Semantic Aware Representing and Intelligent Processing of Information in an Experimental domain: the Seismic Engineering Research Case

Seismic Engineering research projects’ experiments generate an enormous amount of data that would benefit researchers and experimentalists of the community if could be shared with their semantics. Semantics is the meaning of a data element and a term alike. For example, the semantics of the term experiment is a scientific research performed to conduct a controlled test or investigation. Ontology is a key technique by which one can annotate semantics and provide a common, comprehensible foundation for the resources on the Semantic Web. The development of the domain ontology requires expertise both in the domain to model as well as in the ontology development. This means that people from very different backgrounds, such as Seismic Engineering and Computer Science should be involved in the process of creating ontology. With the invention of the Semantic Web, computing paradigm is experiencing a shift from databases to Knowledge Bases (KBs), in which ontologies play a major role in enabling reasoning power that can make implicit facts explicit to produce better results for users. To enable an ontology and a dataset automatically exploring the relevant ontology and datasets from the external sources, these can be linked to the Linked Open Data (LOD) cloud, which is an online repository of a large amount of interconnected datasets published in RDF. Throughout the past few decades, database technologies have been advancing continuously and showing their potential in dealing with large collection of data, but they were not originally designed to deal with the semantics of data. Managing data with the Semantic Web tools offers a number of advantages over database tools, including classifying, matching, mapping and querying data. Hence we translate our database based system that was managing the data of Seismic Engineering research projects and experiments into KB-based system. In addition, we also link our ontology and datasets to the LOD cloud. In this thesis, we have been working to address the following issues. To the best of knowledge the Semantic Web still lacks the ontology that can be used for representing information related to Seismic Engineering research projects and experiments. Publishing vocabulary in this domain has largely been overlooked and no suitable vocabulary is yet developed in this very domain to model data in RDF. The vocabulary is an essential component that can provide logistics to a data engineer when modeling data in RDF to include them in the LOD cloud. Ontology integration is another challenge that we had to tackle. To manage the data of a specific field of interest, domain specific ontologies provide essential support. However, they alone can hardly be sufficient to assign meaning also to the generic terms that often appear in a data source. That necessitates the use of the integrated knowledge of the generic ontology and the domain specific one. To address the aforementioned issues, this thesis presents the development of a Seismic Engineering Research Projects and Experiments Ontology (SEPREMO) with a focus on the management of research projects and experiments. We have used DERA methodology for ontology development. The developed ontology was evaluated by a number of domain experts. Data originating from scientific experiments such as cyclic and pseudodynamic tests were also published in RDF. We exploited the power of Semantic Web technologies, namely Jena, Virtuoso and VirtGraph tools in order to publish, storage and manage RDF data, respectively. Finally, a system was developed with the full integration of ontology, experimental data and tools, to evaluate the effectiveness of the KB-based approach; it yielded favorable outcomes. For ontology integration with WordNet, we implemented a semi-automatic facet based algorithm. We also present an approach for publishing both the ontology and the experimental data into the LOD Cloud. In order to model the concepts complementing the vocabulary that we need for the experimental data representation, we suitably extended the SEPREMO ontology. Moreover, the work focuses on RDF data sets interlinking technique by aligning concepts and entities scattered over the cloud.