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Handling huge and complex 3D geometries with Semantic Web technology
Copyright policy )Handling huge and complex 3D geometries with Semantic Web technology
In INCEPTION, a European collaborative research project, a Heritage BIM (H-BIM) ontology is being developed to store all relevant semantic data concerning cultural heritage objects. Similar to other projects dealing with storing semantics, one of the major questions is whether, and if yes, how should geometry be stored using semantic web technology. The INCEPTION cross-disciplinary research consortium chose to allow the storage of all relevant geometric information using semantic web technology. The alternative is to store geometry in a different way, or storing only the aggregated parts of geometry, for example through bounding box representations.The geometry is originally generated by a CAD/BIM system and, as we are dealing with Cultural Heritage, in many cases it is derived from 3D point clouds. These result in a large amount of 3D data to be stored using semantic web technology. A well-known issue is that the performance of databases and inferencing engines for semantic web data drops considerably when the data grows to very large sizes. This paper explains how the performance issues on these large sets of geometric data can be solved while still being able to use the databases, inferencing engines, and the geometric data effectively.
- University of Florence Italy
- University of Ferrara Italy
- University of Siena Italy
Microsoft Academic Graph classification: Computer science Point cloud Ontology (information science) Semantics Semantic data model Minimum bounding box Semantic Web Geometric data analysis Information retrieval Cultural heritage
H-BIM, SEMANTIC WEB, CULTURAL HERITAGE, BIM, RDS, TRIPLE STORE, NO, 3D geometries, Semantic Web, H-BIM, Digital Heritage, 3D modelling, SEMANTIC WEB; CULTURAL HERITAGE; BIM; RDS; TRIPLE STORE, BIM, SEMANTIC WEB, RDS, TRIPLE STORE, 3D modelling, 3D geometries, CULTURAL HERITAGE, Digital Heritage
H-BIM, SEMANTIC WEB, CULTURAL HERITAGE, BIM, RDS, TRIPLE STORE, NO, 3D geometries, Semantic Web, H-BIM, Digital Heritage, 3D modelling, SEMANTIC WEB; CULTURAL HERITAGE; BIM; RDS; TRIPLE STORE, BIM, SEMANTIC WEB, RDS, TRIPLE STORE, 3D modelling, 3D geometries, CULTURAL HERITAGE, Digital Heritage
Microsoft Academic Graph classification: Computer science Point cloud Ontology (information science) Semantics Semantic data model Minimum bounding box Semantic Web Geometric data analysis Information retrieval Cultural heritage
[1] Arayici, Y.; Counsell, J.; Mahdjoubi, L.; Nagy, G. A.; Hawas, S.; Dweidar, K. (Eds.) (2017): Heritage Building Information Modelling. Routledge (UK). [OpenAIRE]
[2] Historic England (2017): BIM for Heritage: Developing a Historic Building Information Model. Swindon, Historic England [accessed 25 January 2018]. Available at: <https://historicengland.org.uk/images-books/publications/bim-for-heritage/>.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).9 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10% citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).9 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!
- University of Florence Italy
- University of Ferrara Italy
- University of Siena Italy
In INCEPTION, a European collaborative research project, a Heritage BIM (H-BIM) ontology is being developed to store all relevant semantic data concerning cultural heritage objects. Similar to other projects dealing with storing semantics, one of the major questions is whether, and if yes, how should geometry be stored using semantic web technology. The INCEPTION cross-disciplinary research consortium chose to allow the storage of all relevant geometric information using semantic web technology. The alternative is to store geometry in a different way, or storing only the aggregated parts of geometry, for example through bounding box representations.The geometry is originally generated by a CAD/BIM system and, as we are dealing with Cultural Heritage, in many cases it is derived from 3D point clouds. These result in a large amount of 3D data to be stored using semantic web technology. A well-known issue is that the performance of databases and inferencing engines for semantic web data drops considerably when the data grows to very large sizes. This paper explains how the performance issues on these large sets of geometric data can be solved while still being able to use the databases, inferencing engines, and the geometric data effectively.