An increased exchange of (scientific) information across organizations and disciplines is one of the long-term goals of the semantic web. 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 exchange, increasing in this way the acceptance of information by scientists. In this paper, we present 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 paper reports on the usefulness of the extracted trust network and related ranking as identified in a user study with subjects who are experts in the field of concern.