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 behaviour of these techniques when used in large scale experiments, then making feasible the proposed approach.

the biosciences

Ontology researches have been carried out in many diverse research areas in the past decade for numerous purposes especially in the eRecruitment domain. In this article, we would like to take a closer look on the current work of such domain of ontologies such as eRecruitment. Ontology application for e-Recruitment is becoming an important task for matching job postings and applicants semantically in a Semantic web technology using ontology and ontology matching techniques. Most of the reviewed papers used currently (existing) available widespread standards and classifications to build human resource ontology that provide a way of semantic representation for positions offered and candidates to fulfil, some of other researches have been done created their own HR ontologies to build recruitment prototype. We have reviewed number of articles and identified few purposes for which ontology matching techniques are applied so far and how these matching techniques used in that respective domain.