Collective Intelligence for Semantic and Knowledge Grid.
Jason J. Jung, Ngoc Thanh Nguyên · Zenodo (CERN European Organization for Nuclear Research) · 2008
Recently, grid computing has been regarded as the most promising paradigm to interconnect heterogeneous computing environments.Main goal of this grid computing paradigm is to share local but limited resources with others to solve very complex problems [Foster 2003].Especially, semantics and knowledge are playing an important role of building an efficient grid platform to share information and knowledge with each other [de Roure et al. 2005].A variety of domains, e.g., business [Zhen and Jiang 2008, Jung 2008], chemistry [Taylor et al. 2006], information retrieval [Jung 2007], and biomedical areas [Tsiknakis et al. 2008], have been attempting to employ this semantic grid platform.However, there are several hurdles that they have to overcome in common, e.g., semantic heterogeneity (e.g., inconsistency and conflict) between information sources on a grid.In order to efficiently deal with the hurdles and implement the semantic grid platform, there have been representative approaches, e.g., web services (S-OGSA [Corcho et al. 2006]), metadata, ontologies and reasoning.More particularly, collective intelligence is the latest buzzword to take into account how to find any opportunities to link individual intelligence as well as how to apply the collective intelligence to various problems.In this issue, we are focusing on the semantic and knowledge grid platforms (as well as distributed platforms) for building and exploiting collective intelligence.Main topics of interests are noted, as follows;