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

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

n.A.

–

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, COSCH^KR, that employs reasoning and recommends optimal recording technology(ies) according to the application requirements of the recorded and processed data. COSCH^KR 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.

–

How to Enrich Description Logics with Fuzziness

2017

M. Unold; C. Cruz

RTF

SAI Computing Conference

The paper describes the relation between fuzzy and non-fuzzy description logics. It gives an overview about current research in these areas and describes the difference between tasks for description logics and fuzzy logics. The paper also deals with the transformation properties of description logics to fuzzy logics and backwards. While the process of transformation from a description logic to a fuzzy logic is a trivial inclusion, the other way of reducing information from fuzzy logic to description logic is a difficult task, that will be topic of future work.

–

Digital 3D reconstructed models A proposition for structuring visualisation workflows using semantic technologies for recommendations

2017

S. Wefers; A. Karmacharya; M. Pfarr-Harfst; F. BOOCHS

RTF

Studies in Digital Heritage

It is common for cultural heritage applications to use spatial and/or spectral data for documentation, analysis and visualisation. Knowledge on data requirements coming from the cultural heritage application and technical alternatives to generate the required data based on object characteristics and other influencings factors paves the way for the optimal selection of a recording technology. It is a collaborative process requiring knowledge of experts from cultural heritage domains and technical domains. Currently, this knowledge is structured and stored in an ontology (so-called COSCHKR). It has the purpose to support CH experts not familiar with technologies through prescribing an optimal spatial or spectral recording strategy adapted to the physical characteristics of the cultural heritage object and the data requirements of the targeted CH application. The creation of digital 3D reconstructed models for analysis and visualisation purposes is getting more and more common within humanities disciplines. Therefore, an implementation of mechanisms involved in visualisation applications into this ontology would have huge benefits in creating a powerful recommendation solution. A structured view on such project workflows facilitates a rough match with the existing knowledge representation. Illustrating the overall structure of COSCHKR, this paper addresses and discusses challenges in structuring the processes of cultural heritage visualisation and implementing these into the ontology.

weitere Publikationen »