An Ontological Structure for Gathering and Sharing Knowledge among Scientists through Experiment Modeling

An Ontological Structure for Gathering and Sharing Knowledge among Scientists through Experiment Modeling

Luis Casillas (University of Guadalajara, Mexico) and Thanasis Daradoumis (University of the Aegean, Greece & Open University of Catalonia, Spain)
DOI: 10.4018/978-1-4666-0125-3.ch008
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This chapter presents a proposal for modeling / simulating experiments conducted by scientists working in common scientific problems, based on gathering and exploiting knowledge elements produced among them. The authors’ approach enables the adaptation of knowledge structures (bounded to scientific problems) and is based on recurrent refining processes that are fed by indicators, which come from collaboration among the scientists involved. This scheme captures a web-based infrastructure, which allows scientists to collaborate on synthesizing experiments online. The proposed model is approached as an ontology that contains scientific concepts and actions. This ontology is linked to the scientific problem and represents both the “common understanding” for such a problem and the way it could be managed by the group. This dynamic ontology will change its structure according to the collaboration acts among scientists. Frequent collaboration over certain elements of the experiment will make them prevail in time. Besides, this process has been defined in a way that provides a global understanding of the scientific treatment that could be applied on any scientific problem. Hence, the ontology represents a virtualization of the scientific experiment. This whole representation is aimed at providing the media for developing e-research among scientists that are working on common problems.
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Knowledge Grids As Ontologies

It is a fact that knowledge has become an asset for most of the organizations, which are conscious about the resources of awareness and the ways to manage them. Knowledge, by itself, is hard to define and its handling is rather difficult. In such context, any knowledge managing technique acquires some attention from people related to the creation, storing and handling of knowledge. One of the most frequent mechanisms used to manage knowledge is grids (Li & Liu, 2007; Goble, et al., 2005; Zettsu, et al., 2008).

Every concept has its own charge of knowledge, which is a piece of information or even a piece of primitive knowledge. In order to represent a higher level of meaning, concepts can be assembled as nets. Along the process of assembling the nets of knowledge, the semantics bound to the nets becomes complex. Thus the action of assembling is important for meaning, although meaning does not depend on the assembling by itself, it depends on the concepts involved and the kind of relationships established among these concepts. Not every connection of concepts will imply meaningful structures. The connections should be rationally founded in the common understanding of reality.

The human brain is always trying to relate the stimuli, arriving from the environment, to the elements already stored by the previous experiences. The brain's goal is to produce meaning to the current experience. The network of concepts, made during this linking process in the brain, is able to produce meaning. Artificial grids of knowledge could produce the very same support for dealing with synthetic concepts.

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