Abstract
The ultimate goal of Information Retrieval (IR) is to retrieve all and only the relevant documents for a user’s information need. Consequently a good IR model is the one which gives each document a relevance estimation as close as possible to the user’s own relevance judgement. The crucial problem in IR modelling is to correctly capture the notion of relevance within a computational model.
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References
Barry, C. L. (1994). User-defined relevance criteria: an exploratory study. Journal of the American Society for Information Science, 45(3): 149–159.
Belkin, N. J. (1984). Cognitive models and information transfer. Social Science Information Studies, 4:111–129.
Chiaramella, Y. and Nie, J.-Y. (1989). A retrieval model based on an extended modal logic and its application to the rime experimental approach. In Research and Development on Information Retrieval — ACM-SIGIR Conference, pages 25–43, Brussel.
Cooper, W. S. (1971). A definition of relevance for information retrieval. Information Storage and Retrieval, 7:19–37.
Crestani, F. and van Rijsbergen, C. J. (1995). Information retrieval by logical imaging. Journal of Documentation, 51(1):3–17.
Cuadra, C. A. and Katter, R. V. (1967). Opening the black box of relevance. Journal of Documentation, 23:291–303.
d. Swart, H. C. M. (1983). A gentzen-or beth-type system, a practical decision procedure and a constructive completeness proof for the counterfactual logics vc and vcs. The Journal of Symbolic Logic, 48:1–20.
Dervin, B. and Nilan, M. ( 1986). Information needs and uses. Annual Review of Science and Technology, 21:3–27.
Froehlich, T. J. (1994). Relevance reconsidered — towards an agenda for the 21st century: Introduction to special topic issue on relevance research. Journal of the American Society for Information Science, 45(3):124–134.
Gärdenfors, P. (1988). Knowledge in Flux: Modeling in Dynamics of Epistemic States. MIT Press, Cambridge.
Gärdenfors, P. (1992). Belief Revision. Cambridge University Press.
Gent, I. P. (1992). A sequent-or tableau-style system for lewis’s counterfactual logic vc. Notre Dame Journal of Formal Logic, 33(3):369–382.
Gibbard, A. and Harper, W. L. (1981). Counterfactuals and two kinds of expected utility. In Harper, W. L., Stalnaker, R., and Pearce, G., editors, Ifs, pages 153–190. Cambridge, D. Reidel.
Ginsberg, M. L. (1986). Counterfactuals. Artificial Intelligence, 30:35–79.
Katsuno, H. and Mendelzon, A. O. (1992). On the difference between updating a knowledge base and revising it. In Gärdenfors, P., editor, Belief Revision, pages 181–203. Cambridge University Press.
Lewis, D. (1973). Counterfactuals. Harvard University Press.
Lewis, D. (1976). Probability of conditionals and conditional probabilities. Philosophical Review, 85(3):297–315.
Nie, J.-Y. and Brisebois, M. (1994). Using a general thesaurus to set fuzzy relevance of terms in information retrieval. In EXPERSYS, Houston, Texas.
Nie, J.-Y. and Brisebois, M. (1996). An inferential approach to information retrieval and its implementation using a manual thesaurus. Artificial Intelligence Review, 10:409–439.
Nie, J.-Y, Brisebois, M., and Lepage, F. (1995). Information retrieval as counterfactual. The Computer Journal, 38(8):643–657.
Read, S. (1988). Relevant Logic: A Philosophical Examination of Inference. B. Blackwell, Oxford, New York.
Saracevic, T. (1970). The concept of relevance in information science: a historical review. In Saracevic, T., editor, Introduction to Information Science, pages 111–151. R. R. Bowker Company, New York.
Saracevic, T. (1988). A study of information seeking and retrieving (i): Background and methodology. Journal of the American Society for Information Science, 39:161–176.
Sembok, T. M. T. and van Rijsbergen, C. J. (1990). Silol: A simple logical-linguistic document retrieval system. Information Processing & Management, 26(1):111–134.
Sperber, D. and Wilson, D. (1986). Relevance: Communication and cognition. Basil Blackwell, Oxford.
Stalnaker, R. (1968). A Theory of conditional. Studies in Logical Theory. Blackwell, Oxford.
van Rijsbergen, C. J. (1986). A non-classical logic for information retrieval. The Computer Journal, 29(6):481–485.
van Rijsbergen, C. J. (1989). Towards an information logic. In Research and Development on Information Retrieval — ACM-SIGIR Conference, pages 77–86, Brussel.
Yao, Y. Y. (1995). Measuring retrieval effectiveness based on user preference of documents. Journal of the American Society for Information Science, 46(2): 133–145.
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Nie, JY., Lepage, F. (1998). Toward a Broader Logical Model for Information Retrieval. In: Crestani, F., Lalmas, M., van Rijsbergen, C.J. (eds) Information Retrieval: Uncertainty and Logics. The Kluwer International Series on Information Retrieval, vol 4. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5617-6_2
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