Consensus Theory for Cognitive Agents’ Unstructured Knowledge Conflicts Resolving in Management Information Systems

  • Marcin HernesEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11370)


Management information systems of distributed nature, play a vital role in any kind of business organizations’ activity. The multi-agent systems, based on cognitive agent architecture, deserve special attention in this class of systems. They allow not only to access to the information and quick search for interesting us information, its analysis and drawing conclusions, but also, in addition to responding to stimuli from the environment, have the cognitive ability to learning through empirical experience gained through direct interaction with the environment. It, in turn, allows for the automatic generation of variants of decisions and, in many cases, even taking and putting into action the decisions. The big problem currently, however, turns out to be the processing of unstructured knowledge in systems of this kind. In contemporary companies, unstructured knowledge is essential, mainly due to the possibility to obtain better flexibility and competitiveness of the organization. Therefore, unstructured knowledge supports structured knowledge to a high degree. Simultaneously, one must note that the most prevailing phenomenon is a conflict in unstructured knowledge. It is extremely difficult to resolve conflicts of this kind properly. However, it is also very important, since it can improve the operation of management information system and, consequently, help the organization that employs the system become more flexible and competitive.

The main aim of this work is to develop a formal method to resolve conflicts in unstructured knowledge of cognitive agents in management information systems employing the consensus theory. The first part of this work presents an analysis problems related to management information systems and unstructured knowledge processing in these systems. Next, the cognitive agents are characterized with particular emphasis on unstructured knowledge processing. The use of consensus theory in unstructured knowledge conflicts resolving have been characterized in the third part of the work. The last part presents the developed method for cognitive agents’ knowledge conflicts resolving. The correctness of the method was verified using the prototypes of the agents helping to invest in the Forex market and processing user opinions about products and services.


Management information systems Cognitive agents Unstructured knowledge Knowledge conflicts Consensus theory 


  1. 1.
    Kisielnicki, J.: Management information systems. Placet, Warszawa (2013). (in Polish)Google Scholar
  2. 2.
    Wormell, I.: Databases as analytical tools. In: Dekker, M. (ed.) Encyclopedia of Library and Information Science, New York, vol. 70, no. 33, pp. 77–92 (2000)Google Scholar
  3. 3.
    Zins, C.: Conceptual approaches for defining data, information, and knowledge. J. Am. Soc. Inf. Sci. 58, 479–493 (2007). Scholar
  4. 4.
    Coulouris, G., Dollimore, J., Kindberg, T.: Distributed Systems: Concepts and Design, 5th edn. Pearson, London (2011)zbMATHGoogle Scholar
  5. 5.
    Ferber, J.: Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison Wesley, New York (1999)Google Scholar
  6. 6.
    Wooldridge, M.: An Introduction to MultiAgent Systems. Wiley, Hoboken (2002). p. 366Google Scholar
  7. 7.
    Nguyen, N.T.: Methods for deriving consensus and their application in conflict resolving in distributed systems. PWr Printing House, Wroclaw (2002). (in Polish)Google Scholar
  8. 8.
    Hernes, M., Nguyen, N.T.: Deriving consensus for hierarchical incomplete ordered partitions and coverings. J. Univ. Comput. Sci. 13(2), 317–328 (2007)Google Scholar
  9. 9.
    Bush, P.: Tacit Knowledge in Organizational knowledge. IGI Global, Hershey, New York (2008)CrossRefGoogle Scholar
  10. 10.
    Griffin, R.W.: Management, 11th edn. South-Western College Pub, Chula Vista (2012)Google Scholar
  11. 11.
    Drucker, P.F.: Management Challenges for 21st Century. Harper Business, New York (2000)Google Scholar
  12. 12.
    Duan, H., Zheng, Y.: A study on features of the CRFs-based Chinese named entity recognition. Int. J. Adv. Intell. Inform. 3(2), 287–294 (2011)Google Scholar
  13. 13.
    Girdhar, J.: Management Information Systems. Oxford University Press, Oxford (2013)Google Scholar
  14. 14.
    Laudon, K.C., Laudon, J.P.: Management Information Systems: Managing the Digital Firm, 14th edn. Pearson, London (2015)zbMATHGoogle Scholar
  15. 15.
    Burstein, F., Holsapple, C.W.: Handbook on Decision Support Systems. Springer, Berlin (2008). Scholar
  16. 16.
    Kendal, S.L., Creen, M.: An Introduction to Knowledge Engineering. Springer, London (2007). Scholar
  17. 17.
    Adamczewski, P.: Evolution in ERP-expanding functionality by bi-modules in knowledge-based management systems. In: Kubiak, B.F., Korowicki, A. (eds.) Information Management. Gdansk University Press, Gdańsk (2009)Google Scholar
  18. 18.
    Nycz, M.: Business intelligence as the exemplary modern technology influencing on the development on the enterprise. In: Kubiak, B.F., Korowicki, A. (eds.) Information Management. Gdansk University Press, Gdańsk (2009)Google Scholar
  19. 19.
    Sapkota, B., Roman, D., Kruk, S.R., Fensel, D.: Distributed web service discovery architecture. In: Advanced International Conference on Telecommunications and International Conference on Internet and Web Applications and Services (AICT-ICIW 2006), p. 136 (2006)Google Scholar
  20. 20.
    Ferreira, C.: Supporting unified distributed management and autonomic decisions: design, implementation and deployment. J. Netw. Syst. Manag. 25, 416–456 (2017). Scholar
  21. 21.
    Frank, L., Pedersen, R.U.: Integrated distributed/mobile logistics management. In: Hameurlain, A., Küng, J., Wagner, R. (eds.) Transactions on Large-Scale Data- and Knowledge-Centered Systems V. LNCS, vol. 7100, pp. 206–221. Springer, Heidelberg (2012). Scholar
  22. 22.
    Turban, E., King, D., Lee, J.K., Liang, T.-P., Turban, D.C.: Mobile commerce and ubiquitous computing. In: Turban, E., King, D., Lee, J.K., Liang, T.-P., Turban, D.C. (eds.) Electronic Commerce. STBE, pp. 257–308. Springer, Cham (2015). Scholar
  23. 23.
    Markoska, K., Ivanochko, I., Gregus ml., M.: Mobile banking services—business information management with mobile payments. In: Kryvinska, N., Gregus, M. (eds.) Agile Information Business. FSM, pp. 125–175. Springer, Singapore (2018). Scholar
  24. 24.
    Bytniewski, A. (ed.): An Architecture of integrated management system. Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu, Wrocław (2015). (in Polish)Google Scholar
  25. 25.
  26. 26.
    Plikynas, D.: Multiagent based global enterprise resource planning: conceptual view. WSEAS Trans. Bus. Econ. 5(6), 31–123 (2008)Google Scholar
  27. 27.
    Davenport, T.: Putting the enterprise into the enterprise system. Harv. Bus. Rev. 76, 121–131 (1998)Google Scholar
  28. 28.
    Better execute your business strategies - with our enterprise resource planning (ERP) solution. Accessed 28 Nov 2017
  29. 29.
    Zhang, D.Z., Anosike, A.I., Lim, M.K., Akanle, O.M.: An agent-based approach for e-manufacturing and supply chain integration. Comput. Ind. Eng. 51(2), 343–360 (2006)CrossRefGoogle Scholar
  30. 30.
    Boella, G., Hulstijn, J., Van Der Torre, L.: Virtual organizations as normative multiagent systems. In: Proceedings of the 38th Annual Hawaii International Conference on System Sciences, p. 192c (2005)Google Scholar
  31. 31.
    Hecker, A.: Knowledge beyond the individual? Making sense of a notion of collective knowledge in organization theory. Org. Stud. 33(3), 423–445 (2012)CrossRefGoogle Scholar
  32. 32.
    Amidon, D.: The Innovation Strategy for the Knowledge Economy. Heinemann, Butterworth (1997)Google Scholar
  33. 33.
    Jakubczyc, J., Mercier-Laurent, E., Owoc, M.: What is Knowledge Management?. KAM, Wroclaw (1999)Google Scholar
  34. 34.
    Mercier-Laurent, E.: Artificial intelligence for successful Kflow. In: Mercier-Laurent, E., Boulanger, D. (eds.) AI4KM 2015. IAICT, vol. 497, pp. 149–165. Springer, Cham (2016). Scholar
  35. 35.
    Salojärvi, S., Furu, P., Sveiby, K.-E.: Knowledge management and growth in finnish SMEs. J. Knowl. Manag. 9(2), 103–122 (2005)CrossRefGoogle Scholar
  36. 36.
    Girard, J.P., Girard, J.L.: Defining knowledge management: toward an applied compendium. J. Appl. Knowl. Manag. 3(1), 14 (2015)Google Scholar
  37. 37.
    Davenport, T.: Enterprise 2.0: the new, new knowledge management? Harv. Bus. Rev. (2008).
  38. 38.
    Newell, A., Shaw, J.C., Simon, H.A.: Report on a general problem-solving program. In: Proceedings of the International Conference on Information Processing, pp. 256–264 (1959)Google Scholar
  39. 39.
    Kingston, J., Shadbolt, N., Tate, A.: CommonKADS models for knowledge based planning. In: AAAI/IAAI, vol. 1, pp. 477–482 (1996)Google Scholar
  40. 40.
    Mercier-Laurent, E., Owoc, M.L., Boulanger, D. (eds.): AI4KM 2014. IAICT, vol. 469. Springer, Cham (2015). Scholar
  41. 41.
    Motta, E., Rajan, T., Eisenstadt, M.: A methodology and tool for knowledge acquisition in KEATS-2. In: Guida, G., Tasso C. (eds.) Topics in Expert Systems Design, North-Holland (1989)Google Scholar
  42. 42.
    Gruber, T.: Ontology. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems. Springer, Boston (2009). Scholar
  43. 43.
    Mercier-Laurent, E.: Innovation Ecosystems. Wiley, Hoboken (2011)CrossRefGoogle Scholar
  44. 44.
    Mercier-Laurent, E., Boulanger, D. (eds.): AI4KM 2012. IAICT, vol. 422. Springer, Heidelberg (2014). Scholar
  45. 45.
    Owoc, M.L., Marciniak, K.: Knowledge management as foundation of smart university. In: Proceedings of the Federated Conference on Computer Science and Information Systems, Kraków, pp. 1267–1272 (2013)Google Scholar
  46. 46.
    Nonaka, I., Takeuchi, H.: The Knowledge-Creating Company. How Japanese Companies Create the Dynamics of Innovation. Oxford University Press, New York, Oxford (1995)Google Scholar
  47. 47.
    Katarzyniak, R.: Grounding modalities and logic connectives in communicative cognitive agents. In: Nguyen, N.T. (ed.) Intelligent Technologies for Inconsistent Knowledge Processing. Advanced Knowledge International, Australia, Adelaide, pp. 21–37 (2004)Google Scholar
  48. 48.
    Langley, P.: The changing science of machine learning. Mach. Learn. 82(3), 275–279 (2011)zbMATHCrossRefGoogle Scholar
  49. 49.
    Sathish Babu, B., Venkataram, P.: Cognitive agents based authentication & privacy scheme for mobile transactions (CABAPS). Comput. Commun. 31(17), 4060–4071 (2008)CrossRefGoogle Scholar
  50. 50.
    Franklin, S., Patterson, F.G.: The LIDA architecture: adding new modes of learning to an intelligent, autonomous, software agent. In: Proceedings of the International Conference on Integrated Design and Process Technology. Society for Design and Process Science, San Diego (2006)Google Scholar
  51. 51.
    Hernes, M., Bytniewski, A.: Towards big management. In: Król, D., Nguyen, N.T., Shirai, K. (eds.) ACIIDS 2017. SCI, vol. 710, pp. 197–209. Springer, Cham (2017). Scholar
  52. 52.
    Bytniewski, A., Hernes, M.: The use of cognitive agents in the construction of an integrated information management system. In: Porębska-Miąc, T., Sroka, H. (eds.) Systemy Wspomagania Organizacji. Wydawnictwo Uniwersytetu Ekonomicznego w Katowicach, Katowice (2013). (in Polish)Google Scholar
  53. 53.
    Hernes, M.: A cognitive integrated management support system for enterprises. In: Hwang, D., Jung, J.J., Nguyen, N.-T. (eds.) ICCCI 2014. LNCS (LNAI), vol. 8733, pp. 252–261. Springer, Cham (2014). Scholar
  54. 54.
    Hernes, M.: Performance evaluation of the customer relationship management agent’s in a cognitive integrated management support system. In: Nguyen, N.T. (ed.) Transactions on Computational Collective Intelligence XVIII. LNCS, vol. 9240, pp. 86–104. Springer, Heidelberg (2015). Scholar
  55. 55.
    Get ahead of the game with an enterprise resource planning system (ERP) from SAP. Accessed 20 Nov 2017
  56. 56.
    Microsoft Dynamics 365 for Finance and Operations, Enterprise edition. Accessed 20 Nov 2017
  57. 57.
    Hernes, M., Matouk, K.: Knowledge conflicts in business intelligence systems. In: Annals of Computer Science and Information Systems, Proceedings of Federated Conference Computer Science and Information Systems (FedCSIS), Kraków (2013)Google Scholar
  58. 58.
    Zenkin, A.: Intelligent control and cognitive computer graphics. In: IEEE International Symposium on Intelligent Control, Montreal, Calfornia, pp. 366–371 (1995)Google Scholar
  59. 59.
    Pilipczuk, O., Eidenzon, D.: The application of cognitive computer graphics to economic data exploration. J. Autom. Mob. Robot. Intell. Syst. 7(3), 3–9 (2013)Google Scholar
  60. 60.
    Rosenberg, J., Mateos, A.: The Cloud at Your Service, 1st edn. Manning Publications, New York (2010)Google Scholar
  61. 61.
    Kubiak, B.F., Korowicki, A. (eds.): Information Management. Gdansk University Press, Gdańsk (2009)Google Scholar
  62. 62.
    Oxford dictionaries, knowledge. Accessed 20 Nov 2017
  63. 63.
    Owoc, M.L., Weichbroth, P., Zuralski, K.: Towards better understanding of context-aware knowledge transformation. In: Proceedings of FedCSIS 2017, pp. 1123–1126 (2017)Google Scholar
  64. 64.
    Dixon, N.: How to make use of your organization’s collective knowledge - accessing the knowledge of the whole organization - Part I (2011).
  65. 65.
    Chaffey, D., White, G.: Business Information Management. Prentice Hall, London (2011)Google Scholar
  66. 66.
    Kimmerle, J., Cress, U., Held, C.: The interplay between individual and collective knowledge: technologies for organisational learning and knowledge building. Knowl. Manag. Res. Pract. 8, 33–44 (2010). Scholar
  67. 67.
    Lindskog, H., Mercier-Laurent, E.: Knowledge management applied to electronic public procurement. In: Mercier-Laurent, E., Boulanger, D. (eds.) AI4KM 2012. IAICT, vol. 422, pp. 95–111. Springer, Heidelberg (2014). Scholar
  68. 68.
    Salaberry, M.R.: Declarative versus procedural knowledge. In: Liontas, J.I., DelliCarpini, M. (eds.) The TESOL Encyclopedia of English Language Teaching (2018).
  69. 69.
    Furmankiewicz, M., Sołtysik-Piorunkiewicz, A., Ziuziański, P.: Artificial intelligence and multi-agent software for e-health knowledge management system. Bus. Inform. 2(32), 51–63 (2014)Google Scholar
  70. 70.
    Karlsen, J.E.: Eur. J. Futures Res. 2, 40 (2014). Scholar
  71. 71.
    Buzzetto-More, N.: Principles of Effective Online Teaching. Informing Science Press, Santa Rosa (2007)Google Scholar
  72. 72.
    Toffler, A.: Powershift: Knowledge, Wealth and Violence at the Edge of the 21st Century. Bantam Books, New York (1990)Google Scholar
  73. 73.
    Owoc, M.L. (eds.) Elements of expert systems. Wydawnictwo AE we Wrocławiu, Wrocław (2006). (in Polish)Google Scholar
  74. 74.
    Edvinsson, L., Kitts, B., Beding, T.: The next generation of IC measurement – the digital IC-landscape. J. Intell. Capital 1(3), 263–273 (2000). Scholar
  75. 75.
    Chan, K.C., Mills, T.M.: Modeling competition over product life cycles. Asia-Pac. J. Oper. Res. 32(4) (2015)MathSciNetzbMATHCrossRefGoogle Scholar
  76. 76.
    Fikes, R., Kehler, T.: The role of frame-based representation in reasoning. Commun. ACM 28(9), 904–920 (1985). Scholar
  77. 77.
    Kadhim, M.A., Afshar Alam, M., Kaur, H.: A multi-intelligent agent for knowledge discovery in database (MIAKDD): cooperative approach with domain expert for rules extraction. In: Huang, D.-S., Jo, K.-H., Wang, L. (eds.) ICIC 2014. LNCS (LNAI), vol. 8589, pp. 602–614. Springer, Cham (2014). Scholar
  78. 78.
    Palit, I., Phelps, S., Ng, W.L.: Can a zero-intelligence plus model explain the stylized facts of financial time series data? In: Proceedings of the Eleventh International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), vol. 2, pp. 653–660. International Foundation for Autonomous Agents and Multiagent Systems, Valencia (2012)Google Scholar
  79. 79.
    Zhang, X.F., Wang, G.J., Meng, G.W.: Theory of truth degree based on the interval interpretation of first-order fuzzy predicate logic formulas and its application. Fuzzy Syst. Math. 20(2), 8–12 (2006)MathSciNetzbMATHGoogle Scholar
  80. 80.
    Wang, X.Z., An, S.F.: Research on learning weights of fuzzy production rules based on maximum fuzzy entropy. J. Comput. Res. Dev. 43(4), 673–678 (2006)CrossRefGoogle Scholar
  81. 81.
    Zhu, G.J., Xia, Y.M.: Research and practice of frame knowledge representation. J. Yunnan Univ. (Nat. Sci. Ed.) 28(S1), 154–157 (2006)Google Scholar
  82. 82.
    Zeng, Z.: Construction of knowledge service system based on semantic web. J. China Soc. Sci. Tech. Inf. 24(3), 336–340 (2005)MathSciNetGoogle Scholar
  83. 83.
    Castells, P., Fernandez, M., Vallet, D.: An adaptation of the vector-space model for ontology-based information retrieval. IEEE Trans. Knowl. Data Eng. 19(2), 261–272 (2007). Scholar
  84. 84.
    Keikha, M., Razavian, N.S., Oroumchian, F., Razi, H.S.: Document representation and quality of text: an analysis. In: Berry, M.W., Castellanos, M. (eds.) Survey of Text Mining II. Springer, London (2008). Scholar
  85. 85.
    Dudycz, H.: A topics map as a visual representation of economic knowledge. Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu, Wrocław (2013). (in Polish)Google Scholar
  86. 86.
    Hofweber, T.: Logic and ontology. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy (Summer 2018 Edition) (2018).
  87. 87.
    Guarino, N., Oberle, D., Staab, S.: What is an ontology? In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. International Handbooks on Information Systems. Springer, Berlin, Heidelberg (2009). Scholar
  88. 88.
    Fensel, D.: Ontologies: Silver Bullet for Knowledge Management and Electronic Commerce. Springer, New York (2001). Scholar
  89. 89.
    Sowa, J.F.: Semantic Networks. Accessed 20 Oct 2017
  90. 90.
    Korczak, J., Dudycz, H., Dyczkowski, M.: Design of financial knowledge in dashboard for SME managers. In: Proceedings of the 2013 Federated Conference on Computer Science and Information Systems, Kraków, pp. 1111–1118 (2013)Google Scholar
  91. 91.
    Burger, W., Burge, M.J.: Principles of Digital Image Processing: Fundamental Techniques. Springer, London (2009). Scholar
  92. 92.
    Solomon, C.J., Breckon, T.P.: Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab. Wiley, Hoboken (2010)CrossRefGoogle Scholar
  93. 93.
    Murat Tekalp, A.: Digital Video Processing, 2nd edn. Prentice Hall, Upper Saddle River (2015)Google Scholar
  94. 94.
    Newmarch, J.: OpenMAX video processing on the Raspberry Pi. In: Newmarch, J. (ed.) Raspberry Pi GPU Audio Video Programming. Apress, Berkeley (2017)CrossRefGoogle Scholar
  95. 95.
    Lin, H.-P., Hsieh, H.-Y.: On using digital speech processing techniques for synchronization in heterogeneous teleconferencing. In: Bartolini, N., Nikoletseas, S., Sinha, P., Cardellini, V., Mahanti, A. (eds.) QShine 2009. LNICST, vol. 22, pp. 679–695. Springer, Heidelberg (2009). Scholar
  96. 96.
    Baker, J., et al.: Developments and directions in speech recognition and understanding, part 1. IEEE Signal Process. Mag. 26(3), 75–80 (2009)CrossRefGoogle Scholar
  97. 97.
    Baldoni, M., Baroglio, C., Patti, V., Rena, P.: From tags to emotions: ontology-driven sentiment analysis in the social semantic web. Intelligenza Artificiale 6(1), 41–54 (2012)Google Scholar
  98. 98.
    Potiopa, P.: Methods and tools for automatic text information processing and their use in the knowledge management process. Automatyka 15(2), 409–419 (2011). Scholar
  99. 99.
    Tomassen, S.L.: Semi-automatic generation of ontologies for knowledge-intensive CBR. Norwegian University of Science and Technology, Trondheim (2002)Google Scholar
  100. 100.
    Pham, L.V., Pham, S.B.: Information extraction for Vietnamese real estate advertisements. In: Fourth International Conference on Knowledge and Systems Engineering (KSE), Danang (2012)Google Scholar
  101. 101.
    Balke, W.T.: Introduction to information extraction: basic notions and current trends. Datenbank-Spektrum 12(2), 81–88 (2012)CrossRefGoogle Scholar
  102. 102.
    Konchady, M.: Text Mining Application Programming. Cengage Learning India Private Ltd., New Delhi (2009)Google Scholar
  103. 103.
    Duan, R., Zhang, M.: Design of web-based management information system for academic degree & graduate education. In: Wang, W., Li, Y., Duan, Z., Yan, L., Li, H., Yang, X. (eds.) QShine 2009. IFIP International Federation for Information Processing, vol. 252, pp. 218–226. Springer, Boston (2007). Scholar
  104. 104.
    Banko, M., Cafarella, M., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad (2007)Google Scholar
  105. 105.
    Bekkerman, R., McCallum, A.: Disambiguating web appearances of people in a social network. In: Proceedings of International Conference on World Wide Web (WWW), Chiba (2005)Google Scholar
  106. 106.
    Hassell, J., Aleman-Meza, B., Arpinar, I.B.: Ontology-driven automatic entity disambiguation in unstructured text. In: Cruz, I., et al. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 44–57. Springer, Heidelberg (2006). Scholar
  107. 107.
    Chaudhuri, S., Ganti, V., Xin, D.: Mining document collections to facilitate accurate approximate entity matching. In: Proceedings of International Conference on Very Large Data Bases (VLDB), vol. 2, no. 1, Lyon (2009)Google Scholar
  108. 108.
    Dong, X., Halevy, A., Madhavan, J.: Reference reconciliation in complex information spaces. In: Proceedings of ACM International Conference on Management of Data, Baltimore (2005)Google Scholar
  109. 109.
    Cimiano, P., Handschuh, S., Staab, S.: Towards the selfannotating web. In: Proceedings of International Conference on World Wide Web (WWW), New York (2004)Google Scholar
  110. 110.
    Stoica, E., Hearst, M., Richardson, M.: Automating creation of hierarchical faceted metadata structures. In: Proceedings of Human Language Technology Conference of the Association of Computational Linguistics, Rochester (2007)Google Scholar
  111. 111.
    Carlson, A., Betteridge, J., Wang, R.C.: Coupled semi-supervised learning for information extraction. In: WSDM 2010, 4–6 February, New York (2010)Google Scholar
  112. 112.
    Etzioni, O., et al.: Unsupervised named-entity extraction from the web: an experimental study. Artif. Intell. 165(1), 1–42 (2005)MathSciNetCrossRefGoogle Scholar
  113. 113.
    Nenkova, A., McKeown, K.: Automatic summarization. Found. Trends Inf. Retr. 5(2–3), 103–233 (2011)CrossRefGoogle Scholar
  114. 114.
    Clahsen, F., Harald, C.: Grammatical Processing in Language Learners. Appl. Psycholinguist. 27, 3–42 (2006)CrossRefGoogle Scholar
  115. 115.
    Goldberg, Y.: A primer on neural network models for natural language processing. J. Artif. Intell. Res. 57, 345–420 (2016)MathSciNetzbMATHCrossRefGoogle Scholar
  116. 116.
    Costa, F., Branco, A.: LXGram: a deep linguistic processing grammar for Portuguese. In: Pardo, T.A.S., Branco, A., Klautau, A., Vieira, R., de Lima, V.L.S. (eds.) PROPOR 2010. LNCS (LNAI), vol. 6001, pp. 86–89. Springer, Heidelberg (2010). Scholar
  117. 117.
    Pollard, C., Sag, I.: Head-Driven Phrase Structure Grammar. Chicago University Press and CSLI, Stanford (1994)Google Scholar
  118. 118.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. (CSUR) 34(1), 1–47 (2002)MathSciNetCrossRefGoogle Scholar
  119. 119.
    Wawer, A.: Mining opinion attributes from texts using multiple kernel learning. In: IEEE 11th International Conference on Data Mining Workshops, Vancouver (2011)Google Scholar
  120. 120.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing con-textual polarity: an exploration of features for phrase-level sentiment analysis. Comput. Linguist. 35(3), 399–433 (2009)CrossRefGoogle Scholar
  121. 121.
    Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. ACL, Stroudsburg (2004)Google Scholar
  122. 122.
    Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Stroudsburg (2002)Google Scholar
  123. 123.
    Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In: EMNLP, Stroudsburg (2003)Google Scholar
  124. 124.
    Riloff, E.: Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, Stroudsburg (2003)Google Scholar
  125. 125.
    Wiebe, J., Riloff, E.: Creating subjective and objective sentence classifiers from unannotated texts. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 486–497. Springer, Heidelberg (2005). Scholar
  126. 126.
    Michalski, R.S., Tecuci, G. (eds.): Machine Learning: A Multistrategy Approach, Volume IV. Morgan Kaufmann, ‎Burlington (1994)Google Scholar
  127. 127.
    Vetulani, Z., Vetulani G., Kochanowski, B.: Recent advances in development of a lexicon-grammar of Polish: PolNet 3.0. In: Calzolari, N., et al. (eds.) Proceedings of the Tenth International Conference on Language Resources and Evaluation, LREC 2016, pp. 2851–2854. European Language Resources Association, Paris (2016)Google Scholar
  128. 128.
    Jackson, R.G., et al.: Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project. BMJ Open 7, e012012 (2017). Scholar
  129. 129.
    Abramowicz, W., Bukowska, E., Filipowska, A.: Ensuring security through semantic monitoring of cyberspace. E-mentor 3(50), 11–17 (2013)Google Scholar
  130. 130.
    Baptista, P., Cunha, T.R., Gama, C., Bernardes, C.: A new and practical method to obtain grain size measurements in sandy shores based on digital image acquisition and processing. Sed. Geol. 282, 294–306 (2012)CrossRefGoogle Scholar
  131. 131.
    Juang, B.H., Rabiner, L.R.: Automatic speech recognition-a brief history of the technology development. Accessed 17 June 2017
  132. 132.
    Wilpon, J., Gilbert, M.E., Cohen, J.: The business of speech technologies. In: Benesty, J., Sondhi, M.M., Huang, Y.A. (eds.) Springer Handbook of Speech Processing. Springer, Heidelberg (2008). Scholar
  133. 133.
    Owoc, M.L., Ochmanska, M., Gladysz, T.: On principles of knowledge validation. In: Vermesan, A., Coenen, F. (eds.) Validation and Verification of Knowledge Based Systems. Springer, Boston (1999). Scholar
  134. 134.
    Suh, Y.H., Murray, T.J.: A tree-based approach for verifying completeness and consistency in rule-based systems. Expert Syst. Appl. 7(2), 199–220 (1994)CrossRefGoogle Scholar
  135. 135.
    Hernes, M., Sobieska-Karpińska, J.: Application of the consensus method in a multiagent financial decision support system. IseB 14(1), 167–185 (2016)CrossRefGoogle Scholar
  136. 136.
    Korczak, J., Hernes, M., Bac, M.: Risk avoiding strategy in multi-agent trading system. In: Proceedings of Federated Conference Computer Science and Information Systems (FedCSIS), Kraków, pp. 1131–1138 (2013)Google Scholar
  137. 137.
    Nguyen, N.T.: Advanced Methods for Inconsistent Knowledge Management. Springer, London (2008). Scholar
  138. 138.
    Mirkin, B.G., Shestakov, A.: Least square consensus clustering: criteria, methods, experiments. In: Serdyukov, P., et al. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 764–767. Springer, Heidelberg (2013). Scholar
  139. 139.
    Sobieska-Karpińska, J., Hernes, M.: Value of information in distributed decision support system. In: Pańkowska, M. (ed.) Infonomics for Distributed Business and Decision-Making Environments: Creating Information System Ecology. IGI Global, Hershey, New York (2009)Google Scholar
  140. 140.
    Albus, J.S., Barbera, A.J.: RCS: a cognitive architecture for intelligent multi-agent systems. Ann. Rev. Control 29(1), 87–99 (2005)CrossRefGoogle Scholar
  141. 141.
    Kollmann, S., Siafara, L.C., Schaat, S., Wendt, A.: Towards a cognitive multi-agent system for building control. Procedia Comput. Sci. 88, 191–197 (2016)CrossRefGoogle Scholar
  142. 142.
    Iantovics, B.: Cognitive medical multiagent systems. BRAIN. Broad Res. Artif. Intell. Neurosci. 1(1), 12–21 (2010). Happy BRAINew Year!Google Scholar
  143. 143.
    Acampora, G., Vitiello, A.: Learning of fuzzy cognitive maps for modelling gene regulatory networks through Big Bang-Big Crunch algorithm. In: 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Istanbul, pp. 1–6 (2015)Google Scholar
  144. 144.
    Glaser, N., Chevrier, V., Haton, J.P.: Multi-agent modeling for autonomous but cooperative robots. In: Proceedings of 1st DIMAS, Cracow, Poland, pp. 175–182 (1995)Google Scholar
  145. 145.
    Korczak, J., Lipiński, P.: Agent systems in decision support on the securities market. In: Stanek, S., Sroka, H., Paprzycki, M., Ganzha, M. (eds.) Rozwój informatycznych systemów wieloagentowych. Wydawnictwo Placet, Warszawa (2008)Google Scholar
  146. 146.
    Duch, W., Oentaryo, R.J., Pasquier, M.: Cognitive architectures: where do we go from here? In: Wang, P., Goertzel, P., Franklin, S. (eds.) Frontiers in Artificial Intelligence and Applications, vol. 171, pp. 122–136. IOS Press, Amsterdam (2008)Google Scholar
  147. 147.
    Goertzel, B., Wang, P.: Introduction: what is the matter here? In: Goertzel, B., Wang, P. (eds.) Foundations of Artificial General Intelligence. Atlantis Press, Paris (2012)Google Scholar
  148. 148.
    Duch, W.: Artificial Intelligence. Knowledge Representation II: Semantic Networks. Accessed 23 Jan 2017
  149. 149.
    Douglas Bernheim, B., Rangel, A.: Behavioural public economics. In: Durlauf, S.N., Blume, L.E. (eds.) The New Palgrave Dictionary of Economics, 2nd edn. Palgrave Macmillan, Basingstoke (2008)Google Scholar
  150. 150.
    Hawkins, J., Blakeslee, S.: On Intelligence: How a New Understanding of the Brain will Lead to the Creation of Truly Intelligent Machines. Times Books, New York (2004)Google Scholar
  151. 151.
    Hecht-Nielsen, R.: Confabulation Theory: The Mechanism of Thought. Springer, Heidelberg (2007). Scholar
  152. 152.
    Laird, J.E.: Extending the SOAR cognitive architecture. In: Wang, P., Goertzel, B., Franklin, S. (eds.) Frontiers in Artificial Intelligence and Applications, vol. 171 (2008)Google Scholar
  153. 153.
    Kieras, D., Meyer, D.E.: An overview of the EPIC architecture for cognition and performance with application to human-computer interaction. Hum.-Comput. Interac. 12, 391–438 (1997)CrossRefGoogle Scholar
  154. 154.
    Hofstadter, D.R., Mitchell, M.: The copycat project: a model of mental fluidity and analogy-making, chap. 5. In: Hofstadter, D. (ed.) The Fluid Analogies Research Group, Fluid Concepts and Creative Analogies. Basic Books, New York (1995)Google Scholar
  155. 155.
    Wang, P.: Rigid Flexibility. The Logic of Intelligence. Springer, Dordrecht (2006). Scholar
  156. 156.
    Langley, P.: An adaptive architecture for physical agents. In: Proceeding of the 2005 IEEE/WIC/ACM International Conference on Intelligent Agent Technology. IEEE Computer Society Press, Compiegne (2005)Google Scholar
  157. 157.
    Edelman, G.M.: Second Nature: Brain Science and Human Knowledge. Yale University Press, New Haven (2006)Google Scholar
  158. 158.
    Rohrer, B.: An implemented architecture for feature creation and general reinforcement learning. In: Workshop on Self-Programming in AGI Systems, Fourth International Conference on Artificial General Intelligence, Mountain View, CA. Accessed 01 Apr 2017
  159. 159.
    Anderson, J.R., Lebiere, C.: The Newell test for a theory of cognition. Behav. Brain Sci. 26, 587–601 (2003)Google Scholar
  160. 160.
    Sun, R., Zhang, X.: Top-down versus bottom-up learning in cognitive skill acquisition. Cogn. Syst. Res. 5, 63–89 (2004)CrossRefGoogle Scholar
  161. 161.
    Goertzel, B.: OpenCogBot: achieving generally intelligent virtual agent control and humanoid robotics via cognitive synergy. In: Proceedings of ICAI 2010, Beijing (2010)Google Scholar
  162. 162.
    Nestor, A., Kokinov, B.: Towards active vision in the DUAL cognitive architecture. Int. J. Inf. Theor. Appl. 11, 9–15 (2004)Google Scholar
  163. 163.
    Just, M.A., Varma, S.: The organization of thinking: what functional brain imaging reveals about the neuroarchitecture of complex cognition. Cogn. Affect. Behav. Neurosci. 7, 153–191 (2007)CrossRefGoogle Scholar
  164. 164.
    Goertzel, B., et al.: An integrative methodology for teaching embodied non-linguistic agents, applied to virtual animals in second life. In: Wang, P., Goertzel, B., Franklin, S. (eds.) Artificial General Intelligence 2008. IOS Press, Amsterdam (2008)zbMATHGoogle Scholar
  165. 165.
    Hensinger, A., Thome, M., Wright, T.: Cougaar: a scalable, distributed multi-agent architecture. In: IEEE International Conference on Systems, Man and Cybernetics (2004)Google Scholar
  166. 166.
    Cognitive Computing Research Group. Accessed 02 Nov 2017
  167. 167.
    Katarzyniak, R.: Priming the modal language of communication in agent systems. Akademicka Oficyna Wydawnicza EXIT (2007). (in Polish)Google Scholar
  168. 168.
    Pulvermuller, F.: The Neuroscience of Language. On Brain Circuits of Words and Serial Order. Cambridge University Press, Cambridge (2003)CrossRefGoogle Scholar
  169. 169.
    Hernes, M.: The semantic method for agents’ knowledge representation in the cognitive integrated management information system. In: Proceedings of Federated Conference Computer Science and Information Systems (FedCSIS), Łódź (2015)Google Scholar
  170. 170.
    Dalkir, K.: Knowledge Management in Theory and Practice. Elsevier Inc., Jordan Hill, Oxford (2005). p. 330Google Scholar
  171. 171.
    Snaider, J., McCall, R., Franklin, S.: The LIDA framework as a general tool for AGI. In: Schmidhuber, J., Thórisson, Kristinn R., Looks, M. (eds.) AGI 2011. LNCS (LNAI), vol. 6830, pp. 133–142. Springer, Heidelberg (2011). Scholar
  172. 172.
    Hernes, M.: Using cognitive agents for unstructured knowledge management in a business organization’s integrated information system. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, T.-P. (eds.) ACIIDS 2016. LNCS (LNAI), vol. 9621, pp. 344–353. Springer, Heidelberg (2016). Scholar
  173. 173.
    Nguyen, N.T.: Conflicts of ontologies – classification and consensus-based methods for resolving. In: Gabrys, B., Howlett, R.J., Jain, Lakhmi C. (eds.) KES 2006. LNCS (LNAI), vol. 4252, pp. 267–274. Springer, Heidelberg (2006). Scholar
  174. 174.
    Ferber, J.: Multi-agent concepts and methodologies. In: Phan, D., Amblard, F. (eds.) Agent-Based Modelling and Simulation in the Social and Human Sciences. Bardwell Press, Oxford (2007)Google Scholar
  175. 175.
    Rosenfeld, A., Agmon, N., Maksimov, O., Kraus, S.: Intelligent agent supporting human-multi-robot team collaboration. Artif. Intell. 252, 211–231 (2017)MathSciNetzbMATHCrossRefGoogle Scholar
  176. 176.
    Gaudel, R., Sebag, M.: Feature selection as a one-player game. In: ICML (2010)Google Scholar
  177. 177.
    Hemsley, G., Holm, A., Dodd, B.: Conceptual distance and word learning: patterns of acquisition in Samoan-English bilingual children. J. Child Lang. 40, 799–820 (2013). Scholar
  178. 178.
    Basheer, G.S., Ahmad, M.S., Tang, A.Y.C.: A framework for conflict resolution in multi-agent systems. In: Bǎdicǎ, C., Nguyen, N.T., Brezovan, M. (eds.) ICCCI 2013. LNCS (LNAI), vol. 8083, pp. 195–204. Springer, Heidelberg (2013). Scholar
  179. 179.
    Dyk, P., Lenar, M.: Applying negotiation methods to resolve conflicts in multi-agent environments. In: Zgrzywa, A. (ed.) Multimedia and Network Information Systems, MISSI 2006. PWr Publishing house, Wroclaw (2006)Google Scholar
  180. 180.
    Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Sierra, C., Wooldridge, M.: Automated negotiation: prospect, methods and challenge. J. Group Decis. Negot. 10(2), 199–215 (2001)CrossRefGoogle Scholar
  181. 181.
    Niazi, M., Hussain, A.: Agent-based computing from multi-agent systems to agent-based models: a visual survey. Scientometrics 89(2), 479–499 (2011)CrossRefGoogle Scholar
  182. 182.
    Kielar, P.M., Borrmann, A.: Auton. Agents Multi-Agent Syst. 32, 387 (2018). Scholar
  183. 183.
    Li, G., Whiteson, S., Knox, W.B., et al.: Auton. Agents Multi-Agent Syst. 32, 1 (2018). Scholar
  184. 184.
    Gabel, T., Riedmiller, M.: On a successful application of multi-agent reinforcement learning to operations research benchmarks. In: IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, Honolulu, pp. 68–75 (2007)Google Scholar
  185. 185.
    Doniec, A., Mandiau, R., Piechowiak, S., Espié, S.: A behavioral multi-agent model for road traffic simulation. Eng. Appl. Artif. Intell. 21(8), 1443–1454 (2008)CrossRefGoogle Scholar
  186. 186.
    Lecoutre, Ch., Saïs, L., Tabary, S., Vidal, V.: Reasoning from last conflict(s) in constraint programming. Artif. Intell. 173(18), 1592–1614 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  187. 187.
    Abbas, J.: Social software use in public libraries. In: Dumova, T., Fiordo, R. (eds.) Handbook of Research on Social Interaction Technologies and Collaboration Software: Concepts and Trends. IGI Global, Hershey, New York (2009)Google Scholar
  188. 188.
    Uden, L., Eardley, A.: The usability of social software. In: Dumova, T., Fiordo, R. (eds.) Handbook of Research on Social Interaction Technologies and Collaboration Software: Concepts and Trends. IGI Global, Hershey, New York (2009)Google Scholar
  189. 189.
    Mirkin, B., Shestakov, A.: A note on the effectiveness of the least squares consensus clustering. In: Aleskerov, F., Goldengorin, B., Pardalos, P.M. (eds.) Clusters, Orders, and Trees: Methods and Applications. SOIA, vol. 92, pp. 181–185. Springer, New York (2014). Scholar
  190. 190.
    Mercier-Laurent, E.: Knowledge management and risk management. In: Proceedings of Federated Conference on Computer Science and Information Systems (FedCSIS), Gdansk, pp. 1369–1373 (2016)Google Scholar
  191. 191.
    Domenach, F., Tayari, A.: Implications of axiomatic consensus properties. In: Lausen, B., Van den Poel, D., Ultsch, A. (eds.) Algorithms from and for Nature and Life. Studies in Classification Data Analysis and Knowledge Organization. Springer, Cham (2013)zbMATHGoogle Scholar
  192. 192.
    Castano, S., Ferrara, A., Montanelli, S.: Designing crowdsourcing tasks with consensus constraints. In: International Conference on Collaboration Technologies and Systems (CTS), Orlando, FL, pp. 97–103 (2016)Google Scholar
  193. 193.
    Kozierkiewicz-Hetmańska, A., Pietranik, M.: The knowledge increase estimation framework for ontology integration on the relation level. In: Nguyen, N.T., Papadopoulos, G.A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017. LNCS (LNAI), vol. 10448, pp. 44–53. Springer, Cham (2017). Scholar
  194. 194.
    Sobieska-Karpińska, J., Hernes, M.: Consensus determining algorithm in multiagent decision support system with taking into consideration improving agent’s knowledge. In: Federated Conference Computer Science and Information Systems (FedCSIS) (2012)Google Scholar
  195. 195.
    Condorcet, M.: Essai sur l’application de l’analyse a la probabilite des decisions rendues ala prularite des voix. Chelsea Published, no. 6, New York (1974)Google Scholar
  196. 196.
    Maleszka, M., Nguyen, N.T.: Integration computing and collective intelligence. Expert Syst. Appl. 42(1), 332–340 (2015)CrossRefGoogle Scholar
  197. 197.
    Barthlemy, J.P.: Dictatorial consensus function on n-trees. Math. Soc. Sci. 25, 59–64 (1992)MathSciNetCrossRefGoogle Scholar
  198. 198.
    McMorris, F.R., Powers, R.C.: The median function on weak hierarchies. DIMACS Ser. Discret. Math. Theoret. Comput. Sci. 37, 265–269 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  199. 199.
    Sobieska-Karpińska, J., Hernes, M.: The postulates of consensus determining in financial decision support systems. In: Proceedings of Federated Conference Computer Science and Information Systems (FedCSIS), Kraków (2013)Google Scholar
  200. 200.
    Hernes, M., Sobieska-Karpińska, J.: Susceptibility to consensus of conflict situation in intelligent multi-agent decision support system. In: Kubiak, B.F., Korowicki, A. (eds.) Information Management. Gdansk University Press, Gdańsk (2009)Google Scholar
  201. 201.
    Bytniewski, A., Hernes, M.: Algorithm for determining consensus in a situation of conflict of unstructured knowledge in distributed IT systems supporting management. Ekonometria 4(42), 153–164 (2013). (in Polish)Google Scholar
  202. 202.
    Hernes, M.: Deriving consensus for term frequency matrix in a cognitive integrated management information system. In: Núñez, M., Nguyen, N.T., Camacho, D., Trawiński, B. (eds.) ICCCI 2015. LNCS (LNAI), vol. 9329, pp. 503–512. Springer, Cham (2015). Scholar
  203. 203.
    Hamming, R.W.: Error detecting and error correcting codes. Bell Syst. Tech. J. 29(2), 147–160 (1950)MathSciNetCrossRefGoogle Scholar
  204. 204.
    Maleszka, M., Mianowska, B., Nguyen, N.T.: A method for collaborative recommendation using knowledge integration tools and hierarchical structure of user profiles. Knowl.-Based Syst. 47, 1–13 (2013)CrossRefGoogle Scholar
  205. 205.
    Truong, H.B., Nguyen, N.T.: A multi-attribute and multi-valued model for fuzzy ontology integration on instance level. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ACIIDS 2012. LNCS (LNAI), vol. 7196, pp. 187–197. Springer, Heidelberg (2012). Scholar
  206. 206.
    Truong, H.B., Quach, X.H.: An overview of fuzzy ontology integration methods based on consensus theory. In: van Do, T., Thi, H.A.L., Nguyen, N.T. (eds.) Advanced Computational Methods for Knowledge Engineering. AISC, vol. 282, pp. 217–227. Springer, Cham (2014). Scholar
  207. 207.
    Nguyen, Q.U., Duong, T.H., Kang, S.: Solving conflict on collaborative knowledge via social networking using consensus choice. In: Nguyen, N.-T., Hoang, K., Jȩdrzejowicz, P. (eds.) ICCCI 2012. LNCS (LNAI), vol. 7653, pp. 21–30. Springer, Heidelberg (2012). Scholar
  208. 208.
    Jung, J.J., Nguyen, N.T.: Consensus choice for reconciling social collaborations on semantic wikis. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS (LNAI), vol. 5796, pp. 472–480. Springer, Heidelberg (2009). Scholar
  209. 209.
    Sobecki, J.: Hybrid adaptation of web-based systems user interfaces. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3038, pp. 505–512. Springer, Heidelberg (2004). Scholar
  210. 210.
    Jajuga, K.: Managing risk and investment in small business. In: Porada-Rochoń, M., Mifsud, J., Pittella, G. (eds.) Managing a Small Business in the Contemporary Environment, pp. 175–194 (2012)Google Scholar
  211. 211.
    Korczak, J., Hernes, M., Bac, M.: Performance evaluation of decision-making agents’ in the multi-agent system. In: Proceedings of Federated Conference Computer Science and Information Systems (FedCSIS), Warszawa, pp. 1177–1184 (2014)Google Scholar
  212. 212.
    Hernes, M., Chojnacka-Komorowska, A., Matouk, K.: Analysis of text documents in a multi-agent integrated management system. In: Porębska-Miąc, T. (ed.) Systemy Wspomagania Organizacji. Wydawnictwo Uniwersytetu Ekonomicznego w Katowicach, Katowice (2015). (in Polish)Google Scholar
  213. 213.
    Hernes, M., Chojnacka-Komorowska, A., Matouk, K.: External environment scanning using cognitive agents. In: Nguyen, N.T., Papadopoulos, George A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017. LNCS (LNAI), vol. 10448, pp. 342–350. Springer, Cham (2017). Scholar
  214. 214.
    Bartusiak, R., Kajdanowicz, T.: Sentiment analysis based on collaborative data for Polish language. In: Luo, Y. (ed.) CDVE 2015. LNCS, vol. 9320, pp. 216–219. Springer, Cham (2015). Scholar
  215. 215.
    Piasecki, M.: Polish tagger TaKIPI: rule based construction and optimisation. Task Q. 11(1–2), 151–167 (2007)Google Scholar
  216. 216.
    Sokołowska, W., Hossa, T., Fabisz, K., Abramowicz, W., Kubaczyk, M.: Sentiment analysis as a source of gaining competitive advantage on the electricity markets. J. Electron. Sci. Technol. 13(3), 229–236 (2015)Google Scholar
  217. 217.
    Russel, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson, London (2009)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Wrocław University of EconomicsWrocławPoland

Personalised recommendations