document: Large scale integration of senses for the semantic web Nowadays , the increasing amount of semantic data available on the Web leads to a new stage in the potential of Semantic Web applications . However , it also introduces new issues due to the heterogeneity of the available semantic resources . One of the most remarkable is redundancy , that is , the excess of different semantic descriptions , coming from different sources , to describe the same intended meaning . In this paper , we propose a technique to perform a large scale integration of senses ( expressed as ontology terms ) , in order to cluster the most similar ones , when indexing large amounts of online semantic information . It can dramatically reduce the redundancy problem on the current Semantic Web . In order to make this objective feasible , we have studied the adaptability and scalability of our previous work on sense integration , to be translated to the much larger scenario of the Semantic Web . Our evaluation shows a good behavior of these techniques when used in large scale experiments , then making feasible the proposed approach .
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