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We propose a new cluster-based semantic similarity/distance measure for the biomedical domain within the framework of UMLS. The proposed measure is based mainly on the cross-modified path length feature between the concept nodes, and two new features: (1) the common specificity of two concept nodes, and (2) the local granularity of the clusters. We also(More)
—Most of the intelligent knowledge-based applications contain components for measuring semantic similarity between terms. Many of the existing semantic similarity measures that use ontology structure as their primary source cannot measure semantic similarity between terms and concepts using multiple ontologies. This research explores a new way to measure(More)
The semantic similarity techniques are interested in determining how much two concepts, or terms, are similar according to a given ontology. This paper proposes a method for measuring semantic similarity/distance between terms. The measure combines strengths and complements weaknesses of existing measures that use ontology as primary source. The proposed(More)
This paper presents a cross-ontology approach, as an extension of the Cluster-Based approach, to measure semantic distance between concepts within single ontology or between concepts dispersed in multiple ontologies in a unified framework in the biomedical domain. The experimental results (with ~0.81 correlation with human scores) confirmed that the(More)
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