A purely logic-based approach to approximate matching of Semantic Web Services

Most current approaches to matchmaking of semantic Web services utilize hybrid strategies consisting of logic- and non-logic-based similarity measures (or even no logic-based similarity at all). This is mainly due to pure logic-based matchers achieving a good precision, but very low recall values. We present a purely logic-based matcher implementation based on approximate subsumption and extend this approach to take additional information about the taxonomy of the background ontology into account. Our aim is to provide a purely logic-based matchmaker implementation, which also achieves reasonable recall levels without large impact on precision

Approximate Assertional Reasoning Over Expressive Ontologies

In this thesis, approximate reasoning methods for scalable assertional reasoning are provided whose computational properties can be established in a well-understood way, namely in terms of soundness and completeness, and whose quality can be analyzed in terms of statistical measurements, namely recall and precision. The basic idea of these approximate reasoning methods is to speed up reasoning by trading off the quality of reasoning results against increased speed