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, Sätzen, 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 vielfältiger Datenmengen gewinnt zunehmend an Bedeutung. Logische Ausdrücke bringen Maschinen dazu den Menschen in der Informationsverarbeitung zu unterstützen. Die Abstraktion der realen Welt kann durch Modelle ausgedrückt 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 Interoperabilität von Daten. Neben der Interoperabilität 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.

Ansprechpartner

Prof. Dr.-Ing. Frank Boochs

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

Projekte

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…

Publikationen

Automatic Integration of Spatial Data into the Semantic Web

2017

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

RTF

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

2017

C. Prudhomme

RTF

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Towards the design of respond action in disaster management using knowledge modeling

2017

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

RTF

The Fourth International Conference on Information Systems for Crisis Response and Management in Mediterranean Countries (ISCRAM-med 2017)

This position paper highlights current problems linked to the aspects of the multi-agency collaboration during disaster response. The coordination and cooperation depend on the information sharing and use which must face up to interoperability, access rights, and quality problems. The research project aims at providing an assessment of information impact on the disaster response in order to support the decisionmaking about what information shared or what quality of data used to improve the response efficiency. Our research approach propose to combine an information system able to integrate heterogeneous data and a simulation system to assess different strategies of information sharing, dissemination and use. A knowledge base is used as a bridge between information system and simulation system. This knowledge base allows for designing dynamically a simulation according to open data and for managing the own knowledge and information known by each agent.


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

2017

S. Wefers

RTF

gis.Science

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.


Integration, quality assurance and usage of geospatial data with semantic tools

2017

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

RTF

gis.Science








How to optimally record cultural heritage objects? Decision support through connected knowledge.

2017

S. Wefers; A. Karmacharya; F. Boochs; G. Heinz

RTF

EVA Berlin 2017. Elektronische Medien & Kunst, Kultur und Historie 24. Berliner Veranstaltung der internationalen EVA-Serie Electronic Media and Visual Arts, 2017

Optical recording of material cultural heritage (CH) is a multidisciplinary activity where the understanding of cross-disciplinary semantics is vital for a successful completion. In many cases, a lack of understanding of transdisciplinary semantics slows this process down. The end users who are mostly humanities experts lack the technical knowledge of spatial and spectral recording and could therefore demand more than what is actually required or sufficient for the intended CH application. The negotiations between technical experts and the end users are a tedious process. We present a semantic-based decision support system, COSCHKR, that employs reasoning and recommends optimal recording technology(ies) according to the application requirements of the recorded and processed data. COSCHKR is an ontology-based knowledge model that implies the development of semantic technologies within the Semantic Web framework. It represents formalized knowledge of the disciplines involved in the process of optical recording of material CH. The paper describes the applicability of the model in spatial, spectral, and visualization applications and summarises current possibilities and challenges.


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

2016

F. Thiery; T. Engel

RTF

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

2016

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

RTF

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.