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Large scale integration of senses for the semantic web

Large scale integration of senses for the semantic web,10.1145/1526709.1526792,Jorge Gracia,Mathieu D'aquin,Eduardo Mena

Large scale integration of senses for the semantic web   (Citations: 2)
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Nowadays, the increasing amount of semantic data available on the Web leads to a new stage in the potential of Seman- tic 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 ex- cess of dierent semantic descriptions, coming from dierent 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 dramati- cally reduce the redundancy problem on the current Seman- tic 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 sce- nario of the Semantic Web. Our evaluation shows a good behaviour of these techniques when used in large scale ex- periments, then making feasible the proposed approach.
Conference: World Wide Web Conference Series - WWW , pp. 611-620, 2009
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    • ...For architecture, our proposed framework is similar to the work in [9], which dedicated a large-scale clustering to ontology terms...
    • ...Both of us adopted a bootstrapping running mode, but [9] merely executed the extension one time...
    • ...Furthermore, [9] performed extension only based on labels and local names, while our self-training framework is more adaptive for various domains...

    Wei Huet al. A self-training approach for resolving object coreference on the seman...

    • ...Technically speaking, the architecture of our approach is similar to [24]...
    • ...The study in [24] dedicated a large-scale clustering to ontology terms...
    • ...There are two difierences between our approach and [24]: 1) we adopt OWL built-in vocabulary elements to build the kernel, which have standard semantics and usually are trustworthy, while [24] depended on thesauri, which may be imprecise in some cases; and 2) we propose ranking methods for the coreferent URIs, while [24] used a uniform threshold to fllter wrong URIs, which is hard to decide across difierent domains...
    • ...There are two difierences between our approach and [24]: 1) we adopt OWL built-in vocabulary elements to build the kernel, which have standard semantics and usually are trustworthy, while [24] depended on thesauri, which may be imprecise in some cases; and 2) we propose ranking methods for the coreferent URIs, while [24] used a uniform threshold to fllter wrong URIs, which is hard to decide across difierent domains...
    • ...There are two difierences between our approach and [24]: 1) we adopt OWL built-in vocabulary elements to build the kernel, which have standard semantics and usually are trustworthy, while [24] depended on thesauri, which may be imprecise in some cases; and 2) we propose ranking methods for the coreferent URIs, while [24] used a uniform threshold to fllter wrong URIs, which is hard to decide across difierent domains...

    Wei Huet al. Bootstrapping Object Coreferencing on the Semantic Web

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