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In the context of geo-data infrastructures users may want to combine data from different sources and expect consistent data. If bothdatasets are maintained separately, different capturing methods and intervals leads to inconsistencies in geometry and semantic, evenif the same reality has been modelled. Our project aims to automatically harmonize such datasets and to allow an efficientactualisation of the semantics. The application domain in our project is cadastral and topographic datasets. To resolve geometricconflicts between topographic and cadastral data a local nearest neighbour method was used to identify perpendicular distancesbetween a node in the topographic and an edge in the cadastral dataset. The perpendicular distances are reduced iteratively in aconstraint least squares adjustment (LSA) process moving the coordinates from node and edge towards each other. The adjustmentresult has to be checked for conflicts caused by the movement of the coordinates in the LSA.  The correct choice of matching partners has a major influence on the result of the LSA. If wrong matching partners are linked awrong adaptation is derived. Therefore we present an improved matching method, where we take distance, orientation and semanticsimilarity of the neighbouring objects into account. Using Machine Learning techniques we obtain corresponding land-use classes.From these a measurement for the semantic distance is derived. It is combined with the orientation difference to generate a matchingprobability for the two matching candidates. Examples show the benefit of the proposed similarity measure

Using Semantic Distance to Support Geometric Harmonisation of Cadastral and Topographical Data