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3D geometry can be described in many ways, with both a varying syntax and a varying semantics. As a result, several very diverse schemas and file formats can be deployed to describe geometry, depending on the application domain in question. In a multidisciplinary domain such as the domain of architecture, engineering and construction (AEC), this range of specialised schemas makes file format conversions inevitable. The approach adopted by current conversion tools, however, often results in a loss of information, most often due to a ‘mistranslation’ between different syntaxes and/or semantics, leading to errors and limitations in the design conception stage and to inefficiency due to the required remodelling efforts. An approach based on semantic web technology may reduce the loss of information significantly, leading to an improved processing of 3D information and hence to an improved design practice in the AEC domain. This paper documents our investigation of the nature of this 3D information conversion problem and how it may be encompassed using semantic web technology. In a double test case, we show how the specific deployment of semantic rule languages and an appropriate inference engine are to be adopted to improve this 3D information exchange. It shows how semantic web technology allows the coexistence of diverse descriptions of the same 3D information, interlinked through explicit conversion rules. Although only a simple example is used to document the process, and a more in-depth investigation is needed, the initial results indicate the suggested approach to be a useful alternative approach to obtain an improved 3D information exchange

Abstract. As interest in provenance grows among the Semantic Web community, it is recognized as a useful tool across many domains. However, existing automatic provenance collection techniques are not universally applicable. Most existing methods either rely on (low-level) observed provenance, or require that the user discloses formal workflows. In this paper, we propose a new approach for automatic discovery of provenance, at multiple levels of granularity. To accomplish this, we detect entity derivations, relying on clustering algorithms, linked data and semantic similarity. The resulting derivations are structured in compliance with the Provenance Data Model (PROV-DM). While the proposed approach is purposely kept general, allowing adaptation in many use cases, we provide an implementation for one of these use cases, namely discovering the sources of news articles. With this implementation, we were able to detect 73% of the original sources of 410 news stories, at 68% precision. Lastly, we discuss possible improvements and future work.

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