M. Bajec and J. Eder (Eds.): CAiSE 2012 Workshops, LNBIP 112, pp. 123–129, 2012. © Springer-Verlag Berlin Heidelberg 2012 Modeling the Context of Scientific Information: Mapping VIVO and CERIF Leonardo Lezcano 1 , Brigitte Jörg 2 , and Miguel-Angel Sicilia 1 1 Information Engineering Research Unit, Computer Science Department, University of Alcalá, Polytechnic Building, Ctra. De Barcelona km. 33.6 28871 Alcalá de Henares (Madrid), Spain {leonardo.lezcano,msicilia}@uah.es 2 DFKI GmbH, Language Technology Lab Projektbüro Berlin, Alt-Moabit 91c 10559 Berlin, Germany brigitte.joerg@dfki.de Abstract. Institutional repositories (IR) and Current Research Information Sys- tems (CRIS) among other kinds of systems store and manage information on the context in which research activity takes place. Several models, standards and ontologies have been proposed to date as a solution to give coherent seman- tics to research information. These present a large degree of overlap but also present very different approaches to modeling. This paper presents a contrast of two of the more widespread models, the VIVO ontology and the CERIF stan- dards, and provides directions for mapping them in a way that enables clients to integrate data coming from heterogeneous sources. The majority of mapping problems have risen from the representation of VIVO sub-hierarchies in CERIF as well as from the representation of CERIF attributes in VIVO. Keywords: CERIF, VIVO, CRIS, research information, scientific information, ontologies, knowledge representation, mapping. 1 Introduction Traditionally, most of research has been curiosity-led, discipline-oriented, and moti- vated and executed by a small group of individuals following hypothesis, experiment or proven method. Nevertheless, the complex problems that science is facing nowa- days require large teams with each member having a specialized contribution to the whole. These collaborative teams are often geographically dispersed and belong to different disciplines. Gibbons et al. refer to the fact that science has been shifting from discipline-oriented to cross-disciplinary research as Mode Two [1]. Increased knowledge, the paradigm shift, recognition of economic stimulus and collaborative interdisciplinary science lead inexorably to the need for systems to assist researchers, administrators, strategists, opinion-formers, entrepreneurs and also the general public [2]. Current Research Information Systems (CRIS) are expected to provide such scientific information. In order to support decision-making and