A Dual Process Architecture for Ontology-based Systems

Antonio Lieto, Andrea Minieri, Alberto Piana, Daniele P. Radicioni, Marcello Frixione


In this work we present an ontology-based system equipped with a hybrid, cognitively inspired architecture for the representation of conceptual information. The proposed system aims at extending the representational and reasoning capabilities of classical ontological-based systems towards more realistic and cognitively grounded scenarios, such as those envisioned by the prototype theory. It is based on a hybrid knowledge base, composed of a classical symbolic component (grounded on a formal ontology) with a typicality-based one (grounded on the conceptual spaces framework). The resulting system attempts to reconcile the heterogeneous approach to the concepts in Cognitive Science and the dual process theories of reasoning and rationality. The system has been experimentally assessed in a conceptual categorization task where common sense linguistic descriptions were given in input, and the corresponding target concepts had to be identified. The results show that the proposed solution substantially improves on the representational and reasoning "conceptual" capabilities of standard ontology-based systems.


  1. Adams, B. and Raubal, M. (2009). A metric conceptual space algebra. In Hornsby, K. et al., editors, COSIT, volume 5756 of LNCS, pages 51-68. Springer.
  2. Bejan, A. and Marden, J. H. (2006). Constructing animal locomotion from new thermodynamics theory. American Scientist, 94(4):342.
  3. Brachmann, R. J. and Schmolze, J. G. (1985). An overview of the KL-ONE knowledge representation system. Cognitive Science, 9(2):171-202.
  4. Chella, A., Frixione, M., and Gaglio, S. (1997). A cognitive architecture for artificial vision. Artificial Intelligence, 89(1-2):73 - 111.
  5. Evans, J. S. B. and Frankish, K. E. (2009). In two minds: Dual processes and beyond. Oxford University Press.
  6. Frixione, M. and Lieto, A. (2010). The computational representation of concepts in formal ontologies-some general considerations. In KEOD.
  7. Frixione, M. and Lieto, A. (2012). Representing concepts in formal ontologies: Compositionality vs. typicality effects. Logic and Logical Philosophy, 21(4):391-414.
  8. Frixione, M. and Lieto, A. (2014). Towards an Extended Model of Conceptual Representations in Formal Ontologies: A Typicality-Based Proposal. Journal of Universal Computer Science, 20(3):257-276.
  9. Gärdenfors, P. (2000). Conceptual spaces: The geometry of thought. MIT press.
  10. Getz, W. M. (2011). Biomass transformation webs provide a unified approach to consumer-resource modelling. Ecology letters, 14(2):113-124.
  11. Ghignone, L., Lieto, A., and Radicioni, D. P. (2013). Typicality-Based Inference by Plugging Conceptual Spaces Into Ontologies. In Lieto, A. and Cruciani, M., editors, Proceedings of the International Workshop on Artificial Intelligence and Cognition. CEUR.
  12. Giordano, L., Gliozzi, V., Olivetti, N., and Pozzato, G. L. (2013). A non-monotonic description logic for reasoning about typicality. Artificial Intelligence, 195:165- 202.
  13. Jansen, B. J., Booth, D. L., and Spink, A. (2008). Determining the informational, navigational, and transactional intent of web queries. Information Processing & Management, 44(3):1251-1266.
  14. Johnson-Laird, P. (1980). Mental models in cognitive science. Cognitive Science, 4(1):71-115.
  15. Kahneman, D. (2011). Thinking, fast and slow. Macmillan.
  16. Khemlani, S. and Johnson-Laird, P. (2013). The processes of inference. Argument & Computation, 4(1):4-20.
  17. Lieto, A., Minieri, A., Piana, A., and Radicioni, D. P. (In press, 2014). A knowledge-based system for prototypical reasoning. Connection Science.
  18. Machery, E. (2009). Doing without concepts. OUP.
  19. Minsky, M. (1975). A framework for representing knowledge. In Winston, P., editor, The Psychology of Computer Vision, pages 211-277. McGraw-Hill, New York.
  20. Nardi, D. and Brachman, R. J. (2003). An introduction to description logics. In Description logic handbook, pages 1-40.
  21. Pilato, G., Augello, A., and Gaglio, S. (2012). A modular system oriented to the design of versatile knowledge bases for chatbots. ISRN Artificial Intelligence, 2012.
  22. Rosch, E. (1975). Cognitive representations of semantic categories. J. Exp. Psychol. Gen., 104(3):192-233.
  23. Straccia, U. (2011). Reasoning within fuzzy description logics. arXiv preprint arXiv:1106.0667.

Paper Citation

in Harvard Style

Lieto A., Minieri A., Piana A., P. Radicioni D. and Frixione M. (2014). A Dual Process Architecture for Ontology-based Systems . In Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2014) ISBN 978-989-758-049-9, pages 48-55. DOI: 10.5220/0005070800480055

in Bibtex Style

author={Antonio Lieto and Andrea Minieri and Alberto Piana and Daniele P. Radicioni and Marcello Frixione},
title={A Dual Process Architecture for Ontology-based Systems},
booktitle={Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2014)},

in EndNote Style

JO - Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2014)
TI - A Dual Process Architecture for Ontology-based Systems
SN - 978-989-758-049-9
AU - Lieto A.
AU - Minieri A.
AU - Piana A.
AU - P. Radicioni D.
AU - Frixione M.
PY - 2014
SP - 48
EP - 55
DO - 10.5220/0005070800480055