An approach for measuring rdf data completeness

International audienc

Methods for Matching of Linked Open Social Science Data

In recent years, the concept of Linked Open Data (LOD), has gained popularity and acceptance across various communities and domains. Science politics and organizations claim that the potential of semantic technologies and data exposed in this manner may support and enhance research processes and infrastructures providing research information and services. In this thesis, we investigate whether these expectations can be met in the domain of the social sciences. In particular, we analyse and develop methods for matching social scientific data that is published as Linked Data, which we introduce as Linked Open Social Science Data. Based on expert interviews and a prototype application, we investigate the current consumption of LOD in the social sciences and its requirements. Following these insights, we first focus on the complete publication of Linked Open Social Science Data by extending and developing domain-specific ontologies for representing research communities, research data and thesauri. In the second part, methods for matching Linked Open Social Science Data are developed that address particular patterns and characteristics of the data typically used in social research. The results of this work contribute towards enabling a meaningful application of Linked Data in a scientific domain

Semantic Audio Analysis Utilities and Applications.

PhDExtraction, representation, organisation and application of metadata about audio recordings are in the concern of semantic audio analysis. Our broad interpretation, aligned with recent developments in the field, includes methodological aspects of semantic audio, such as those related to information management, knowledge representation and applications of the extracted information. In particular, we look at how Semantic Web technologies may be used to enhance information management practices in two audio related areas: music informatics and music production. In the first area, we are concerned with music information retrieval (MIR) and related research. We examine how structured data may be used to support reproducibility and provenance of extracted information, and aim to support multi-modality and context adaptation in the analysis. In creative music production, our goals can be summarised as follows: O↵-the-shelf sound editors do not hold appropriately structured information about the edited material, thus human-computer interaction is inefficient. We believe that recent developments in sound analysis and music understanding are capable of bringing about significant improvements in the music production workflow. Providing visual cues related to music structure can serve as an example of intelligent, context-dependent functionality. The central contributions of this work are a Semantic Web ontology for describing recording studios, including a model of technological artefacts used in music production, methodologies for collecting data about music production workflows and describing the work of audio engineers which facilitates capturing their contribution to music production, and finally a framework for creating Web-based applications for automated audio analysis. This has applications demonstrating how Semantic Web technologies and ontologies can facilitate interoperability between music research tools, and the creation of semantic audio software, for instance, for music recommendation, temperament estimation or multi-modal music tutorin