MatchMaking – A Tool to Match OWL Schemas

This paper describes a software tool that implements an instancebased schema matching technique for OWL dialects. The technique is based on a matching algorithm that depends on the definition of similarity functions that evaluate the semantic proximity of elements from two different schemas. The tool is engineered to accommodate different similarity functions and variations of the matching algorithm, thereby facilitating experimentation with alternative setups

Instance-Based Ontology Matching by Instance Enrichment

The ontology matching (OM) problem is an important barrier to achieve true Semantic Interoperability. Instance-based ontology matching (IBOM) uses the extension of concepts, the instances directly associated with a concept, to determine whether a pair of concepts is related or not. While IBOM has many strengths it requires instances that are associated with concepts of both ontologies, (i.e) dually annotated instances. In practice, however, instances are often associated with concepts of a single ontology only, rendering IBOM rarely applicable. In this paper we discuss a method that enables IBOM to be used on two disjoint datasets, thus making it far more generically applicable. This is achieved by enriching instances of each dataset with the conceptual annotations of the most similar instances from the other dataset, creating artificially dually annotated instances. We call this technique instance-based ontology matching by instance enrichment (IBOMbIE). We have applied the IBOMbIE algorithm in a real-life use-case where large datasets are used to match the ontologies of European libraries. Existing gold standards and dually annotated instances are used to test the impact and significance of several design choices of the IBOMbIE algorithm. Finally, we compare the IBOMbIE algorithm to other ontology matching algorithms

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