Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Domain Modeling

  • Chapter
  • First Online:
Cognitive Tutor

Part of the book series: Advanced Technologies and Societal Change ((ATSC))

  • 186 Accesses

Abstract

Acquiring and representing data in ITS is a time-consuming and challenging endeavor that requires considerable expertise. This has been the subject of a flurry of studies on artificial intelligence. It is the purpose of this chapter to examine various approaches and procedures. As a result of domain knowledge engineering, a major epistemological dilemma has arisen, which is discussed in detail in the first section of this work. Following that, a range of knowledge representation languages considers factors such as expressiveness, inferential power, cognition plausibility, and pedagogical emphasis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Brachman, R.J., Levesque, H.J.: Knowledge Representation and Reasoning. Morgan Kaufmann, San Francisco (2004). ISBN 978-1-55860-932-7

    Google Scholar 

  2. Nawaz, M.S., Hassan, M, Shaukat, S.: Impact of knowledge management practices on firm performance: testing the mediation role of innovation in the manufacturing sector of Pakistan. Pak. J. Commer. Soc. Sci. 8(1), 99–111. Clancey, B.: Acquiring, representing and evaluating a competence model of diagnostioc strategy. Technical Report: CS-TR-85–1067, Stanford University (1985)

    Google Scholar 

  3. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, Upper Saddle River (2009)

    MATH  Google Scholar 

  4. Woolf, B.: Building Intelligent Interactive Tutors: Student-centered Strategies for Revolutionizing E-learning. Morgan Kaufmann, San Francisco (2008)

    Google Scholar 

  5. Wenger, E.: Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge. Morgan Kaufmann Publishers Inc., Los Altos (1987)

    Google Scholar 

  6. Corbett, A.T., Koedinger, K.R., Anderson, J.R.: Intelligent tutoring systems. In: Helander, T.K., Landauer, P. (eds.) Handbook of Human-Computer Interaction. Elsevier Science, Amsterdam (1997)

    Google Scholar 

  7. Piaget, J.: The Principles of Genetic Epistemology. Routledge, London (1997)

    Google Scholar 

  8. Gagne, R.M.: The Conditions of Learning, 4th edn. Holt Rinehart and Winston, New York (1985)

    Google Scholar 

  9. Winograd, T., Fernando, F.: Understanding Computers and Cognition: A New Foundation for Design. Ablex, Norwood (1986)

    MATH  Google Scholar 

  10. Merrill, D., Twitchell, D.: Instructional Design Theory. Educational Technology Publication Inc., Englewood Cliff (1994)

    Google Scholar 

  11. Anderson, J.R.: The Architecture of Cognition. Lawrence Erlbaum Associates Inc., NJ (1996)

    Google Scholar 

  12. Collins, A.M., Quillian, R.: Retrieval time from semantic memory. J. Verbal Learn. Verbal Behav. 8, 240–247 (1969)

    Article  Google Scholar 

  13. Minsky, M.: A Framework for representing knowledge. In: Winston, P.H. (ed.) The Psychology of Computer Vision. McGraw-Hill, New York (1975)

    Google Scholar 

  14. Sowa, J.F.: Conceptual Structures: Information Processing in Mind and Machine. Addison Wesley, Reading (1984)

    Google Scholar 

  15. Ramoni, M.F., Stefanelli, M., Magnani, L., Barosi, G.: An epistemological framework for medical knowledge-based systems. IEEE Trans. Syst. Man Cybern. 22, 1–14 (1992)

    Article  Google Scholar 

  16. Bloom, B.S.: Taxonomy of Educational Objectives: the Classification of Educational Goals. McKay D, New York (1975)

    Google Scholar 

  17. Merrill, M.D.: An introduction to instructional transaction theory. Educ. Technol. 31, 45–53 (1991)

    Google Scholar 

  18. Kyllonen, P.C., Shute, V.J.: A Taxonomy of Learning Skills. Brooks Air Force Base, TX, AFHRL Report No. TP-87–39 (1988)

    Google Scholar 

  19. De Jong, T., Ferguson-Hessler, M.G.M.: Types and qualities of knowledge. Educ. Psychol. 31(2), 105–113 (1996)

    Google Scholar 

  20. Schraw, G.J., Olafson, L.: Assessing teachers’ epistemological and ontological world views. In: Khine, M.S. (ed.) Knowing, Knowledge and Beliefs, pp. 24–44. Springer, Heidelberg (2008)

    Google Scholar 

  21. Peters, J.M., Gray, A.: Epistemological perspectives in research on teaching and learning science. Educ. Res. 5(1), 1–27 (2006)

    Google Scholar 

  22. Brachman, R.: On the epistemological status of semantic networks. In: Findler, N.V. (ed.) Associative Networks: Representation and Use of Knowledge by Computers. Academic Press, New York (1979)

    Google Scholar 

  23. Hickman, F.R., Killin, J.L., Land, L., Mulhall, T., Porter, D., Taylor, R.: Analysis for knowledge based systems. In: A Practical Guide to the KADS Methodology. Ellis Horwood, New York (1989)

    Google Scholar 

  24. Brown, J.S., Burton, R.R.: SOPHIE: a pragmatic use of artificial intelligence in CAI. In: Proceedings of the 1974 annual ACM conference, vol. 2, pp. 571–579 (1974)

    Google Scholar 

  25. Nwana, H.: Intelligent tutoring systems: an overview. Artif. Intell. Rev. 4, 251–277 (1990)

    Article  Google Scholar 

  26. Clancey, W.J.: Overview of Guidon. J. Comput.-Based Instr. 10(1–2), 8–15 (1982)

    Google Scholar 

  27. Corbett, A.T., Koedinger, K.R., Anderson, J.R.: Intelligent tutoring systems. In: Helander, T.K., Landauer, P. (eds.) Handbook of Human-Computer Interaction. Elsevier Science, Amsterdam (1997)

    Google Scholar 

  28. Corbett, A.T., Anderson, J.R.: The LISP intelligent tutoring system: research in skill acquisition. In: Larkin, J., Chabay, R., Scheftic, C. (eds.) Computer Assisted Instruction and Intelligent Tutoring Systems: Establishing Communication and Collaboration. Erlbaum, Hillsdale (1992)

    Google Scholar 

  29. Beck, J., Mia, S., Haugsjaa, E.: Applications of AI in education. Crossroads Spec. Issue Artif. Intell. 3(1), 11–15 (1996)

    Google Scholar 

  30. Lesgold, A., Lajoie, S., Bunzo, M., Eggan, G.: SHERLOCK: A coached practice environment for an electronics troubleshooting job. In: Larkin, J., Chabay, R. (eds.) Computer assisted instruction and intelligent tutoring systems: shared issues and complementary approaches, pp. 201–238. Lawrence Erlbaum Associates, Hillsdale (1992)

    Google Scholar 

  31. Nawaz, M.S., Hassan, M., Shaukat, S.: Impact of knowledge management practices on firm performance: testing the mediation role of innovation in the manufacturing sector of Pakistan. Pak. J. Commer. Soc. Sci. 8(1), 99–111 (2014)

    Google Scholar 

  32. Lytras, M., Pouloudi, A., Poulymenakou, A.: Knowledge management convergence—expanding learning frontiers. J. Knowl. Manage. 6(1), 40–51 (2002)

    Google Scholar 

  33. Wu, I.L., Chen, J.L.: Knowledge management driven firm performance: the roles of business process capabilities and organizational learning. J. Knowl. Manage. 18(6), 1141–1164 (2014)

    Google Scholar 

  34. Nonaka, I.: Dynamic theory of organizational knowledge creation. Organ. Sci. 5(1), 14–37 (1994)

    Article  Google Scholar 

  35. Pathirage, C., Amaratunga, D., Haigh, R.: Tacit knowledge and organizational performance: construction industry perspective. J. Knowl. Manag. 11(1), 115–126 (2007)

    Article  Google Scholar 

  36. O’Dell, C., Grayson, J.: If Only We Knew What We Know. The Free Press, New York (1998)

    Google Scholar 

  37. Baumard, P.: Tacit knowledge in professional firms: the teaching of firms in very puzzling situations. J. Knowl. Manag. 6(2), 135–151 (2002)

    Article  Google Scholar 

  38. Borges, R.: Tacit knowledge sharing between IT workers: the role of organizational culture, personality, and social environment. Manag. Res. Rev. 36(1), 89–108 (2013)

    Article  Google Scholar 

  39. Nonaka, I.: The Knowledge Creating Company, pp. 21–46. Harward Business Review on Knowledge Management, USA (1998)

    Google Scholar 

  40. Polanyi, M.: The logic of tacit inference. Philosophy 41(155), 1–18 (1966)

    Article  Google Scholar 

  41. Polanyi, M.: Personal Knowledge. Towards a Post Critical Philosophy, Routledge, London (1998)

    Google Scholar 

  42. Berente, N.: (2007), http://filer.case.edu/~nxb41/tacit.html

  43. Hodgkin, R.: Michael Polanyi—prophet of life, the universe and everything. Times High. Educ. Suppl. 15–21 (1991)

    Google Scholar 

  44. Linde, C.: Narrative and social tacit knowledge. J. Knowl. Manag. 5(2), 160–170 (2001)

    Article  Google Scholar 

  45. Chen, L., Mohamed, S.: The strategic importance of tacit knowledge management activities in construction. Constr. Innov. 10(2), 138–163 (2010)

    Google Scholar 

  46. Nonaka, I., Takeuchi, H.: Theory of organisational knowledge creation. In: Theoksessa, H., Takeuchi, H., Nonaka, I. (ed.) Hitutsobashi on Knowledge Management, (2004)

    Google Scholar 

  47. Jones, S., Eden, C.: Modelling in marketing: explicating subjective knowledge. Eur. J. Mark. (1981)

    Google Scholar 

  48. Cropper, S., Eden, C., Ackermann, F.: Keeping sense of accounts using computer-based cognitive maps. Soc. Sci. Comput. Rev. 8(3), 345–366 (1990)

    Article  Google Scholar 

  49. Huff, A.S.: Mapping Strategic Thought. Wiley (1990)

    Google Scholar 

  50. Bougon, M.G.: Uncovering cognitive maps: the self-Q technique. Beyond Method: Strat. Soc. Res. 173487 (1983)

    Google Scholar 

  51. Ambrosini, V., Bowman, C.: Tacit knowledge: some suggestions for operationalization. J. Manage. Stud. 38(6), 811–829 (2001)

    Article  Google Scholar 

  52. Rabionet, S.E.: How I learned to design and conduct semi-structured interviews: an ongoing and continuous journey. Qual. Rep. 16(2), 563–566 (2011)

    Google Scholar 

  53. Guarino, N., Welty, C.: Evaluating ontological decisions with OntoClean. Commun. ACM 45(2), 61–65 (2002)

    Article  Google Scholar 

  54. Fortuna, B., Grobelnik, M., Mladenic, D.: OntoGen: semi-automatic ontology editor. In: HCI International 2007, Beijing (2007)

    Google Scholar 

  55. Cimiano, P., Völker, J.: Text2Onto–a framework for ontology learning and data-driven change discovery. In: Montoyo, A., Muńoz, R., Métais, E. (eds.) NLDB 2005. LNCS, vol. 3513, pp. 227–238. Springer, Heidelberg (2005)

    Google Scholar 

  56. Zouaq, A., Nkambou, R.: Enhancing learning objects with an ontology-based memory. IEEE Trans. Knowl. Data Eng. 21(6), 881–893 (2009)

    Article  Google Scholar 

  57. Zouaq, A., Nkambou, R.: Evaluating the generation of domain ontologies in the knowledge puzzle project. IEEE Trans. Knowl. Data Eng. 21(11), 1559–1572 (2009)

    Article  Google Scholar 

  58. Velardi, P., Navigli, R., Cuchiarelli, A., Neri, F.: Evaluation of ontolearn, a methodology for automatic population of domain ontologies. In: Buitelaar, P., Cimiano, P., Magnini, B. (eds.) Ontology Learning from Text: Methods, Applications and Evaluation. IOS Press, Amsterdam (2005)

    Google Scholar 

  59. Buitelaar, P., Cimiano, P., Magnini, B.: Ontology learning from text: an overview. In: Buitelaar, P., Cimiano, P., Magnini, B. (eds.) Ontology Learning from Text: Methods, Evaluation and Applications. Frontiers in Artificial Intelligence and Applications Series, vol. 123. IOS Press, Amsterdam (July 2005)

    Google Scholar 

  60. Sabou, M.: Learning web service ontologies: an automatic extraction method and its evaluation. In: Buitelaar, P., Cimiano, P., Magnini, B. (eds.) Ontology Learning from Text: Methods, Evaluation and Applications. IOS Press, Amsterdam (2005)

    Google Scholar 

  61. Moldovan, D.I., Girju, R.C.: An interactive tool for the rapid development of knowledge bases. Int. J. Artif. Intell. Tools (IJAIT) 10(1–2) (2001)

    Google Scholar 

  62. Reinberger, M.-L., Spyns, P.: Unsupervised text mining for the learning of DOGMA inspired ontologies. In: Buitelaar, P., Cimiano, P., Magnini, B. (eds.) Ontology Learning from Text: Methods, Applications and Evaluation. Advances in Artificial Intelligence, pp. 29–43. IOS Press, Amsterdam (2005)

    Google Scholar 

  63. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5), 515–523 (1988)

    Article  Google Scholar 

  64. Frantzi, K., Ananiadou, S., Tsuji, J.: The c-value/nc-value method of automatic recognition for multi-word terms. In: Nikolaou, C., Stephanidis, C. (eds.) ECDL 1998. LNCS, vol. 1513, pp. 585–604. Springer, Heidelberg (1998)

    Google Scholar 

  65. Harris, Z.: Distributional structure. Word 10(23), 146–162 (1954)

    Google Scholar 

  66. Almuhareb, A., Poesio, M.: Attribute-based and value-based clustering: an evaluation. In: Proceedings of EMNLP, Barcelona (July 2004)

    Google Scholar 

  67. Lin, D., Pantel, P.: Induction of semantic classes from natural language text. In: Proceedings Of SIGKDD 2001, San Francisco, CA, pp. 317–322 (2001)

    Google Scholar 

  68. Sahlgren, M.: The word-space model: using distributional analysis to represent syntagmatic and paradigmatic relations between words in high-dimensional vector spaces. Ph.D. dissertation, Department of Linguistics, Stockholm University (2006)

    Google Scholar 

  69. Lin, D.: Automatic identification of non-compositional phrases. In: Proceeding of ACL 1999, pp. 317–324 (1999)

    Google Scholar 

  70. Widdows, D., Dorow, B.: A graph model for unsupervised lexical acquisition. In: 19th International Conference on Computational Linguistics, Taipei, Taiwan, pp. 1093–1099 (2002)

    Google Scholar 

  71. Hearst, M., Schutze, H.: Customizing a lexicon to better suit a computational task. In: ACL SIGLEX Workshop, Columbus, Ohio (1993)

    Google Scholar 

  72. Hindle, D.: Noun classification from predicate-argument structures. In: Proceeding of ACL 1990, Pittsburg, Pennsylvania, pp. 268–275 (1990)

    Google Scholar 

  73. Lin, D.: Automatic retrieval and clustering of similar words. In: Proceeding of COLING-ACL 1998, Montreal, Canada, pp. 768–774 (1998)

    Google Scholar 

  74. Cimiano, P.: Ontology learning attributes and relations. In: Ontology Learning and Population from Text, pp. 185–231. Springer, Heidelberg (2006)

    Google Scholar 

  75. Fortuna, B., Mladevic, D., Grobelnik, M.: Visualization of text document corpus. In: ACAI 2005 Summer School (2005)

    Google Scholar 

  76. Navigli, R., Velardi, P., Cucchiarelli, A., Neri, F.: Quantitative and qualitative evaluation of the OntoLearn ontology learning system. In: Proceeding of the 20th International Conference on Computational Linguistics, Switzerland (2004)

    Google Scholar 

  77. Almuhareb, A., Poesio, M.: Finding concept attributes in the web. In: Proceeding of the Corpus Linguistics Conference, Birmingham (July 2005)

    Google Scholar 

  78. Guarino, N.: Concepts, attributes and arbitrary relations: some linguistic and ontological criteria for structuring knowledge base. Data Knowl. Eng. 8, 249–261 (1992)

    Article  Google Scholar 

  79. Pustejovsky, J.: The Generative Lexicon. MIT Press, Cambridge (1995)

    Google Scholar 

  80. Poesio, M., Almuhareb, A.: Identifying concept attributes using a classifier. In: Proceeding Of the ACL Workshop on Deep Lexical Acquisition, pp. 18–27. Association for Computational Linguistics, Ann Arbor (2005)

    Google Scholar 

  81. Woods, W.A.: What’s in a link: foundations for semantic networks. In: Bobrow, D.G., Collins, A.M. (eds.) Representation and Understanding: Studies in Cognitive Science, pp. 35–82. Academic Press, New York (1975)

    Chapter  Google Scholar 

  82. Poesio, M., Almuhareb, A.: Extracting concept descriptions from the Web: the importance of attributes and values. In: Buitelaar, P., Cimiano, P. (eds.) Bridging the Gap between Text and Knowledge, pp. 29–44. IOS Press, Amsterdam (2008)

    Google Scholar 

  83. Ravi, S., Pasca, M.: Using structured text for large-scale attribute extraction. In: Proceeding Of the 17th ACM Conference on Information and Knowledge Management, CIKM-2008 (2008)

    Google Scholar 

  84. Corcho, O., Gómez-Pérez, A.: A Roadmap to ontology specification languages. In: Dieng, R., Corby, O. (eds.) EKAW 2000. LNCS (LNAI), vol. 1937, pp. 80–96. Springer, Heidelberg (2000)

    Google Scholar 

  85. Hearst, M.: Automatic acquisition of hyponyms from large text corpora. In: Proceeding of the Fourteenth International Conference on Computational Linguistics, Nantes, pp. 539–545 (1992)

    Google Scholar 

  86. Cimiano, P., Volker, J.: Towards large-scale, open-domain and ontology-based named entity classification. In: Proceeding of RANLP 2005, Borovets, Bulgaria, pp. 166–172 (2005b)

    Google Scholar 

  87. Snow, R., Jurafsky, D., Ng, A.Y.: Learning syntactic patterns for automatic hypernym discovery. In: Advances in Neural Information Processing Systems (NIPS 2004), Vancouver, British Columbia (2004)

    Google Scholar 

  88. Cimiano, P., Staab, S.: Learning by googling. ACM SIGKDD Explor. 6(2), 24–33 (2004)

    Google Scholar 

  89. Maedche, A., Staab, S.: Ontology learning for the semantic web. IEEE Intell. Syst. 16(2), 72–79 (2001)

    Article  Google Scholar 

  90. Etzioni, O., Cafarella, M., Downey, D., Kok, S., Popescu, A.-M., Shaked, T., Soderland, S., Weld, D.S., Yates, A.: Web-scale information extraction in knowitall (preliminary results). In: Proceeding of the 13th World Wide Web Conference, pp. 100–109 (2004)

    Google Scholar 

  91. Buitelaar, P., Olejnik, D., Sintek, M.: A protege plug-in for ontology extraction from text based on linguistic analysis. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870. Springer, Heidelberg (2003)

    Google Scholar 

  92. Maedche, A., Pekar, V., Staab, S.: Ontology learning part one—on discovering taxonomic relations from the web. In: Web Intelligence, pp. 301–322. Springer, Heidelberg (2002)

    Google Scholar 

  93. Bisson, G., Nedellec, C., Canamero, L.: Designing clustering methods for ontology building—the Mo’K workbench. In: Proceeding of the ECAI Ontology Learning Workshop, pp. 13–19 (2000)

    Google Scholar 

  94. Caraballo, S.A.: Automatic construction of a hypernym-labeled noun hierarchy from text. In: Proceeding of the 37th Annual Meeting of the Association for Computational Linguistics, pp. 120–126 (1999)

    Google Scholar 

  95. Faure, D., Nedellec, C.: A corpus-based conceptual clustering method for verb frames and ontology. In: Velardi, P. (ed.) Proceeding of the LREC Workshop on Adapting lexical and corpus resources to sublanguages and applications, pp. 5–12 (1998)

    Google Scholar 

  96. Widdows, D.: Unsupervised methods for developing taxonomies by combining syntactic and statistical information. In: Proceeding of HLT-NAACL, pp. 197–204 (2003)

    Google Scholar 

  97. Baker, C.F., Fillmore, C.J., Lowe, J.B.: The berkeley framenet project. In: Proceeding of the COLING-ACL, Montreal, Quebec, Canada (1998)

    Google Scholar 

  98. Kipper, K., Dang, H.D., Palmer, M.: Class-Based Construction of a Verb Lexicon. In: Proceeding of AAAI-2000 Seventeenth National Conference on Artificial Intelligence, pp. 691–696 (2000)

    Google Scholar 

  99. Cimiano, P., Wenderoth, J.: Automatically learning qualia structures from the web. In: Proceeding of the ACL Workshop on Deep Lexical Acquisition, pp. 28–37 (2005)

    Google Scholar 

  100. Claveau, V.: Acquisition automatique de lexiques sémantiques pour la recherche d’information. Thèse de doctorat, Université de Rennes-1, Rennes (2003)

    Google Scholar 

  101. Cimiano, P., Wenderoth, J.: Automatic acquisition of ranked qualia structures from the web. In: Proceeding of the 45th Annual Meeting of the Association for Computational Linguistics (ACL), Prague (2007)

    Google Scholar 

  102. Craven, M., Di Pasquo, D., Freitag, D., McCallum, A., Mitchell, T.M., Nigam, K., Slattery, S.: Learning to construct knowledge bases from the World Wide Web. Artif. Intell. 1–2(118), 69–113 (2000)

    Article  MATH  Google Scholar 

  103. Schlobach, S., Olsthoorn, M., de Rijke, M.: Type checking in open-domain question answering. In: Proceeding of European Conference on Artificial Intelligence, pp. 398–402. IOS Press, Amsterdam (2004)

    Google Scholar 

  104. Amardeilh, F., Laublet, P., Minel, J.-L.: Document annotation and ontology population from linguistic extractions. In: Proceeding of the 3rd International Conference on Knowledge Capture, Banff, Alberta, Canada, pp. 161–168 (2005)

    Google Scholar 

  105. Tanev, H., Magnini, B.: Weakly supervised approaches for ontology population. In: Proceeding of EACL 2006, Trento, Italy, pp. 3–7 (2006)

    Google Scholar 

  106. Fleischman, M.: Automated subcategorization of named entities. In: 39th Annual Meeting of the ACL. In: Student Research Workshop, Toulouse, France (July 2001)

    Google Scholar 

  107. Shamsfard, M., Barforoush, A.A.: An Introduction to HASTI: an ontology learning system. In: Proceeding of 6th Conference on Artificial Intelligence and Soft Computing (ASC 2002), Banff, Alberta, Canada (2002)

    Google Scholar 

  108. Shamsfard, M., Barforoush, A.A.: The state of the art in ontology learning: a framework for comparison. Knowl. Eng. Rev. 18(4), 293–316 (2003)

    Article  Google Scholar 

  109. Volker, J., Haase, P., Hitzler, P.: Learning expressive ontologies. In: Buitelaar, P., Cimiano, P. (eds.) Ontology Learning and Population: Bridging the Gap between Text and Knowledge. Frontiers in Artificial Intelligence and Applications, vol. 167, pp. 45–69. IOS Press, Amsterdam (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, Telangana, India

    Ninni Singh & Vinit Kumar Gunjan

  2. Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

Authors
  1. Ninni Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vinit Kumar Gunjan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jacek M. Zurada
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Singh, N., Gunjan, V.K., Zurada, J.M. (2022). Domain Modeling. In: Cognitive Tutor. Advanced Technologies and Societal Change. Springer, Singapore. https://doi.org/10.1007/978-981-19-5197-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-5197-8_2

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-5196-1

  • Online ISBN: 978-981-19-5197-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation