An Ontology-based Tool for Dynamic Generation, Classification and Recommendation of Novel Contents in Online Libraries Chiara Barbera 1,† , Antonio Lieto 1,† and Gian Luca Pozzato 1,∗,† 1 Dipartimento di Informatica, Università di Torino, c.so Svizzera 185, 10149 Turin, Italy Abstract In this work we present AMARETTO (dynAMic generAtoR of novEl conTenT in bOoks), an intelligent recommender system exploiting a nonmonotonic extension of Description Logics with typical properties and probabilities to dynamically generate novel contents in Goodreads, the largest website for readers and book recommendations (https://www.goodreads.com). The tool AMARETTO can be used to both the generation/suggestion of novel genres of books and the reclassifcation of the available items within such new genres. AMARETTO frst extracts a prototypical description of the available genres by means of a standard information extraction pipeline, then it generates novel classes of genres as the result of an ontology-based combination of such extracted representations, by exploiting the reasoning capabilities of a probabilistic extension of a Description Logic of typicality. We have tested AMARETTO by reclassifying the available books in Goodreads with respect to the new generated genres, as well as with an evaluation, in the form of a controlled user study experiment, of the feasibility of using the obtained reclassifcations as recommended contents. The obtained results are encouraging and pave the way to many possible further improvements and research directions. Keywords Cognitive Systems, Recommender System, Knowledge Invention, Description Logics, nonmonotonic reasoning, probabilities, reasoning about typicality 1. Introduction Dynamic generation of novel knowledge via conceptual recombination is a relevant phe- nomenon. It highlights some crucial aspects of the knowledge processing capabilities in human cognition. Indeed, such ability concerns high-level capacities associated to creative thinking and problem solving. The recent literature suggests the relevance of this topic [1, 2, 3], however, it still represents an open challenge in the feld of artifcial intelligence [4]. Indeed, dealing with this problem requires, from an AI perspective, the harmonization of two conficting re- quirements: the need of a syntactic and semantic compositionality – typical of logical systems 1st Italian Workshop on Artifcial Intelligence for Cultural Heritage (AI4CH22), co-located with the 21st International Conference of the Italian Association for Artifcial Intelligence (AIxIA 2022). 28 November 2022, Udine, Italy. ∗ Corresponding author. † These authors contributed equally. E chiara.barbera@edu.unito.it (C. Barbera); antonio.lieto@unito.it (A. Lieto); gianluca.pozzato@unito.it (G. L. Pozzato) G https://www.antoniolieto.net (A. Lieto); http://www.di.unito.it/~pozzato/ (G. L. Pozzato) O 0000-0002-8323-8764 (A. Lieto); 0000-0002-3952-4624 (G. L. Pozzato) © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings http://ceur-ws.org ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)