Affiliations: [a] Knowledge Media Institute (KMi), The Open University (OU), MK7 6AA, Milton Keynes, United Kingdom | [b] Ontology Engineering Group (OEG), Universidad Politécnica de Madrid (UPM), Campus de Montegancedo sn, 28660, Madrid, Spain | [c] Insight Centre for Data Analytics, NUI Galway (NUIG), IDA business park, Lower Dangan, Galway, Ireland | [d] Department of English and Creative Writing, The Open University (OU), MK7 6AA, Milton Keynes, United Kingdom | [e] 3L.AM, Le Mans Université, Avenue Olivier Messiaen, 72085 Le Mans, France | [f] Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), 263 Avenue Général Leclerc, 35000 Rennes, France | [g] Dipartimento Interateneo di Scienze, Progetto e Politiche del Territorio (DIST), Polytechnic of Turin & Univerisity of Turin, Viale Mattioli, 39, 10125 Torino, Italy
Abstract: Large scale cultural heritage datasets and computational methods for the Humanities research framework are the two pillars of Digital Humanities (DH), a research field aiming to expand Humanities studies beyond specific sources and periods to address macro-scale research questions on broad human phenomena. In this regard, the development of machine-readable semantically enriched data models based on a cross-disciplinary “language” of phenomena is critical for achieving the interoperability of research data. This paper reports on, documents, and discusses the development of a model for the study of reading experiences as part of the EU JPI-CH project Reading Europe Advanced Data Investigation Tool (READ-IT). Through the discussion of the READ-IT ontology of reading experience, this contribution will highlight and address three challenges emerging from the development of a conceptual model for the support of research on cultural heritage. Firstly, this contribution addresses modelling for multi-disciplinary research. Secondly, this work describes the development of an ontology of reading experience, under the light of the experience of previous projects, and of ongoing and future research developments. Lastly, this contribution addresses the validation of a conceptual model in the context of ongoing research, the lack of a consolidated set of theories and of a consensus of domain experts.