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Some Notes on Extracting Linguistic Summaries Built with Epistemic Modalities and Natural Language Connectives of Equivalence

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EN
Abstrakty
EN
In this paper we deal with an original technically oriented model for cognitive semantics. As the expected area of application we focus on the process of extraction of modal linguistic summaries from data managed by autonomous components of ambient systems and intelligent environments. As such, the cognitive semantics is defined for a particular case of modal natural language statements with epistemic modalities. The statements of interest are built with natural language operators, representing epistemic modalities (related to the main cognitive states of knowledge certainty: full certainty, strong belief and epistemic possibility), and natural language connectives of equivalence. Furthermore, an approach to their effective processing by autonomous computational systems is designed. An internal architecture of the autonomous computational component is designed with respect to modular model for natural language processing with separate modules for epistemic and semantic memory storage and processing. An original theoretical concept underlying the model of semantic memory is a holon defined as a collection of complementary linguistic protoforms. Finally, we provide several illustrative computational examples of linguistic summaries’ extraction, based on artificial and real data.
Twórcy
  • Wroclaw University of Science and Technology Faculty of Computer Science and Management, Department of Informatics Laboratory of Computational Semiotics and Interactive Systems
  • Wroclaw University of Science and Technology Faculty of Computer Science and Management, Department of Informatics Laboratory of Computational Semiotics and Interactive Systems
autor
  • Wroclaw University of Science and Technology Faculty of Computer Science and Management, Department of Informatics Laboratory of Computational Semiotics and Interactive Systems
Bibliografia
  • [1] A. Ramos-Soto, A. Bugarín, S. Barro, On the role of linguistic descriptions of data in the building of natural language generation systems, Fuzzy Sets and Systems 285, pp. 31–51 (2016).
  • [2] A. Włodarczyk, Grounding of the meta-informative status of utterances, in Meta-informative Centering in Utterances: Between Semantics and Pragmatics, A. Włodarczyk and H. Włodarczyk, Eds. Amsterdam: John Benjamins Publishing Co., pp. 41–58, (2013).
  • [3] C. Ogden, I. Richards, The meaning of meaning, Harcourt, Brace, 1923.
  • [4] C. Peirce, Collected Papers of C. S. Peirce, Harvard University Press, Cambridge, 1932.
  • [5] D. Dennett, The Intentional Stance, MIT Press, 1989.
  • [6] D. Dennett, True believers: The intentional strategy and why it works, In Mental Representation: A Reader. Blackwell, 1994.
  • [7] Dataset Housevoters, archive.ics.uci.edu/ml/datasets/Congressional+Voting+Records, Congressional Quarterly Almanac, 98th Congress, 2nd session 1984, Volume XL: Congressional Quarterly Inc. Washington, D.C., 1985.
  • [8] E. Aarts, B. de Ruyter, New research perspectives on Ambient Intelligence. Journal of Ambient and Smart Env. 1, 50-14 (2009).
  • [9] F.J. Stachowiak, Tracing the role of memory and attention for the meta-informative validation of utterances, in Metainformative Centering in Utterances: Between Semantics and Pragmatics, A. Włodarczyk and H. Włodarczyk, Eds. Amsterdam: John Benjamins Publishing Co., pp. 121-142 (2013).
  • [10] G. Lakoff, Women, Fire, and Dangerous Things: What Categories Reveal About the Mind, Chicago: University of Chicago, 1987.
  • [11] G. Moysea, M.-J. Lesota, Linguistic summaries of locally periodic time series, Fuzzy Sets and Systems 285, 94–117 (2016).
  • [12] G. Underwood, Implicit Cognition, Oxford University Press, 1996.
  • [13] J. Kacprzyk, Computing with words is an implementable paradigm: fuzzy queries, linguistic data summaries, and natural-language generation, IEEE Trans. Fuzzy Syst. 18(3), 451–472 (2010).
  • [14] J. Kacprzyk, A. Wilbik, S. Zadro˙zny, An approach to the linguistic summarization of time series using a fuzzy quantifier driven aggregation, Int. J. Intell. Syst. 25(5), 411–439 (2010).
  • [15] J. Kacprzyk, A. Wilbik, S. Zadro˙zny, Linguistic summarization of time series using a fuzzy quantifier driven aggregation, Fuzzy Sets Syst. 159(12), 1485–1499 (2008).
  • [16] J. Kacprzyk, R.R. Yager, Linguistic summaries of data using fuzzy logic, International Journal of General Systems 30, 133–154 (2001).
  • [17] J. Kacprzyk, S. Zadro˙zny, Computing with words in intelligent database querying: standalone and Internet-based applications, Information Sciences 34, 71–109 (2001).
  • [18] J. Kacprzyk, S. Zadro˙zny, Linguistic database summaries and their protoforms: towards natural language based knowledge discovery tools, Information Sciences 173, 281–304 (2005).
  • [19] J. Nuyts, Epistemic modality, language, and conceptualization: A cognitive-pragmatic perspective, Amsterdam: John Benjamins Publishing Co., 2001.
  • [20] L.A. Zadeh, The concept of linguistic variable and its applications to approximate reasoning - I (II, III), Information Sciences 8(8,9), 199–249 (301-357, 43-80) (1975).
  • [21] L. Talmy, Toward a Cognitive Semantics, Cambridge, MA: MIT Press, 2000.
  • [22] L.A. Zadeh, A computational approach to fuzzy quantifiers in natural languages, Computers and Mathematics with Applications 9,149–184 (1983).
  • [23] L.A. Zadeh, A prototype-centered approach to adding deduction capabilities to search engines – the concept of a protoform,BISC Seminar, University of California, Berkeley, 2002
  • [24] L.A. Zadeh, From computing with numbers to computing with words - from manipulation of measurements to manipulation of perceptions, Int. J. Applied Mathematics and Computer Science 12(3), 307–324 (2002).
  • [25] L.A. Zadeh, Fuzzy logic = computing with words, IEEE Trans. Fuzzy Syst. 4 (2), pp. 103–111, 1996.
  • [26] L.A. Zadeh, Outline of a new approach to the analysis of complex systems and decision processes, IEEE Trans. Systems Man Cybernet. SMC-3, pp. 28–44, 1973.
  • [27] P. Smets, Varieties of ignorance, Information Sciences 57-58, 135-144 (1991).
  • [28] P.N. Johnson-Laird, Mental models. Towards a cognitive science of language, inference, and consciousness, Cambridge Uni. Press, 1983.
  • [29] R. Katarzyniak, On some properties of grounding nonuniform sets of modal conjunctions. International Journal of Applied Mathematics and Computer Science 16(3), 399–412 (2006).
  • [30] R. Katarzyniak, On some properties of grounding simple modalities, Systems Science 31 (3), 59–86 (2005).
  • [31] R. Katarzyniak, On some properties of grounding uniform sets of modal conjunctions. Journal of Intelligent Fuzzy Systems 17(3), 209–218 (2006).
  • [32] R. Katarzyniak, Some notes on grounding singletons of modal conjunctions, Systems Science 32(1), 45–55 (2005).
  • [33] R. Katarzyniak, D. Więcek, An approach to extraction of linguistic recommendation rules – application of modal conditionals grounding, Lecture Notes in Computer Science Lecture Notes in Artificial Intelligence, vol. 7653, pp. 249-258, 2012.
  • [34] R. Katarzyniak, D. Więcek, Modal equivalences as linguistic summarisation of data resources, [In:] Information Systems Architecture and Technology: Intelligent Information Systems, Knowledge Discovery, Big Data and High Performance Computing, Wrocław University of Technology, Wrocław (Poland), pp. 23-32, 2013.
  • [35] R. Katarzyniak, N.T. Nguyen and J.C. Jain, A model for fuzzy grounding of modal conjunctions in artificial agents, Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), vol. 4953, pp. 341-350, 2008.
  • [36] R. Katarzyniak, N.T. Nguyen and J.C. Jain, Soft computing approach to contextual determinantion of grounding sets for simple modalities, Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), vol. 4692, pp. 230-237, 2007.
  • [37] R. Katarzyniak,W. Lorkiewicz, J. Sobecki An architecture for verbal summaries in smart mobile devices, 2015 IEEE 15th International Conference on Data Science and Data Intensive Systems, Sydney, Australia. Piscataway, NJ: IEEE, pp. 540-547, 2015.
  • [38] R. Katarzyniak, W. Lorkiewicz, J. Sobecki A model for linguisticsummaries of results from usability studies, 2015 IEEE 15th International Conference on Data Science and Data Intensive Systems, Sydney, Australia. Piscataway, pp. 103-110, NJ: IEEE, 2015.
  • [39] R. Katarzyniak, W. Lorkiewicz, O. Krejcar, Linguistic alerts in information filtering systems: towards technical implementations of cognitive semantics, 2016 ICEIS 18th International Conference on Enterprise Information Systems, 25-28 April 2016 Rome, Italy, pp. 512-519, SCITEPRESS, 2016.
  • [40] R. Kitchin, The Data Revolution. Big Data, Open Data, Data Infrastructures and Their Consequences, Sage Pub. Ltd., 2014.
  • [41] R.J.G.B. Campello, W.C. Amaral, Modeling and linguistic knowledge extraction from systems using fuzzy relational models, Fuzzy Sets and Systems 121, 113–126 (2001).
  • [42] R.M. Tong, Synthesis of fuzzy models for industrial processes – some recent results, Internat. J. General Systems 4, 143–162 (1978).
  • [43] R.P. Katarzyniak, Extracting Modal Implications and Equivalences from Cognitive Minds, Proc. of the 7th Int. Conf. Discovery Science – DS 2004, 2-5 October 2004, Padova (Italy), LNCS, vol. 3245, pp. 420-428, 2004.
  • [44] R.P. Katarzyniak, The Language Grounding Problem and its Relation to the Internal Structure of Cognitive Agents, Journal of Universal Computer Science 11(2), 357–374 (2005).
  • [45] R.P. Katarzyniak, W.A. Lorkiewicz, D. Więcek, Modal linguistic summaries based on natural language equivalence with cognitive semantics, Proc. 12th Int. Conf. on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNCFSKD), IEEE, pp. 1904-1909, 2016.
  • [46] R.P.Katarzyniak, G. Popek, Integration of modal and fuzzy methods of knowledge representation in artificial agents, International Journal of Software Engineering and Knowledge Engineering 23(1), 13–29 (2012).
  • [47] R.R. Yager, A new approach to summarization of data, Information Sciences 28, 69–86 (1982).
  • [48] S. Harnad, The symbol grounding problem, Physica D: Nonlinear Phenomena 42(1-3), 335–346 (1990).
  • [49] T. Takagi, M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Systems Man Cybernet. SMC-15, pp. 116–132, 1985.
  • [50] W. Minker, R. López-Cózar, M. McTear, The role of spoken language dialogue interaction in intelligent environments. Journal of Ambient and Smart Environments 1, 31–36 (2009).
  • [51] W. Pedrycz, An identification algorithm in fuzzy relational systems, Fuzzy Sets and Systems 13, 153–167 (1984).
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
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