Matches in ScholarlyData for { ?s ?p The widespread use of ontologies raises the need to resolve heterogeneities betweendistinct conceptualisations in order to support interoperability. The aim of ontology mapping is,to establish formal relations between a set of knowledge entities which represent the same or asimilar meaning in distinct ontologies. Whereas the symbolic approach of established SWrepresentation standards – based on first-order logic and syllogistic reasoning – does notimplicitly represent similarity relationships, the ontology mapping task strongly relies onidentifying semantic similarities. However, while concept representations across distinctontologies hardly equal another, manually or even semi-automatically identifying similarityrelationships is costly. Conceptual Spaces (CS) enable the representation of concepts as vectorspaces which implicitly carry similarity information. But CS provide neither an implicitrepresentational mechanism nor a means to represent arbitrary relations between concepts orinstances. In order to overcome these issues, we propose a hybrid knowledge representationapproach which extends first-order logic ontologies with a conceptual grounding through a setof CS-based representations. Consequently, semantic similarity between instances –represented as members in CS – is indicated by means of distance metrics. Hence, automaticsimilarity-detection between instances across distinct ontologies is supported in order tofacilitate ontology mapping.. }
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- 28 abstract "The widespread use of ontologies raises the need to resolve heterogeneities betweendistinct conceptualisations in order to support interoperability. The aim of ontology mapping is,to establish formal relations between a set of knowledge entities which represent the same or asimilar meaning in distinct ontologies. Whereas the symbolic approach of established SWrepresentation standards – based on first-order logic and syllogistic reasoning – does notimplicitly represent similarity relationships, the ontology mapping task strongly relies onidentifying semantic similarities. However, while concept representations across distinctontologies hardly equal another, manually or even semi-automatically identifying similarityrelationships is costly. Conceptual Spaces (CS) enable the representation of concepts as vectorspaces which implicitly carry similarity information. But CS provide neither an implicitrepresentational mechanism nor a means to represent arbitrary relations between concepts orinstances. In order to overcome these issues, we propose a hybrid knowledge representationapproach which extends first-order logic ontologies with a conceptual grounding through a setof CS-based representations. Consequently, semantic similarity between instances –represented as members in CS – is indicated by means of distance metrics. Hence, automaticsimilarity-detection between instances across distinct ontologies is supported in order tofacilitate ontology mapping.".