeoSPARQL has become the most important spatial Semantic Web standard, as judged by references to it in other Semantic Web standards and its wide use for Semantic Web data.
An update to GeoSPARQL was proposed in 2019 to deliver a version 1.1 with a charter to: handle outstanding change requests and source new ones from the user community and to "better present" the standard, that is to better link all the standard's parts and better document \& exemplify elements. Expected updates included new geometry representations, alignments to other ontologies, handling of new spatial referencing systems, and new artifact
presentation. This paper describes motivating change requests and actual resultant updates in the candidate version 1.1 of the standard alongside reference implementations and usage examples.
We also describe the theory behind particular updates, initial implementations of many parts of the standard, and our expectations for GeoSPARQL 1.1's use.
... Also, triplestores supports GeoSPARQL in many different ways. (Jovanovik et al., 2021) tested the GeoSPARQL support of some triplestores and pointed out, that the choice of the right triplestore is important for a good geometry support. Their results also show, that there is no triplestore which fully supports GeoSPARQL. ...
The integration of geodata and building models is one of the current challenges in the AECOO (architecture, engineering, construction , owner, operation) domain. Data from Building Information Models (BIM) and Geographical Information Systems (GIS) can't be simply mapped 1:1 to each other because of their different domains. One possible approach is to convert all data in a domain-independent format and link them together in a semantic database. To demonstrate, how this data integration can be done in a federated database architecture, we utilize concepts of the semantic web, ontologies and the Resource Description Framework (RDF). It turns out, however, that traditional object-relational approaches provide more efficient access methods on geometrical representations than triplestores. Therefore we developed a hybrid approach with files, geodatabases and triplestores. This work-in-progess-paper (extend abstract) demonstrates our intermediate research results by practical examples and identifies opportunities and limitations of the hybrid approach.
... net/ (accessed on 23 November 2021)). This has entailed geospatial data playing a pre-eminent role in the Web of Data cloud, operating as central nexuses that interconnect events, people, and objects [6] and offering an ever-growing semantic representation of the geospatial information wealth [7]. ...
... According to [1], they are capable of better addressing several types of issues at which relational databases struggle or are not intended to accomplish: queries with many joins across entities [11], queries with variable properties [11], and ontological inference on datasets. The World Wide Web Consortium (W3C) has collected a compendium of existing triple stores (https://www.w3.org/2001 /sw/wiki/Category:Triple_Store (accessed on 23 November 2021)), where different implementations related to the geospatial domain can be found, and recent works have tested GeoSPARQL compliance in diverse triple store [7], highlighting, for instance, Apache Marmotta (http://marmotta.apache.org/ (accessed on 23 November 2021)), Parliament (https://github.com/SemWebCentral/parliament ...
... However, the application of queries with geospatial functions is limited, and GeoSPARQL is not entirely compliant. Additionally, considering the increasing approaches based on GeoSPARQL, some works have provided ways to measure the support in GeoSPARQL-enabled RDF triple stores [7,[47][48][49]. Even a benchmark utilizing GeoSPARQL constructs was defined, facing all phases of federated query processing [50]. ...
Geospatial data is increasingly being made available on the Web as knowledge graphs using Linked Data principles. This entails adopting the best practices for publishing, retrieving, and using data, providing relevant initiatives that play a prominent role in the Web of Data. Despite the appropriate progress related to the amount of geospatial data available, knowledge graphs still face significant limitations in the GIScience community since their use, consumption, and exploitation are scarce, especially considering that just a few developments retrieve and consume geospatial knowledge graphs from within GIS. To overcome these limitations and address some critical challenges of GIScience, standards and specific best practices for publishing, retrieving, and using geospatial data on the Web have appeared. Nevertheless, there are few developments and experiences that support the possibility of expressing queries across diverse knowledge graphs to retrieve and process geospatial data from different and distributed sources. In this scenario, we present an approach to request, retrieve, and consume (geospatial) knowledge graphs available at diverse and distributed platforms, prototypically implemented on Apache Marmotta, supporting SPARQL 1.1 and GeoSPARQL standards. Moreover, our approach enables the consumption of geospatial knowledge graphs through a lightweight web application or QGIS. The potential of this work is shown with two examples that use GeoSPARQL-based knowledge graphs.
... The GeoSPARQL standard, issued in 2012 by the Open Geospatial Consortium 18 (OGC) 1 is one of the most popular Semantic Web standards for spatial data. 2 The original 19 release -GeoSPARQL 1.0 [4] -contained: 20 • a specification document 21 -the main GeoSPARQL document defining, in human-readable terms and with 22 code snippets, most elements of the standard including ontology elements, 23 geospatial functions that may be performed on Resource Description Format 24 (RDF) [5] data via SPARQL [6,7] queries, entailment rules in the Rules Inter- 25 change Format (RIF) [8] for RDF reasoning and requirements & abstract tests 26 for testing ontology data and function implementations 27 • an RDF/OWL [9] schema 28 -the GeoSPARQL ontology -Semantic Web data model -in an RDF file 29 were then made. 48 The authors note that in the 3+ years since that statement's publication, GeoSPARQL 49 1.0 has become far more widely supported by Semantic Web databases (so-called "triple-50 stores") and other Semantic Web applications, as evidenced by frequent attempts to 51 benchmark geospatial-aware triplestores for GeoSPARQL compliance and performance 52 [13][14][15][16]. Some further notes on GeoSPARQL support is provided in Section 6.1. ...
... practices of standards of any kind are that they are first defined and then 512 implemented in reference implementations. To test whether the reference implementa-513 tion and all following implementations fulfill the criteria that the given standard sets, 514 compliance benchmarking can be used.[13] created the first compliance benchmark for 515 GeoSPARQL 1.0 using the HOBBIT benchmarking platform[33]. ...
... GeoSPARQL implementation of the Apache Jena software library GeoSPARQL-557Jena[37] provides, according to recent benchmarks[13], the only complete implemen-558 tation of the GeoSPARQL 1.0 specification. In addition, GeoSPARQL-Jena has been 559 extended in a prototypical use case to support raster data in[38]. ...
In 2012 the Open Geospatial Consortium published GeoSPARQL defining “an RDF/OWL ontology for [spatial] information”, “SPARQL extension functions” for performing spatial operations on RDF data and “RIF rules” defining entailments to be drawn from graph pattern matching. In the 8+ years since its publication, GeoSPARQL has become the most important spatial Semantic Web standard, as judged by references to it in other Semantic Web standards and its wide use for Semantic Web data. An update to GeoSPARQL was proposed in 2019 to deliver a version 1.1 with a charter to: handle outstanding change requests and source new ones from the user community and to “better present” the standard, that is to better link all the standard’s parts and better document & exemplify elements. Expected updates included new geometry representations, alignments to other ontologies, handling of new spatial referencing systems, and new artifact presentation. In this paper, we describe motivating change requests and actual resultant updates in the candidate version 1.1 of the standard alongside reference implementations and usage examples. We also describe the theory behind particular updates, initial implementations of many parts of the standard, and our expectations for GeoSPARQL 1.1’s use.
... On top of these core databases, the RDF4J API can be extended with SPARQL Inferencing Notation (SPIN) rule-based reasoning functionalities [49]. The RDF4J framework implements GeoSPARQL functions, but it fails almost all of the GeoSPARQL benchmark tests [50]. The SAIL interface can be successfully used for communication between the RDF4J framework and an Apache HBase database in order to process petabytes of heterogeneous RDF data [51]. ...
... A GeoSPARQL compliance benchmark test used thirty benchmark requirements to prove that Jena Fuseki can handle geographical vector data representation literals. The Jena Fuseki server supports top level spatial and topological relation vocabulary components, as well as Resource Description Framework Schema (RDFS) entailment [50]. Because geospatial search support has been set as an essential requirement for the dynamic geospatial knowledge graph, the lack of it, as well as limited scalability, motivated further research on the triple store of choice for the Semantic 3D City Database proof of concept. ...
This paper presents a dynamic geospatial knowledge graph as part of The World Avatar project, with an underlying ontology based on CityGML 2.0 for three-dimensional geometrical city objects. We comprehensively evaluated, repaired and refined an existing CityGML ontology to produce an improved version that could pass the necessary tests and complete unit test development. A corresponding data transformation tool, originally designed to work alongside CityGML, was extended. This allowed for the transformation of original data into a form of semantic triples. We compared various scalable technologies for this semantic data storage and chose Blazegraph™ as it provided the required geospatial search functionality. We also evaluated scalable hardware data solutions and file systems using the publicly available CityGML 2.0 data of Charlottenburg in Berlin, Germany as a working example. The structural isomorphism of the CityGML schemas and the OntoCityGML Tbox allowed the data to be transformed without loss of information. Efficient geospatial search algorithms allowed us to retrieve building data from any point in a city using coordinates. The use of named graphs and namespaces for data partitioning ensured the system performance stayed well below its capacity limits. This was achieved by evaluating scalable and dedicated data storage hardware capable of hosting expansible file systems, which strengthened the architectural foundations of the target system.
... Until recently, no tools for this task had been adapted to the expectations of the geospatial community. Although the definition of vector data in Semantic Web standards is complete, the adoption of GIS data into Semantic Web software lags behind even to this day (Jovanovik et al., 2021). The GIS community did not see the need to migrate to a Linked Data approach, yet. ...
Geodesists work in Industry 4.0 and Spatial Information Management by using cross linked machines, people and data. Yet, one of the most popular technologies for interlinking data-Semantic Web technologies-have been largely absent from the geodesy community, because of the slow development of standards, a mandatory non-trivial conversion between geospatial features and graph data, and a lack of commonly available GIS tools to achieve this. This is slowly changing due to an increased awareness of the advantages of Linked Data technology in the GIS community and an improvement of standards in the Semantic Web community. Hence, the importance of open source software, open geodata and open access increases. A fundamental requirement for data sharing is the use of standardised data models. In this paper we compare two different modelling approaches for Irish Ogham Stones as a best practice example for linked open data management: One approach uses Wikidata, and the other a custom ontology. While Wikidata offers direct integration into the Linked Open Data Cloud and needs less technological expertise, using a custom ontology enables the creation of best-fitting data models. Both approaches facilitate the use of new information sources for the geodesy community. We aim to demonstrate how Linked Open Geodata can be re-used and further enriched with information from other open sources such as spatial data from OpenStreetMap. For this purpose, we also present a QGIS plugin and a modified geospatial web service, as well as a geo-optimised Linked Data browser, as solutions for bridging the gap between geospatial features and Linked Open Data triples.
... On top of these core databases, the RDF4J API can be extended with SPARQL Inferencing Notation (SPIN) rule based reasoning functionalities [37]. The RDF4J framework implements GeoSPARQL functions, but it fails almost all of the GeoSPARQL benchmark tests [33]. The SAIL interface can be successfully used for communication between the RDF4J framework and an Apache HBase database in order to process petabytes of heterogeneous RDF data [55]. ...
... A GeoSPARQL compliance benchmark test used thirty benchmark requirements to prove that Jena Fuseki can handle geographical vector data representation literals. The Jena Fuseki server supports top level spatial and topological relation vocabulary components, as well as Resource Description Framework Schema (RDFS) entailment [33]. Because geospatial search support has been set as an essential requirement for the dynamic geospatial knowledge graph, the lack of it, as well as limited scalability, motivated further research on the triple store of choice for the Semantic 3D City Database proof of concept. ...
Available on https://como.ceb.cam.ac.uk/preprints/273/.
This paper presents a dynamic geospatial knowledge graph as part of The World Avatar project, with an underlying ontology based on CityGML 2.0 for three-dimensional geometrical city objects. We comprehensively evaluated, repaired and refined an existing CityGML ontology to produce an improved version that could pass the necessary tests and complete unit test development. A corresponding data transformation tool, originally designed to work alongside CityGML, was extended. This allowed for the transformation of original data into a form of semantic triples. We compared various scalable technologies for this semantic data storage and chose Blazegraph™ as it provided the required geospatial search functionality. We also evaluated scalable hardware data solutions and file systems using the publicly available CityGML 2.0 data of Charlottenburg in Berlin, Germany as a working example. The structural isomorphism of the CityGML schemas and the OntoCityGML Tbox allowed the data to be transformed without loss of information. Efficient geospatial search algorithms allowed us to retrieve building data from any point in a city using coordinates. The use of named graphs and namespaces for data partitioning ensured the system performance stayed well below its capacity limits. This was achieved by using scalable and dedicated data storage hardware capable of hosting expansible file systems, which strengthened the architectural foundations of the target system.
Highlights
• OntoCityGML based on CityGML 2.0 and W3C standards.
• Architecture definition for a dynamic geospatial knowledge graph enabled by the Semantic 3D City Database.
• Data interoperability capabilities provided by means of sustainable digitisation practices.
