Skip to main content
SpringerLink
Log in

Abductive Reasoning and Similarity: Some Computational Tools

  • Chapter
  • First Online:
Computer-Based Diagnostics and Systematic Analysis of Knowledge

Abstract

Abductive reasoning includes discovering new hypotheses or explanations. This chapter identifies several factors involved in abductive reasoning in an effort to move toward a theory of such reasoning. The chapter has an ultimate focus on the nature and influence of similarity. A major goal of our work is to develop computational tools that provide intelligent abductive suggestions to people engaged in discovering new knowledge. Novel abductive inferences often exhibit interesting similarities to the phenomena under investigation. Such similarities are not strong or direct but rather are often only obvious once the inference has been drawn. Some of our research is directed at discovering indirect similarities from text by using measures that are sensitive to indirect relations between terms in the text. By focusing on terms that are related but do not co-occur, potentially interesting indirect relations can be explored. Our work employs Random Indexing methods and Pathfinder networks to identify and display relations among terms in a text corpus. These displays are provided to individuals to suggest possible abductive inferences. We explore a variety of methods for identifying indirect similarities. The degree to which surprising and interesting inferences are suggested is the primary measure of success. Several examples are presented to illustrate the method: An analysis showing a positive relationship between (a) the strength of indirect similarity in one period of time and (b) the likelihood that the terms involved become directly related in future time This correlation supports the hypothesis that discoveries may be latent in such indirect similarities. Presumably, noticing such similarity brings indirectly related concepts together suggesting a new idea.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The PFNETs presented here were all computed with parameters, r = ∞ and q = n – 1, where n is the number of nodes in the network. The links preserved with these parameters consist of the union of the links in all minimum spanning trees or, in terms of similarities, the union of the links in all maximum spanning trees. The sum of the similarities associated with the links in such trees is the maximum over all possible spanning trees. See “Pathfinder Networks” in Wikipedia for additional information.

References

  • Aliseda, A. (2000). Abduction as epistemic change: A Peircean model in artificial intelligence. In P. Flach & A. Kakas (Eds.), Abduction and induction: Essays on their relation and integration. Boston, MA: Kluwer Academic Publishers.

    Google Scholar 

  • Bruza, P., Cole, R., Song, D., & Bari, Z. (2006). Towards operational abduction from a cognitive perspective. In L. Magnani (Ed.), Abduction and creative inferences in science. Logic journal of the IGPL. Oxford: Oxford University Press.

    Google Scholar 

  • Burgess, C., Livesay, K., & Lund, K. (1998). Explorations in context space: Words, sentences, discourse. Discourse Processes, 25(2–3), 211–257.

    Article  Google Scholar 

  • Bylander, T., Allemang, D., Tanner, M. C., & Josephson, J. R. (1991). The computational complexity of abduction. Artificial Intelligence, 49, 25–60.

    Article  Google Scholar 

  • Carpenter, G. A., & Grossberg, S. (2003). Adaptive resonance theory. In M. A. Arbib (Ed.), The Handbook of brain theory and neural networks (2nd ed., pp. 87–90). Cambridge, MA: MIT Press.

    Google Scholar 

  • Charniak, E., & Shimony, S. E. (1990). Probabilistic semantics for cost based abduction. In Proceedings of the National Conference on Artificial Intelligence (pp. 106–111). Boston.

    Google Scholar 

  • Cohen, T. (2008). Exploring MEDLINE space with random indexing and Pathfinder networks. American Medical Informatics Association Symposium, Washington, DC.

    Google Scholar 

  • Cohen, T., Schvaneveldt, R. W., & Rindflesch, T. C. (2009). Predication-based semantic indexing: Permutations as a means to encode predications in semantic space. American Medical Informatics Association Symposium, San Francisco.

    Google Scholar 

  • Cohen, T., Schvaneveldt, R., & Widdows, D. (2009). Reflective random indexing and indirect inference: A scalable method for discovery of implicit connections. Journal of Biomedical Informatics, Sep. 15, [Epub ahead of print]

    Google Scholar 

  • Coombs, M., & Hartley, R. (1987). The MGR algorithm and its application to the generation of explanations for novel events. International Journal of Man-Machine Studies, 20, 21–44.

    Article  Google Scholar 

  • Coombs, M. J., Pfeiffer, H. D., & Hartley, R. T. (1992). e-MGR: An architecture for symbolic plasticity. International Journal of Man-Machine Studies, 36, 247–263.

    Article  Google Scholar 

  • Dayton, T., Durso, F. T., & Shepard, J. D. (1990). A measure of the knowledge organization underlying insight. In R. Schvaneveldt (Ed.), Pathfinder associative networks: Studies in knowledge organization. Norwood, NJ: Ablex.

    Google Scholar 

  • Eco, U. (1998). Hooves, horns, insteps: Some hypotheses on three types of abduction. In U. Eco & T. A. Sebeok (Eds.), The sign of three. Bloomington, IN: Indiana University Press.

    Google Scholar 

  • Fann, K. T. (1970). Peirce’s theory of abduction. The Hague: Martinus Nijhoff.

    Google Scholar 

  • Flach, P. A., & Kakas, A. C. (2000). Abduction and induction: Essays on their relation and integration. Boston, MA: Kluwer Academic Publishers.

    Google Scholar 

  • Gärdenfors, P. (2000). Conceptual spaces: The geometry of thought. Cambridge, MA: MIT Press.

    Google Scholar 

  • Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7, 155–170.

    Article  Google Scholar 

  • Gentner, D., Holyoak, K. J., & Kokinov, B. N. (Eds.). (2001). The analogical mind: Perspectives from cognitive science. Cambridge, MA: MIT Press.

    Google Scholar 

  • Gentner, D., & Markman, A. B. (1997). Structure mapping in analogy and similarity. American Psychologist, 52, 45–56.

    Article  Google Scholar 

  • Giles, J. T, Wo, L., & Berry, M. W. (2003). GTP (General Text Parser) Software for Text Mining. In H. Bozdogan (Ed.), Statistical data mining and knowledge discovery (pp. 455–471). Boca Raton: CRC Press.

    Google Scholar 

  • Gonzalez, G., Uribe, J. C., Tari, L., Brophy, C., & Baral, C. (2007). Mining gene-disease relationships from biomedical literature: Incorporating interactions, connectivity, confidence, and context Measures. Proceedings of the Pacific Symposium in Biocomputing. Maui, Hawaii.

    Google Scholar 

  • Harman, G. H. (1965). The inference to the best explanation. The Philosophical Review, 74, 88–95.

    Article  Google Scholar 

  • Holland, J. H. (1992). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. Cambridge, MA: MIT Press.

    Google Scholar 

  • Holland, J. H. (1995). Hidden order: How adaptation builds complexity. New York: Addison Wesley.

    Google Scholar 

  • Holland, J. H. (1998). Emergence: From chaos to order. Cambridge, MA: Perseus.

    Google Scholar 

  • Holyoak, K. J., & Thagard, P. (1995). Mental leaps. Cambridge, MA: MIT Press.

    Google Scholar 

  • Josephson, J. R., & Josephson, S. G. (Eds.). (1994). Abductive inference: Computation, philosophy, technology. New York: Cambridge University Press.

    Google Scholar 

  • Juarrero, A. (1999). Dynamics in action: Intentional behavior as a complex system. Cambridge, MA: MIT Press.

    Google Scholar 

  • Kakas, A. C., Kowalski, R. A., & Toni, F. (1998). The role of abduction in logic programming. In D. M. Gabbay, C. J. Hogger, & J. A. Robinson (Eds.), Handbook of logic in artificial intelligence and logic programming: Vol. 5, Logic programming. Oxford: Clarendon Press.

    Google Scholar 

  • Kanerva, P., Kristofersson, J., & Holst, A. (2000). Random indexing of text samples for latent semantic analysis. In L. R. Gleitman & A. K. Josh (Eds.), Proceedings of the 22nd Annual Conference of the Cognitive Science Society (p. 1036). Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Karlgren, J., & Sahlgren, M. (2001). From words to understanding. In Y. Uesaka, P. Kanerva & H. Asoh (Eds.), Foundations of real-world intelligence (pp. 294–308). Stanford, CA: CSLI Publications.

    Google Scholar 

  • Kauffmann, S. (2000). Investigations. Oxford: Oxford University Press.

    Google Scholar 

  • Koestler, A. (1990). The act of creation. New York: Penguin.

    Google Scholar 

  • Kolodner, J. (1993). Case-Based Reasoning. San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  • Konolige, K. (1996). Abductive theories in artificial intelligence. In B. Brewka (Ed.), Principles of knowledge representation. Stanford, CA: CSLI Publications.

    Google Scholar 

  • Kruskal, J. B. (1964a). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29, 1–27.

    Article  Google Scholar 

  • Kruskal, J. B. (1964b). Nonmetric multidimensional scaling: A numerical method. Psychometrika, 29, 115–129.

    Article  Google Scholar 

  • Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The Latent Semantic Analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104(2), 211–240.

    Article  Google Scholar 

  • Landauer, T. K, Foltz, P. W., & Laham, D. (1998). An introduction to Latent Semantic Analysis. Discourse Processes, 25(2–3), 259–284.

    Article  Google Scholar 

  • Levesque, H. J. (1989). A knowledge-level account of abduction. In Proceedings of the International Conference on Artificial Intelligence. Detroit, MI.

    Google Scholar 

  • Lund, K., & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instruments & Computers, 28(2), 203–208.

    Google Scholar 

  • McDonald, J. E., Plate, T. A., & Schvaneveldt, R. W. (1990). Using Pathfinder to extract semantic information from text. In R. Schvaneveldt (Ed.), Pathfinder associative networks: Studies in knowledge organization (pp. 149–164). Norwood, NJ: Ablex.

    Google Scholar 

  • Peirce, C. S. (1940a). Abduction and induction. In J. Buchler (Ed.), Philosophical writings of Peirce. New York: Routledge.

    Google Scholar 

  • Peirce, C. S. (1940b). Logic as semiotic: The theory of signs. In J. Buchler (Ed.), Philosophical writings of Peirce. New York: Routledge.

    Google Scholar 

  • Peng, Y., & Reggia, J. A. (1990). Abductive inference models for diagnostic problem-solving. New York: Springer-Verlag.

    Google Scholar 

  • Poole, D. (2000). Abducing through negation as failure: Stable models within the independent choice logic. The Journal of Logic Programming, 44, 5–35.

    Article  Google Scholar 

  • Popper, K. (1962). Conjectures and refutations. London: Routledge.

    Google Scholar 

  • Prendinger, H., & Ishizuka, M. (2005). A creative abduction approach to scientific and knowledge discovery. Knowledge-Based Systems, 18(7), 321–326.

    Article  Google Scholar 

  • Prusiner, S. B. (1982). Novel proteinaceous infectious particles cause scrapie. Science, 216(4542), 136–144.

    Article  Google Scholar 

  • Prusiner, S. B., Cochran, S. P., & Alpers, M. P. (1985). Transmission of scrapie in hamsters. The Journal of Infectious Diseases, 152(5), 971–978.

    Google Scholar 

  • Reggia, J. A., & Peng, Y. (1993). A connectionist approach to diagnostic problem solving using causal networks. Information Sciences, 70, 27–48.

    Article  Google Scholar 

  • Santos, E., Jr., & Santos, E. S. (1996). Polynomial solvability of cost-based abduction. Artificial Intelligence, 86, 157–170.

    Article  Google Scholar 

  • Schvaneveldt, R. W. (Ed.). (1990). Pathfinder associative networks: Studies in knowledge organization. Norwood, NJ: Ablex.

    Google Scholar 

  • Schvaneveldt, R. W., Dearholt, D. W., & Durso, F. T. (1988). Graph theoretic foundations of Pathfinder networks. Computers and Mathematics with Applications, 15, 337–345.

    Article  Google Scholar 

  • Schvaneveldt, R. W., Durso, F. T., & Dearholt, D. W. (1989). Network structures in proximity data. In G. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 24, pp. 249–284). New York: Academic Press.

    Google Scholar 

  • Senglaub, M., Harris, D., & Raybourn, E. M. (2001). Foundations for reasoning in cognition-based computational representations of human decision making (Tech. Rep. SAND2001-3496). Albuquerque: Sandia National Laboratories.

    Google Scholar 

  • Shepard, R. N. (1962a). The analysis of proximities: Multidimensional scaling with unknown distance function Part I. Psychometrika, 27, 125–140.

    Article  Google Scholar 

  • Shepard, R. N. (1962b). The analysis of proximities: Multidimensional scaling with unknown distance function Part II. Psychometrika, 27, 219–246.

    Article  Google Scholar 

  • Shrager, J., & Langley, P. (Eds.). (1990). Computational models of scientific discovery and theory formation. Palo Aalto, CA: Morgan Kaufmann.

    Google Scholar 

  • Simon, H. A. (1947). Administrative Behavior. New York: The Free Press.

    Google Scholar 

  • Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106, 467–482.

    Google Scholar 

  • Simon, H. A. (1981). The sciences of the artificial (2nd Ed.). Cambridge, MA: MIT Press.

    Google Scholar 

  • Swanson, D. R. (1986). Fish oil, Raynaud’s Syndrome, and undiscovered public knowledge. Perspectives in Biology and Medicine, 30(1), 7–18.

    Google Scholar 

  • Swanson, D. R. (1987). Two medical literatures that are logically but not bibliographically related. Journal of the American Society for Information Science, 38, 228–233.

    Article  Google Scholar 

  • Thagard, P., & Verbeurgt, K. (1998). Coherence as constraint satisfaction. Cognitive Science, 22, 1–24.

    Article  Google Scholar 

  • Walton, D. N. (2004). Abductive reasoning. Tuscaloosa, Alabama: University of Alabama Press.

    Google Scholar 

  • Widdows, D. (2004). Geometry and meaning. Stanford, CA: CSLI Publications.

    Google Scholar 

  • Widdows, D., & Ferraro, K. (2008). Semantic vectors: A scalable open source package and online technology management application. Sixth International Conference on Language Resources and Evaluation (LREC 2008).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Arizona State University, Mesa, AZ, USA

    Roger W. Schvaneveldt

  2. University of Texas, Austin, TX, USA

    Trevor A. Cohen

Authors
  1. Roger W. Schvaneveldt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Trevor A. Cohen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roger W. Schvaneveldt .

Editor information

Editors and Affiliations

  1. Abt. Lernforschung, Universität Freiburg, Rempartstr. 11, Freiburg, 79085, Germany

    Dirk Ifenthaler

  2. Abt. Lernforschung, Universität Freiburg, Rempartstr. 11, Freiburg, 79085, Germany

    Pablo Pirnay-Dummer

  3. Inst. Erziehungswissenschaft, Univ. Freiburg, Freiburg, 79085, Germany

    Norbert M. Seel

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Schvaneveldt, R.W., Cohen, T.A. (2010). Abductive Reasoning and Similarity: Some Computational Tools. In: Ifenthaler, D., Pirnay-Dummer, P., Seel, N. (eds) Computer-Based Diagnostics and Systematic Analysis of Knowledge. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-5662-0_11

Download citation

Publish with us

Policies and ethics

Search

Navigation