X-som: A flexible ontology mapper

System interoperability is a well known issue, especially for heterogeneous information systems, where ontologybased representations may support automatic and usertransparent integration. In this paper we present X-SOM: an ontology mapping and integration tool. The contribution of our tool is a modular and extensible architecture that automatically combines several matching techniques by means of a neural network, performing also ontology debugging to avoid inconsistencies. Besides describing the tool components, we discuss the prototype implementation, which has been tested against the OAEI 2006 benchmark with promising results.

Handling instance coreferencing in the KnoFuss architecture

Finding RDF individuals that refer to the same real-world entities but have different URIs is necessary for the efficient use of data across sources. The requirements for such instance-level integration of RDF data are different from both database record linkage and ontology schema matching scenarios. Flexible configuration and reuse of different methods is needed to achieve good performance. Our data integration architecture, called KnoFuss, implements a component-based approach, which allows flexible selection and tuning of methods and takes the ontological schemata into account to improve the reusability of methods

A NEW SIMILARITY MEASURE FOR TAXONOMY BASED ON EDGE COUNTING

ABSTRAC

Ontology matching: state of the art and future challenges

shvaiko2013aInternational audienceAfter years of research on ontology matching, it is reasonable to consider several questions: is the field of ontology matching still making progress? Is this progress significant enough to pursue some further research? If so, what are the particularly promising directions? To answer these questions, we review the state of the art of ontology matching and analyze the results of recent ontology matching evaluations. These results show a measurable improvement in the field, the speed of which is albeit slowing down. We conjecture that significant improvements can be obtained only by addressing important challenges for ontology matching. We present such challenges with insights on how to approach them, thereby aiming to direct research into the most promising tracks and to facilitate the progress of the field

State of the Art Automatic Ontology Matching

Abstrac