Information Technology, Information and History
Keywords:
Information Technology in History, Source Criticism, Information Theory, Fuzzy Sets, Semantic ComputingAbstract
One of the truisms of our decade is, that we live in the information age. Indeed information technology influences all disciplines in academia and its nature is central to the discussion of at least a dozen disciplines, from Floridi's philosophy of information to computer science and from quantum physics to library science. That such a large number of fields of research emphasize different concepts is not really surprising; it would be, if they would not. Nevertheless, all of them are supported by one integrated type of information technology, so the underlying concept must be consistent. In the field of history we can show, however, that the classical derivation of practical information technology, derived from Shannon's implicit creation of an equivalence between communication and information, vulnerates the way information is handled in the field. We will discuss how this creates severe limits to the practical application of current information technology and which changes would be needed to support history's problem domain. This is particularly important as experience shows that theoretical discussions of the nature of information, general or within specific knowledge domains, have almost no influence on the development of the actual information technology, unless the theoretical discussions reflect implementation policies.References
References
Ackoff, R.L. (1989), “From Data to Wisdom”, Journal of Applied Systems Analysis, vol. 15, pp. 3-9.
Adamo, J.M. (1980), “L.P.L. A fuzzy Programming Language: 1 Syntactic Aspects,” Fuzzy Sets and Systems, vol. 3, pp. 151-179.
Adamo, J.M. (1980), “L.P.L. A fuzzy Programming Language: 2 Semantic Aspects,” Fuzzy Sets and Systems, vol. 3, pp. 261-289.
Ashenhurst, R.L (1996), “Ontological Aspects of Information Modeling”, Minds and Machines, vol. 6, pp. 287-394.
Atanassov, K.T. (1986), “Intuitionistic Fuzzy Sets”, Fuzzy Sets and Systems, vol. 20, pp. 87-96.
Barr, M. & C. Wells (2010), Category Theory for Computing Science, Montréal, Canada.
Baskarada, S. & A. Koronios (2013), “Data, Information, Knowledge, Wisdom (DIKW): A Semiotic Theoretical and Empirical Exploration of the Hierarchy and its Quality Dimension”, Australasian Journal of Information Systems, vol. 18, pp. 5-24.
Blair, B. (1994), “Interview with Lotfi Zadeh”, Azerbaijan International, vol. 2, Winter, pp. 46-47, 50.
Devlin, K. (1991), Logic and Information, Cambridge, UK.
Devlin, K. (2009), “Modeling Real Reasoning”, in: Sommaruga, G. (ed.): Formal Theories of Information, (= Lecture Notes in Computer Science 5363), Berlin-Heidelberg, Germany, pp. 234-252.
Droysen, J.G. (1937), Historik. Vorlesungen über Enzyklopädie und Methodologie der Geschichte, ed. by Rudolf Hübner, München, Deutschland.
Duan, Y. et al. (2017), “Specifying Architecture of Knowledge Graph with Data Graph, Information Graph, Knowledge Graph and Wisdom Graph”, presented at SERA 2017, available at: doi.org/10.1109/SERA.2017.7965747 (accessed 10.07.2019).
Fauconnier, G. & M. Turner (2003), The Way We Think. Conceptual Blending and the Mind’s Hidden Complexities, New York, USA.
Favre-Bull, B. (2001), Information und Zusammenhang. Informationsfluß in Prozessen der Wahrnehmung, des Denkens und der Kommunikation, Heidelberg, Deutschland.
Floridi, L. (2011), The Philosophy of Information, Oxford, UK.
Frické, M. (2009), “The Knowledge Pyramid: A Critique of the DIKW Hierarchy”, Journal of Information Science, vol. 35, pp. 131-142.
Harris, R. (1998), Introduction to Integrational Linguistics, Oxford, UK.
Herrera, F. et al. (eds.) (2014), “Special Issue on Hesitant Fuzzy Sets”, International Journal of Intelligent Systems, vol. 29, pp. 493-595.
Isac, D. & C. Reiss (2008) I-Language, Oxford University Press, Oxford, UK, 392 p.
Jiang, Y. et al. (2009), “Reasoning with Expressive Fuzzy Rough Description Logics”, Fuzzy Sets and Systems, vol. 160, pp. 3403-3424.
Jifa, G. & Z. Lingling (2014), “Data, DIKW, Big Data and Data Science”, Procedia Computer Science, vol. 31, pp. 814-821.
Kettinger, W.J. & Y. Li (2010), “The infological equation extended: towards conceptual clarity in the relationship between data, information and knowledge”, European Journal of Information Systems, vol. 19, pp. 409-421.
Lakoff, G. & M. Johnson (1980), Metaphors We Live By, Chicago, USA, with a substantial afterword reprinted 2003.
Liu, S. & Y. Lin (2006), Grey Information. Theory and Practical Applications, London, UK.
Liu, S. & Y. Lin (2011), Grey Systems. Theory and Practical Applications, London, UK.
Langefors, B. (1973), Theoretical Analysis of Information Systems, Göteborg, Germany.
Nanda, S. & S. Majumdar (1992), “Fuzzy Rough Sets”, Fuzzy Sets and Systems, vol. 45, pp. 157-160.
Nielsen, M.A. & I.L. Chuang (2000), Quantum Computation and Quantum Information, Cambridge, UK.
Pawlak, Z. (1982), “Rough Sets”, International Journal of Parallel Programming, vol. 11,
pp. 341-356.
Pawlak, Z. (1985), “Rough Sets and Fuzzy Sets”, Fuzzy Sets and Systems, vol. 17, pp. 99-102.
Rowley, J. (2007), “The Wisdom Hierarchy: Representations of the DIKW Hierarchy”, Journal of Information Science, vol. 33, pp. 163-180.
Saab, D.J. & U.V. Riss (2011), “Information as Ontologization”, Journal of the American Society for Information Science and Technology, vol. 62, pp. 2236-2246.
Schmidt, D. & R. Colomb (2009), “A Data Structure for Representing Multi-Version Texts Online”, International Journal of Human-Computer Studies, vol. 67, pp. 497-514.
Shafer, G. (1976), A Mathematical Theory of Evidence, Princeton University Press, Princeton, USA, 314 p.
Shannon, C.E. (1948), “A Mathematical Theory of Communication”, Bell System Technical Journal, vol. 27, pp. 379–423, 623–656.
Sommaruga, G. (2009), “One or Many Concepts of Information?”, in Sommaruga, G. (ed.), Formal Theories of Information, (= Lecture Notes in Computer Science 5363), Berlin-Heidelberg, Deutschland, pp. 253-267.
Termini, T. (2012), “On some ‘Family Resemblances’ of Fuzzy Set Theory and Human Sciences”, in: Seising, R. & V. Sanz (eds.), Soft Computing in Humanities and Social Sciences (= Studies in Fuzziness and Soft Computing 273), Berlin-Heidelberg, Deutschland, pp. 39-54.
Thaller, M. (1993): “Historical Information Science: Is there such a Thing? New Comments on an Old Idea.”, in Orlandi, T., Seminario discipline umanistiche e informatica. Il problema dell' integrazione, Rome, Italy, pp. 51-86. Reprinted under the same title in: Historical Social Research, Suppl. 29 (2017), pp. 260-286, available at: doi.org/10.12759/hsr.suppl.29.2017.260-286 (accessed 10.07.2019).
Thaller, M. (2017), “The Cologne Information Model: Representing Information Persistently”, in Thaller, M. (ed.), The eXtensible Characterisation Languages – XCL, Hamburg, Deutschland, pp. 223-39. Reprinted under the same title in: Historical Social Research Supplement 29, pp. 344-356, available at: doi.org/10.12759/hsr.suppl.29.2017. 344-356 (accessed 10.07.2019).
Torra, V. (2010), “Hesitant Fuzzy Sets”, International Journal of Intelligent Systems, vol. 25, pp. 529-539.
Weaver, W. (1949), “Introductory Note on the General Setting of the Analytical Communication Studies”, in Shannon, C.E. & W. Weaver, The Mathematical Theory of Communication, The University of Illinois Press, Urbana and Chicago, USA.
Zadeh, L.A. (1965), “Fuzzy Sets”, Information and Control, 8, pp. 338-353.
Zadeh, L.A. (1975), “The Concept of a Linguistic Variable and its Application to Approximate Reasoning”, I – III, Information Sciences, vol. 8, pp. 199-249, 301-357 and vol. 9, pp. 43-80.
Zadeh, L.A. (1978), “Fuzzy Sets as a Basis for a Theory of Possibility”, Fuzzy Sets and Systems, vol.1, pp. 3-28.
Zadeh, L.A. & J. Kacprzyk (eds.) (1999), Computing with Words in Information / Intelligent Systems I and II (= Studies in Fuzziness and Soft Computing, vols. 33 and 34).
Zadeh, L.A. (2005), “Toward a Generalized Theory of Uncertainty (GTU) – an outline”, Information Sciences, 172, pp. 1-40.