Some Notes on Extracting Linguistic Summaries Built with Epistemic Modalities and Natural Language Connectives of Equivalence
Katarzyniak Radosław, Lorkiewicz Wojciech, Więcek Dominik
Wroclaw University of Science and Technology
Faculty of Computer Science and Management, Department of Informatics
Laboratory of Computational Semiotics and Interactive Systems
E-mail: {radoslaw.katarzyniak, wojciech.lorkiewicz, dominik.wiecek}@pwr.edu.pl
Received:
Received: 23 November 2016; revised: 27 December 2016; accepted: 27 December 2016; published online: 07 March 2017
DOI: 10.12921/cmst.2016.0000056
Abstract:
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.
Key words:
autonomous system, epistemic modality, equivalence, linguistic description, linguistic summary, natural language connective, natural language engineering
References:
[1] A. Ramos-Soto, A. Bugarín, S. Barro, On the role of linguistic
descriptions of data in the building of natural language gener-
ation 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: Be-
tween Semantics and Pragmatics, A. Włodarczyk and H. Wło-
darczyk, 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 Ambi-
ent 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 Meta-
informative Centering in Utterances: Between Semantics and
Pragmatics, A. Włodarczyk and H. Włodarczyk, Eds. Amster-
dam: 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. Zadrożny, An approach to the lin-
guistic 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. Zadrożny, Linguistic summariza-
tion 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. Zadrożny, Computing with words in intelli-
gent database querying: standalone and Internet-based appli-
cations, Information Sciences 34, 71–109 (2001).
[18] J. Kacprzyk, S. Zadrożny, 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 conceptualiza-
tion: A cognitive-pragmatic perspective, Amsterdam: John
Benjamins Publishing Co., 2001.
[20] L.A. Zadeh, The concept of linguistic variable and its appli-
cations to approximate reasoning – I (II, III), Information Sci-
ences, 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 Applica-
tions, 9,149–184 (1983).
[23] L.A. Zadeh, A prototype-centered approach to adding deduc-
tion capabilities to search engines—the concept of a proto-
form, 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 Sci-
ence, 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 sci-
ence 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. Wi ̨ ecek, An approach to extraction of lin-
guistic recommendation rules – application of modal condi-
tionals grounding, Lecture Notes in Computer Science Lecture
Notes in Artificial Intelligence, vol. 7653, pp. 249-258, 2012.
[34] R. Katarzyniak, D. Wi ̨ecek, Modal equivalences as linguis-
tic summarisation of data resources, [In:] Information Systems
Architecture and Technology: Intelligent Information Systems,
Knowledge Discovery, Big Data and High Performance Com-
puting, 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 Intel-
ligence), 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 (Lec-
ture 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 lin-
guistic summaries of results from usability studies, 2015 IEEE
15th International Conference on Data Science and Data Inten-
sive 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 implemen-
tations 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 mod-
els, 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 Equiv-
alences from Cognitive Minds, Proc. of the 7th Int. Conf. Dis-
covery 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. Wi ̨ecek, Modal lin-
guistic summaries based on natural language equivalence with
cognitive semantics, Proc. 12th Int. Conf. on Natural Com-
putation, Fuzzy Systems and Knowledge Discovery (ICNC-
FSKD), IEEE, pp. 1904-1909, 2016.
[46] R.P.Katarzyniak, G. Popek, Integration of modal and fuzzy
methods of knowledge representation in artificial agents, In-
ternational Journal of Software Engineering and Knowledge
Engineering, 23(1), 13–29 (2012).
[47] R.R. Yager, A new approach to summarization of data, Infor-
mation Sciences, 28, 69–86 (1982).
[48] S. Harnad, The symbol grounding problem, Physica D: Non-
linear 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 spo-
ken 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).
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.
Key words:
autonomous system, epistemic modality, equivalence, linguistic description, linguistic summary, natural language connective, natural language engineering
References:
[1] A. Ramos-Soto, A. Bugarín, S. Barro, On the role of linguistic
descriptions of data in the building of natural language gener-
ation 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: Be-
tween Semantics and Pragmatics, A. Włodarczyk and H. Wło-
darczyk, 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 Ambi-
ent 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 Meta-
informative Centering in Utterances: Between Semantics and
Pragmatics, A. Włodarczyk and H. Włodarczyk, Eds. Amster-
dam: 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. Zadrożny, An approach to the lin-
guistic 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. Zadrożny, Linguistic summariza-
tion 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. Zadrożny, Computing with words in intelli-
gent database querying: standalone and Internet-based appli-
cations, Information Sciences 34, 71–109 (2001).
[18] J. Kacprzyk, S. Zadrożny, 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 conceptualiza-
tion: A cognitive-pragmatic perspective, Amsterdam: John
Benjamins Publishing Co., 2001.
[20] L.A. Zadeh, The concept of linguistic variable and its appli-
cations to approximate reasoning – I (II, III), Information Sci-
ences, 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 Applica-
tions, 9,149–184 (1983).
[23] L.A. Zadeh, A prototype-centered approach to adding deduc-
tion capabilities to search engines—the concept of a proto-
form, 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 Sci-
ence, 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 sci-
ence 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. Wi ̨ ecek, An approach to extraction of lin-
guistic recommendation rules – application of modal condi-
tionals grounding, Lecture Notes in Computer Science Lecture
Notes in Artificial Intelligence, vol. 7653, pp. 249-258, 2012.
[34] R. Katarzyniak, D. Wi ̨ecek, Modal equivalences as linguis-
tic summarisation of data resources, [In:] Information Systems
Architecture and Technology: Intelligent Information Systems,
Knowledge Discovery, Big Data and High Performance Com-
puting, 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 Intel-
ligence), 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 (Lec-
ture 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 lin-
guistic summaries of results from usability studies, 2015 IEEE
15th International Conference on Data Science and Data Inten-
sive 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 implemen-
tations 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 mod-
els, 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 Equiv-
alences from Cognitive Minds, Proc. of the 7th Int. Conf. Dis-
covery 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. Wi ̨ecek, Modal lin-
guistic summaries based on natural language equivalence with
cognitive semantics, Proc. 12th Int. Conf. on Natural Com-
putation, Fuzzy Systems and Knowledge Discovery (ICNC-
FSKD), IEEE, pp. 1904-1909, 2016.
[46] R.P.Katarzyniak, G. Popek, Integration of modal and fuzzy
methods of knowledge representation in artificial agents, In-
ternational Journal of Software Engineering and Knowledge
Engineering, 23(1), 13–29 (2012).
[47] R.R. Yager, A new approach to summarization of data, Infor-
mation Sciences, 28, 69–86 (1982).
[48] S. Harnad, The symbol grounding problem, Physica D: Non-
linear 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 spo-
ken 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).
