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

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i3mainz - Jahresbericht 2018

2019

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Jahrebericht 2018

Im Jahresbericht werden die Projekte und Aktivitäten des i3mainz in komprimierter Form vorgestellt.

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Evaluating linked data location based services using the example of Stolpersteine

2019

T. Homburg; K. Böhm; N. Bruhn; G. Hubrich

RTF

15th International Conference on Location Based Services (LBS 2019) 11–13 November 2019, Vienna, Austria G. Gartner and H. Huang

In this publication we introduce a linked data powered application which assists users to find so-called Stolpersteine, stones commemorating Jewish victims of the second world war. We show the feasibility of a dedicated location based service as an app using linked data resources and evaluate this approach against local data sources gathered by communities to find out if the current linked data environment can equally and/or sufficiently support an application in this knowledge domain.

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Connected Semantic Concepts as a Base for Optimal Recording and Computer-Based Modelling of Cultural Heritage Objects

2019

J.J. Ponciano; A. Karmacharya; S. Wefers; P. Atorf; F. BOOCHS

RTF

Structural Analysis of Historical Constructions

3D and spectral digital recording of cultural heritage monuments is a common activity for their documentation, preservation, conservation management, and reconstruction. Recent developments in 3D and spectral technologies have provided enough flexibility in selecting one technology over another, depending on the data content and quality demands of the data application. Each technology has its own pros/cons, suited perfectly to some situations and not to others. They are mostly unknown to humanities experts, besides having a limited understanding of the data requirements demanded by the research question. These are often left to technical experts who again have a limited understanding of cultural heritage requirements. A common point of view has to be achieved through interdisciplinary discussions. Such agreements need to be documented for their future references and re-uses. We present a method based on semantic concepts that not only documents the semantic essence of such discussions, but also uses it to infer a guidance mechanism that recommends technologies/technical process to generate the required data based on individual needs. Experts' knowledge is represented explicitly through a knowledge representation that allows machines to manage and infer recommendations. First, descriptive semantics guide end users to select the optimal technology/technologies for recording data. Second, structured knowledge controls the processing chain extracting and classifying objects contained in the acquired data. Circumstantial situations during object recording and the behaviour of the technologies in that situation are taken into account. We will explain the approach as such and give results from tests at a CH object.

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Identification and classification of objects in 3D point clouds based on a semantic concept

2019

J.J. Ponciano; F. Boochs; A. Trémeau

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Modélisation sémantique et logique pour une simulation multi-agent dans le contexte de gestion de catastrophe

2019

C. Prudhomme; C. Cruz; F. BOOCHS

RTF

Spatial Analysis and Geomatics (SAGEO) 2019

Disaster management requires both individual and collaborative preparedness among the various stakeholders.
Collaborative exercises aim to train stakeholders to apply the plans prepared and to identify potential problems and areas for improvement. As these exercises are costly, computer simulation is an interesting tool to evaluate preparation through a wider variety of contexts.
However, research on simulation and disaster management focuses on a particular problem rather than on the overall assessment of the plans prepared. This limitation is explained by the challenge of creating a simulation model that can represent and adapt to a wide variety of plans from various disciplines.
The work presented in this paper addresses this challenge by adapting the simulation model based on disaster management information and plans integrated into a knowledge base. The simulation model created is then automatically programmed to perform simulation experiments to improve action plans.
The results of the experiments are analyzed in order to generate new knowledge and know-how to enrich disaster management plans in a virtuous cycle.
This paper presents a proof of concept on the French national Novi plan, for which simulation experiments have made it possible to know the impact of the distribution of doctors on the application of the plan as well as to identify their distribution.

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Semantische Geoinformationssysteme: Integration heterogener Geodaten am Beispiel XErleben

2018

T. Homburg; C. Prudhomme; F. BOOCHS

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This poster has been presented during the conference Der Fachaustausch Geoinformation (http://www.fachaustausch-geoinformation.de/) organized by GeoNet.MRN to exhibit the semantic geographic information system developed in the context of SemGIS project. The poster shows the approaches used to integrate heterogeneous data sets from different sources. These data sets can then, be enriched through resources from the Semantic Web. An example of such enrichment is presented from an integrated XErleben data. Finally, it illustrates the functionalities of the system to query and visualize data, but also the downlift of selected data according to different standardized formats.

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Semantische Geoinformationssysteme: Integration und Management von heterogener Geodaten

2018

T. Homburg; C. Prudhomme; F. BOOCHS

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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.

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