## 1 Introduction
Two of them are dedicated to spatial management. Objects are represented via a set of primitives like points, lines, polygons, envelopes or solids in the GEO sub-ontology. In addition to spatial information, restrictions defining relations among objects, like minimal distances between water pipes, are stored in the USRO sub-ontology. This allows to check construction code compliance via SPARQL [14] requests. However, the approach is limited to utility lines without using completion strategies nor confidence intervals. A case of data incompleteness and corresponding semantic reconstitution is proposed in [15] to find the correct semantic for city objects which are only represented by their geometry. An automated workflow uses heuristics to identify the type of geometrical objects. Surfaces can be classified as walls, slabs or roofs. Information related to precision in GIS Layers can be modelled using the fuzzy set theory. [PERSON] [16] developed a membership function to qualify the level of precision of an object's geo-positioning. The FOOSBALL prototype [17; 18] integrates membership functions into queries, assigning a confidence level to query results. [PERSON] et al. [19] propose adding a fuzzy utility zone to the utility network application domain extension (ADE) [20]. This zone integrates precision information to the positioning of objects. Techniques for visualizing uncertainty presented in [21] show alternatives in expressing the confidence level associated with a point representation on a 2D map. The quantitative assessment of uncertainty parameters based on topographic predictions can be undertaken through the use of a series of estimators enumerated in [22] (e.g., Bias, RBias, ARAD, STD, RMSE). Approaches mentioned above provide solutions to integrate data available in diverse formats, using mapping approaches based on ontologies. IFC and CityGML mainly cover surface objects or isolated groups of subsurface objects (utility network ADE [20]). Only limited solutions in terms of data completion and appreciation of confidence levels can be found. There is no coherent strategy that classifies existing subsurface objects, completes missing data and assigns confidence levels to the derived attributes, which are finally consolidated on an object-class level. Research presented in this paper is part of the InnoSubsurface project, funded by the Swiss innovation agency grant number 35265.1 IP-ICT. The project exploits open government data from the State of Geneva [23] by a resource description framework (RDF triple store), providing interoperability between BIM, GIS ontologies and a minimal model for subsurface objects (Figure 1). If required, the data are completed [24; 25], such that a full description of the geometric primitives of subsurface objects is possible. The entire workflow has been tested on two subsurface volumes in Geneva and feasibility demonstrated by the implementation partner [26]. This paper concentrates on the description of strategies to complete missing geometric attributes, integrating a probabilistic approach to indicate the interval of 3D spatial confidence for the obtained results (SHACL, Figure 1). Confidence levels are visualized by a twofold object representation in the frontend of the system.