Institut für Raumbezogene Informations- und Messtechnik
Hochschule Mainz - University of Applied Sciences

Semantische Modellierung

Semantik wird auch als Bedeutungslehre bezeichnet. Es extrahiert Bedeutungen von Wörtern, SÀtzen, Phrasen, Symbolen und andere Formen der Information durch die zugrunde liegenden Beziehungen der Komponenten zueinander.

Die Entwicklung des Semantischen Webs hat die semantischen Modellierung in der Informationstechnologie revolutioniert. Semantische Modellierung als wichtige Komponente bei der Verwaltung großer und vielfĂ€ltiger Datenmengen gewinnt zunehmend an Bedeutung. Logische AusdrĂŒcke bringen Maschinen dazu den Menschen in der Informationsverarbeitung zu unterstĂŒtzen. Die Abstraktion der realen Welt kann durch Modelle ausgedrĂŒckt definiert werden. Solche semantischen Modelle sind Grundlagen von Semantic Web Anwendungen die unser Institut erarbeitet.

Das semantische Modell definiert Wissen im Hintergrund und schafft somit einen höheren Grad an InteroperabilitÀt von Daten. Neben der InteroperabilitÀt von Inforamtionen erforscht das i3mainz weitere Potentiale um mit Hilfe der Sematik Wissen abzuleiten und dabei neues Wissen zu entdecken. Erfolgreich werden semantische Technologien in verschiedenen Forschungsprojekten umgesetzt.


Prof. Dr.-Ing. Frank Boochs

Tel.: +49 6131-628-1432
Fax.: +49 6131-628-91432



The KnowDIP project aims at the conception of a framework for an automatic object detection in unstructured and heterogeneous data. This framework uses a representation of human kn

The SemGIS project aims at interpreting heterogeneous data by creating interoperability via a semantic layer between former unrelated spatial and non-spatial data sets. Applicatio


Automatic Integration of Spatial Data into the Semantic Web


C. Prudhomme; T. Homburg; J.J. Ponciano; F. Boochs; A. Roxin; C. Cruz


WebIST 2017

For several years, many researchers tried to semantically integrate geospatial datasets into the semantic web. Although, there are many general means of integrating interconnected relational datasets (e.g. R2RML), importing schema-less relational geospatial data remains a major challenge in the semantic web community. In our project SemGIS we face significant importation challenges of schema-less geodatasets, in various data formats without relations to the semantic web. We therefore developed an automatic process of semantification for aforementioned data using among others the geometry of spatial objects. We combine Natural Language processing with geographic and semantic tools in order to extract semantic information of spatial data into a local ontology linked to existing semantic web resources. For our experiments, we used LinkedGeoData and Geonames ontologies to link semantic spatial information and compared links with DBpedia and Wikidata for other types of information. The aim of our experiments presented in this paper, is to examine the feasibility and limits of an automated integration of spatial data into a semantic knowledge base and to assess its correctness according to different open datasets. Other ways to link these open datasets have been applied and we used the different results for evaluating our automatic approach.

Katastrophenmanagement: Die geflutete Stadt


C. Prudhomme



Ontology-based Knowledge Representation for Recommendation of Optimal Recording Strategies - Photogrammetry and Laser Scanning as Examples.


S. Wefers



Experts’ knowledge about optical technologies for spatial and spectral recording is logically structured and stored in an ontology-based knowledge representation with the aim to provide objective recommendations for recording strategies. Besides operational functionalities and technical parameters such as measurement principles, instruments, and setups further factors such as the targeted application, data, physical characteristics of the object, and external influences are considered creating a holistic view on spectral and spatial recording strategies. Through this approach impacting factors on the technologies and generated data are identified. Semantic technologies allow to flexibly store this knowledge in a hierarchical class structure with dependencies, interrelations and description logic statements. Through an inference system the knowledge can be retrieved adapted to individual needs.

The Labelling System: A Bottom-up Approach for Enriched Vocabularies in the Humanities


F. Thiery; T. Engel


43rd Annual Conference on Computer Applications and Quantitative Methods in Archaeology, CAA 2015

Shared thesauri of concepts are increasingly used in the process of data modelling and annotating resources in the Semantic Web. This growing family of linked data resources follows a top-down principle. In contrast, the Labeling System follows a bottom-up approach, enabling scientists working in the digital humanities to manage, create and publish their own controlled vocabularies in SKOS (Simple Knowledge Organization System). The created concepts can then be interlinked with well-known LOD (Linked Open Data) resources, a process named the ‘Labeling Approach’. The Labeling System is domain independent, while uniting perspectives of different scientific disciplines on the same label and therefore contributing to interdisciplinary collaboration for building up cross and inter-domain linked data communities. This paper addresses principles of the Labeling System in the light of archaeological use cases.

Interpreting Heterogenous Geospatial Data using Semantic Web Technologies


T. Homburg; A. Karmacharya; F. BOOCHS; C. Cruz; A.M. Roxin


Computational Science and Its Applications -- ICCSA 2016

The paper presents work on implementation of semantic technologies within a geospatial environment to provide a common base for further semantic interpretation. The work adds on the current works in similar areas where priorities are more on spatial data integration. We assert that having a common unified semantic view on heterogeneous datasets provides a dimension that allows us to extend beyond conventional concepts of searchability, reusability, composability and interoperability of digital geospatial data. It provides contextual understanding on geodata that will enhance effective interpretations through possible reasoning capabilities.  We highlight this through use cases in disaster management and planned land use that are significantly different. This paper illustrates the work that firstly follows existing Semantic Web standards when dealing with vector geodata and secondly extends current standards when dealing with raster geodata and more advanced geospatial operations.



C. Prudhomme; A. Roxin; C. Cruz; F. Boochs


The 15th International Conference on Informatics in Economy 2016

With the climate change, disasters occur more frequently and the need for efficient disaster management systems becomes highly recommended to save lives. This paper deals with a study of existing systems, with the intention of determining the main recent improvement in the domain. The heterogeneous data integration process is a major central point. Thus, a semantic system with three main components is proposed as a new position in the disaster management systems. These three components are a knowledge base, a reasoner and a semantic catalogue. The knowledge base provides a controlled vocabulary and allows storing information retrieval. The semantic catalogue facilitates the access to data sources adapted to user's and agent's needs. The reasoner analyzes the information in the knowledge base thus replying to the user queries. In addition, the reasoner aims at adding automatically new data sources in the semantic catalogue. The fast access to a great number of data sources is of benefit for decision-making systems such as disaster management systems.

Vorstellung SemGIS Projekt - Einblick und Status


T. Homburg; C. Prudhomme



Knowledge guided object detection and identification in 3D point clouds


A. Karmacharya; F. BOOCHS


Videometrics, Range Imaging, and Applications XIII, 952804

Modern instruments like laser scanner and 3D cameras or image based techniques like structure from motion produce huge point clouds as base for further object analysis. This has considerably changed the way of data compilation away from selective manually guided processes towards automatic and computer supported strategies. However it’s still a long way to achieve the quality and robustness of manual processes as data sets are mostly very complex. Looking at existing strategies 3D data processing for object detections and reconstruction rely heavily on either data driven or model driven approaches. These approaches come with their limitation on depending highly on the nature of data and inability to handle any deviation. Furthermore, the lack of capabilities to integrate other data or information in between the processing steps further exposes their limitations. This restricts the approaches to be executed with strict predefined strategy and does not allow deviations when and if new unexpected situations arise. We propose a solution that induces intelligence in the processing activities through the usage of semantics. The solution binds the objects along with other related knowledge domains to the numerical processing to facilitate the detection of geometries and then uses experts’ inference rules to annotate them. The solution was tested within the prototypical application of the research project “Wissensbasierte Detektion von Objekten in Punktwolken fĂŒr Anwendungen im Ingenieurbereich (WiDOP)”. The flexibility of the solution is demonstrated through two entirely different USE Case scenarios: Deutsche Bahn (German Railway System) for the outdoor scenarios and Fraport (Frankfort Airport) for the indoor scenarios. Apart from the difference in their environments, they provide different conditions, which the solution needs to consider. While locations of the objects in Fraport were previously known, that of DB were not known at the beginning. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.