An increased exchange of scientific information across organizations and disciplines is one of the long-term goals of cyber-infrastructure (CI) and e-Science initiatives. In any such exchange of information, it is not difficult to identify one or more scientific communities responsible for the measurement, gathering and processing of scientific information. More challenging, however, is to understand the trust relations between members of these communities, whether the members are organizations or people. With a better understanding of trust relations, one may be able to compute trust recommendations for scientific information measured, gathered and processed with the help of members of these communities, increasing in this way the acceptance of information by scientists.
This thesis presents CI-Learner, which is a systematic approach for extracting trust-related meta-information from scientific portals and related web sites. CI-Learner meta-information is organized as trust networks based on people, organizations, publications, and trust relations derived from publication co-authorship. Participation in a given trust network is restricted to organizations and people as identified by the CI-Learner information extraction process. The thesis reports on the usefulness of an extracted trust network for the Earth Science community and the associated rankings of researchers as evaluated in a user study with experts in the field.
