Journal of Intelligent Information Systems

, Volume 50, Issue 1, pp 1–28 | Cite as

A multi-strategy approach to structural analogy making

  • Fabio LeuzziEmail author
  • Stefano Ferilli


Analogy is the cognitive process of matching the characterizing features of two different items. This may enable reuse of knowledge across domains, which can help to solve problems. Indeed, abstracting the ‘role’ of the features away from their specific embodiment in the single items is fundamental to recognize the possibility of an analogical mapping between them. The analogical reasoning process consists of five steps: retrieval, mapping, evaluation, abstraction and re-representation. This paper proposes two forms of an operator that includes all these elements, providing more power and flexibility than existing systems. In particular, the Roles Mapper leverages the presence of identical descriptors in the two domains, while the Roles Argumentation-based Mapper removes also this limitation. For generality and compliance with other reasoning operators in a multi-strategy inference setting, they exploit a simple formalism based on First-Order Logic and do not require any background knowledge or meta-knowledge. Applied to the most critical classical examples in the literature, they proved to be able to find insightful analogies.


Analogy Argumentation Multi-strategy First-order logic 


  1. de los Angeles, C.M., & Forbus, K.D. (2012). Using quantitative information to improve analogical matching between sketches.Google Scholar
  2. Baydin, A.G., de Mántaras, R.L., & Ontañón, S. (2012). Automated generation of cross-domain analogies via evolutionary computation. CoRR abs/1204.2335.Google Scholar
  3. Doumas, L.A.A., Hummel, J.E., & Sandhofer, C.M. (2008). A theory of the discovery and predication of relational concepts. Psychological Review, 115(1), 1–43.CrossRefGoogle Scholar
  4. Dung, P.M. (1995). On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence, 77, 321–357.MathSciNetCrossRefzbMATHGoogle Scholar
  5. Egly, U., Gaggl, S.A., & Woltran, S. (2010). Answer-set programming encodings for argumentation frameworks. Argument &, Computation, 1(2), 147–177.CrossRefzbMATHGoogle Scholar
  6. Falkenhainer, B., Forbus, K.D., & Gentner, D. (1989). The structure-mapping engine: Algorithm and examples. Artificial Intelligence, 41, 1–63.CrossRefzbMATHGoogle Scholar
  7. Ferilli, S., Basile, T., Biba, M., Mauro, N.D., & Esposito, F. (2009). A general similarity framework for horn clause logic. Fundamenta Informaticae, 90(1–2), 43–66.MathSciNetzbMATHGoogle Scholar
  8. Gentner, D. (1983). Structure-mapping: a theoretical framework for analogy. Cognitive Science, 7(2), 155–170.CrossRefGoogle Scholar
  9. Gentner, D. (1998). Analogy. A companion to cognitive science (pp. 107–113).Google Scholar
  10. Gentner, D., & Markman, A.B. (1997). Structure mapping in analogy and similarity. American psychologist, 52, 45–56.CrossRefGoogle Scholar
  11. Gick, M.L., & Holyoak, K.J. (1980). Analogical problem solving. Cognitive Psychology, 12(3), 306– 355.CrossRefGoogle Scholar
  12. Giordana, A., Saitta, L., & Roverso, D. (1991). Abstracting concepts with inverse resolution. In L. Birnbaum & G. Collins (Eds.) ML, Morgan Kaufmann (pp. 142–146).Google Scholar
  13. Halford, G.S., Wilson, W.H., Guo, J., Gayler, R.W., Wiles, J., & Stewart, J. (1994). Connectionist implications for processing capacity limitations in analogies. Advances in Connectionist and Neural Computation Theory, 2(1-2), 363–415.Google Scholar
  14. Hofstadter, D.R., & Mitchell, M. (1994). The copycat project: A model of mental fluidity and analogymaking. In Advances in connectionist and neural computation theory. Norwood, N.J.: Ablex Publishing Corporation.Google Scholar
  15. Holyoak, K.J., & Hummel, J.E. Dedre Gentner, K.J.H., & Konikov, B.N. (Eds.) (2001). Understanding analogy within a biological symbol system. Cambridge, MA: The MIT Press.Google Scholar
  16. Holyoak, K.J., & Thagard, P. (1989). Analogical mapping by constraint satisfaction. Cognitive Science, 13, 295–355.CrossRefGoogle Scholar
  17. Leuzzi, F., & Ferilli, S. (2013). Reasoning by analogy using past experiences. In Proceedings of the 28th italian conference on computational logic (CILC 2013), (Vol. 1068, pp. 115–129).Google Scholar
  18. Leuzzi, F., & Ferilli, S. (2016). New Frontiers in Mining Complex Patterns: 4th International Workshop, NFMCP 2015, Held in Conjunction with ECML-PKDD 2015, Porto, Portugal, September 7, 2015, Revised Selected Papers, Springer International Publishing, Cham, chap Generalizing Patterns for Cross-Domain Analogy, pp. 147–162.Google Scholar
  19. Lloyd, J.W. (1987). Foundations of logic programming, 2nd Edn. Springer.Google Scholar
  20. Lu, H., Chen, D., & Holyoak, K.J. (2012). Bayesian analogy with relational tansformations. Psychological Review, 119(3), 617–648.CrossRefGoogle Scholar
  21. Michalski, R.S. (1993). Inferential theory of learning: Developing foundations for multistrategy learning. In Machine learning: a multi-strategy approach (Vol. 4, pp. 3–62). Morgan Kaufmann Publishers.Google Scholar
  22. O’Donoghue, D., & Keane, M.T. (2012). A creative analogy machine: Results and challenges. In Proceedings of the 3rd international conference on computational creativity (pp. 17–24). Dublin, Ireland.Google Scholar
  23. Rotella, F., Leuzzi, F., & Ferilli, S. (2015). Learning and exploiting concept networks with connektion. Applied Intelligence, 42(1), 87–111.CrossRefGoogle Scholar
  24. Schwering, A., Krumnack, U., Kühnberger, K.U., & Gust, H. (2009). Syntactic principles of heuristic-driven theory projection. Cognitive Systems Research, 10(3), 251–269.CrossRefGoogle Scholar
  25. Turney, P.D. (2005). Measuring semantic similarity by latent relational analysis. CoRR abs/cs/0508053.Google Scholar
  26. Turney, P.D. (2008). A uniform approach to analogies, synonyms, antonyms, and associations. CoRR abs/0809.0124.Google Scholar
  27. Veloso, M.M., & Carbonell, J.G. (1993). Derivational analogy in PRODIGY: Automating case acquisition, storage, and utilization (pp. 55–84). US, Boston, MA: Springer.Google Scholar
  28. Wilson, W.H., Halford, G.S., Gray, B., & Phillips, S. (2001). The star-2 model for mapping hierarchically structured analogs. The analogical mind (pp. 125–159).Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Dipartimento di InformaticaUniversità di BariBariItaly
  2. 2.Centro Interdipartimentale per la Logica e sue ApplicazioniUniversità di BariBariItaly

Personalised recommendations