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Semantische Netze

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Zusammenfassung

Netzartige Repräsentationsformate, die man unter dem Begriff semantische Netze zusammenfaßt, gehen zurück auf Modelle menschlichen Gedächtnisses der Kognitionspsychologie (ein detaillierter historischer Abriß findet sich in Kap.3.6). Diesen Modellen liegt aufgrund experimenteller Untersuchungen die Annahme zugrunde, daß Konzepte, die semantisch miteinander in Beziehung stehen, durch Strukturen repräsentiert sind, die in einer geeigneten Art und Weise (die in den Modellen nicht näher festgelegt zu werden braucht) miteinander verbunden sind. Diese Verbindungen, die man sich formal als zweistellige Relationen vorstellen kann, heißen assoziative Beziehungen. Modelle menschlichen Gedächtnisses, die assoziative Beziehungen vorsehen, heißen Assoziationsmodelle. Wird eine Konzeptrepräsentation durch einen Erinnerungsvorgang aktiviert, dann sehen solche Assoziationsmodelle die Ausbreitung der Aktivierung über alle Verbindungen, die von der betroffenen Konzeptrepräsentation ausgehen, vor. Dadurch erhalten alle Konzepte, die mit dem primär angesprochenen Konzept in einer assoziativen Beziehung stehen, eine Aktivierungserhöhung und rücken damit näher an die Erinnerungsschwelle oder überschreiten sie.

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Literatur

  1. Abrial, J.R.: Data Semantics. In: J.W. Klimbie, K.L. Koffeman (eds): Data Base Management. Amsterdam: North-Holland, 1974, pp.1–59.

    Google Scholar 

  2. Anderson, J.R.: FRAN: A Simulation Model of Free Recall. In: G.H. Bower (ed): The Psychology of Learning and Motivation. New York: Academic Press, 1972, pp.315–378.

    Google Scholar 

  3. Anderson, J.R.: The Architecture of Cognition. Cambridge/Mass.: Harvard University Press, 1983.

    Google Scholar 

  4. Anderson, J.R. / Bower, G.H.: Human Associative Memory. Washington: V.H. Winston & Sons, 1973.

    Google Scholar 

  5. Andrews, P.B.: An Introduction to Mathematical Logic and Type Theory: To Truth Through Proof. Orlando: Academic Press, 1986.

    MATH  Google Scholar 

  6. Boley, H.: Directed Recursive Labelnode Hypergraphs: A New RepresentationLanguage. In: Artificial Intelligence, Vol.9, 1977, pp.49–85.

    Article  MATH  Google Scholar 

  7. Brachman, R.J.: What’s in a Concept: Structural Foundations for Semantic Networks. In: International Journal of Man-Machine Studies, Vol.9, 1977, pp.127–152.

    Article  Google Scholar 

  8. Brachman, R.J.: What IS-A Is and Isn’t: An Analysis of Taxonomic Links in Semantic Networks. In: IEEE Computer, Vol.16, No.10, 1983, pp.30–36.

    Article  Google Scholar 

  9. Brachman, R.J. / Levesque H.J. (eds): Readings in Knowledge Representation. Los Altos/Cal.: Morgan Kaufmann, 1985.

    MATH  Google Scholar 

  10. Chan, M.C. / Garner, B.J. / Tsui, E.: Recursive Modal Unification for Reasoning with Knowledge Using a Graph Representation. In: Knowledge-Based Systems, Vol.1, No.2, 1988, pp.94–104.

    Article  Google Scholar 

  11. Chandrasekaran, B. / Goel, A. / Allemang, D.: Connectionism and InformationProcessing Abstractions. In: AI Magazine, Vol.9, No.4, 1988, pp.24–34.

    Google Scholar 

  12. Charniak, E.: Passing Markers: A Theory of Contextual Influence in Language Comprehension. In: Cognitive Science, Vol.7, 1983, pp.171–190.

    Article  Google Scholar 

  13. Charniak, E.: A Neat Theory of Marker Passing. In: Proc. of the National Conf. on Artificial Intelligence, 1986, pp.584–588.

    Google Scholar 

  14. Chen, P.P.: The Entity-Relationship Model: Towards a Unified View of Data. In: ACM Transactions on Database Systems, Vol.1, No.1, 1976, pp.9–36.

    Article  Google Scholar 

  15. Cohen, P.R. / Kjeldsen, R.: Information Retrieval by Constrained Spreading Activation in Semantic Networks. In: Information Processing & Management, Vol.23, No.4, 1987, pp.255–268.

    Article  Google Scholar 

  16. Collins, A.M. / Loftus, E.F.: A Spreading-Activation Theory of Semantic Processing. In: Psychological Review, Vol.82, No.6, 1975, pp.407–428.

    Article  Google Scholar 

  17. Cottrell, G.W.: Parallelism in Inheritance Hierarchies with Exceptions. In: Int. Joint Conf. on Artificial Intelligence, 1985, pp.194–202.

    Google Scholar 

  18. Deliyanni, A. / Kowalski, R.A.: Logic and Semantic Networks. In: Communications of the ACM, Vol.22, No.3, 1979, pp.184–192.

    Article  MATH  Google Scholar 

  19. Dilger, W. / Womann, W.: Semantic Networks as Abstract Data Types. In: Proc. Int. Joint Conf. on Artificial Intelligence, 1983, pp.321–324.

    Google Scholar 

  20. Di Manzo, M. / Ricci, F. / Batistoni, A. / Ferrari, C.: A Framework for Object Functional Descriptions. In: Advances in Artificial Intelligence (CIIAM 86) (=Proc. of the 2nd Int. Conf. on Artificial Intelligence, 1986). London: Kogan Page, 1987, pp.23–33.

    Google Scholar 

  21. Dörfler, W. / Mühlbacher, J.: Graphentheorie für Informatiker. Berlin: W. de Gruyter, 1973.

    Book  MATH  Google Scholar 

  22. Elmasri, R. / Navathe, S.B.: Fundamentals of Database Systems. Redwood City/Cal.: Benjamin Cummings, 1989.

    MATH  Google Scholar 

  23. Etherington, D.W.: More On Inheritance Hierarchies with Exceptions. Default Theories and Inferential Distance. In: Proc. National Conf. on Artificial Intelligence, 1987, pp.352–357.

    Google Scholar 

  24. Etherington, D.W. / Reiter, R.: On Inheritance Hierarchies With Exceptions. In: Proc. National Conf. on Artificial Intelligence, 1983, pp.104–108.

    Google Scholar 

  25. Fahlman, S.E.: NETL: A System for Representing and Using Real-World Knowledge. Cambridge/Mass.: The MIT Press, 1979.

    MATH  Google Scholar 

  26. Fahlman, S.E.: Representing Implicit Knowledge. In: G.E. Hinton, J.A. Anderson (eds): Parallel Models of Associative Memory. Hillsdale/N.J.: Lawrence Erlbaum, 1981, pp.145–159.

    Google Scholar 

  27. Fahlman, S.E. / Hinton, G.E.: Connectionist Architectures for Artificial Intelligence. In: IEEE Computer, Vol.20, No.1, 1987, pp.100–109.

    Article  Google Scholar 

  28. Fahlman, S.E. / Touretzky, D.S. / Roggen, W. van: Cancellation in a Parallel Semantic Network. In: Proc. Int. Joint Conf. on Artificial Intelligence, 1981, pp.257–263.

    Google Scholar 

  29. Feldman, J.A.: Connectionist Representation of Concepts. In: D. Waltz, J.A. Feldman (eds): Connectionist Models and Their Implications: Readings from Cognitive Science. Norwood/N.J.: Ablex, 1988, pp.341–363.

    Google Scholar 

  30. Feldman, J.A. / Ballard, D.H.: Connectionist Models and Their Properties. In: Cognitive Science, Vol.6, 1982, pp.205–254.

    Article  Google Scholar 

  31. Fillmore, C.J.: The Case for Case. In: E. Bach, R.T. Harms (eds): Universals in Linguistic Theory. New York: Holt, Rinehart & Winston, 1968, pp.1–88.

    Google Scholar 

  32. Frisch, A.M. / Allen, J.F.: Knowledge Retrieval as Limited Inference. In: D.W. Loveland (ed): 6th Conference on Automated Deduction. Berlin: Springer-Verlag, 1982, pp.274–291.

    Chapter  Google Scholar 

  33. Froidevaux, C. / Kayser, D.: Inheritance in Semantic Networks and Default Logic. In: Ph. Smets, A. Mamdani, D. Dubois, H. Prade (eds): Non-Standard Logics for Automated Reasoning. London: Academic Press, 1988, pp.179–212.

    Google Scholar 

  34. Hammer, M. / McLeod, D.: Database Description with SDM: A Semantic Database Model. In: ACM Transactions on Database Systems, Vol.6, No.3, 1981, pp.351–386.

    Article  Google Scholar 

  35. Hayes, P.J.: On Semantic Nets, Frames and Associations. In: Proc. Int. Joint Conf. on Artificial Intelligence, 1977, pp.99–107.

    Google Scholar 

  36. Hayes-Roth, F.: The Role of Partial and Best Matches in Knowledge Systems. In: D.A. Waterman, F. Hayes-Roth (eds): Pattern-Directed Inference Systems. New York: Academic Press, 1978, pp.557–574.

    Google Scholar 

  37. Hendler, J.A.: Integrating Marker-Passing and Problem-Solving. A Spreading Activation Approach to Improved Choice in Planning. Hillsdale/N.J.: Lawrence Erlbaum, 1988.

    Google Scholar 

  38. Hendrix, G.G.: Encoding Knowledge in Partitioned Networks In: N.V. Findler (ed): Associative Networks. New York: Academic Press, 1979, pp.51–92.

    Google Scholar 

  39. Hinton, G.E.: Implementing Semantic Networks in Parallel Hardware. In: G.E. Hinton, J.A. Anderson (eds): Parallel Models of Associative Memory. Hillsdale/N.J.: Lawrence Erlbaum, 1981, pp.161–187.

    Google Scholar 

  40. Hinton, G.E. / Anderson, J.A. (eds): Parallel Models of Associative Memory. Hillsdale/N.J.: Lawrence Erlbaum, 1981.

    Google Scholar 

  41. Hinton, G.E. / McClelland, J.L. / Rumelhart, D.E.: Distributed Representations. In: D.E. Rumelhart, J.L. McClelland (eds): Parallel Distributed Processing. Explorations in the Microstructure of Cognition. Volume 1: Foundations. Cambridge/Mass.: The MIT Press, 1986, pp.77–109.

    Google Scholar 

  42. Horty, J.F. / Thomason, R.H. / Touretzky, D.S.: A Skeptical Theory of Inheritance in Nonmonotonic Semantic Networks. In: Proc. National Conf. on Artificial Intelligence, 1987, pp.357–363.

    Google Scholar 

  43. Israel, D.J.: Interpreting Network Formalisms. In: Computers and Mathematics with Applications, Vol.9, No.1, 1983, pp.1–13.

    Article  MathSciNet  MATH  Google Scholar 

  44. Janas, J.M. / Schwind, C.B.: Extensional Semantic Networks: Their Representation, Application, and Generation. In: N.V. Findler (ed): Associative Networks. New York: Academic Press, 1979, pp.267–302.

    Google Scholar 

  45. Kemke, C.: Der Neuere Konnektionismus. Ein Überblick. In: InformatikSpektrum, Band 11, Heft 3, 1988, pp.143–162.

    Google Scholar 

  46. Klimesch, W.: Struktur und Aktivierung des Gedächtnisses. Das Vernetzungsmodell: Grundlagen und Elemente einer übergreifenden Theorie. Bern: Verlag Hans Huber, 1988.

    Google Scholar 

  47. Levesque, H. / Mylopoulos, J.: A Procedural Semantics for Semantic Networks. In: N.V. Findler (ed): Associative Networks. New York: Academic Press, 1979, pp.93–120.

    Google Scholar 

  48. Maida, A.S. / Shapiro, S.C.: Intensional Concepts in Propositional Semantic Networks. In: Cognitive Science, Vol.6, No.4, 1982, pp.291–330. (auch in (Brachman/Levesque 85))

    Article  Google Scholar 

  49. McCarthy, J.: First Order Theories of Individual Concepts and Propositions. In: J.E. Hayes, D. Michie, L.I. Mikulich (eds): Machine Intelligence 9. Chichester: Ellis Horwood, 1979, pp.129–147. (auch in (Brachman/Levesque 85))

    Google Scholar 

  50. McSkimin, J.R. / Minker, J.: A Predicate Calculus Based Semantic Network for Deductive Searching. In: N.V. Findler (ed): Associative Networks. New York: Academic Press, 1979, pp.205–238.

    Google Scholar 

  51. Mylopoulos, J. / Cohen, P. / Borgida, A. / Sugar, L.: Semantic Networks and the Generation of Context. In: Proc. Int. Joint Conf. on Artificial Intelligence, 1975, pp.134–142.

    Google Scholar 

  52. Nagao, M. / Tsujii, J.: S-Net: A Foundation for Knowledge Representation Languages. In: Proc. Int. Joint Conf. on Artificial Intelligence, 1979, pp.617–624.

    Google Scholar 

  53. Norman, D.A. / Rumelhart, D.E.: Strukturen des Wissens. Wege der Kognitionsforschung. Stuttgart: Klett-Cotta, 1978.

    Google Scholar 

  54. Papalaskaris, M.A.I. Schubert, L.K.: Parts Inference: Closed and Semi-Closed Partitioning Graphs. In: Proc. Int. Joint Conf. on Artificial Intelligence, 1981, pp.304–309.

    Google Scholar 

  55. Peckham, J. / Maryanski, F.: Semantic Data Models. In: ACM Computing Surveys, Vol.20, No.3, 1988, pp.153–189.

    Article  MATH  Google Scholar 

  56. Pulman, S.G.: Word Meaning and Belief. London: Croom Helm, 1983.

    Google Scholar 

  57. Quillian, M.R.: Word Concepts: A Theory and Simulation of Some Basic Semantic Capabilities. In: Behavioral Science, Vol.12, 1967, pp.410–430. (auch in (Brachman/Levesque 85))

    Article  Google Scholar 

  58. Raphael, B.: SIR: Semantic Information Retrieval. In: M. Minsky (ed): Semantic Information Processing. Cambridge/Mass.: The MIT Press, 1968, pp.33–145.

    Google Scholar 

  59. Reitman, W.R.: Cognition and Thought. An Information-Processing Approach. New York: John Wiley, 1965.

    Google Scholar 

  60. Rich, E. : Artificial Intelligence. New York: McGraw-Hill, 1983.

    Google Scholar 

  61. Ritter, H. / Martinetz, T. / Schulten, K.: Neuronale Netze. Eine Einführung in die Neuroinformatik selbstorganisierender Netzwerke. Bonn: Addison-Wesley, 1990.

    Google Scholar 

  62. Rumelhart, D.E. / Lindsay, R.H. / Norman, D.A. : A Process Model for LongTerm Memory. In: E. Tulving, W. Donaldson (eds): Organization of Memoryi. New York: Academic Press, 1972, pp.197–246.

    Google Scholar 

  63. Schank, R.C.: Conceptual Information Processing. Amsterdam: North-Holland, 1975.

    MATH  Google Scholar 

  64. Schank, R.C. / Rieger, C.J. III : Inference and the Computer Understanding of Natural Language. In: Artificial Intelligence, Vol.5, No.4, 1974, pp.373–412.

    Article  MATH  Google Scholar 

  65. Schubert, L.K.: Problems with Parts. In: Proc. Int. Joint Conf. on Artificial Intelligence, 1979, pp.778–784.

    Google Scholar 

  66. Schubert, L.K. / Goebel, R.G. / Cercone, N.J.: The Structure and Organization of a Semantic Net for Comprehension and Inference. In: N.V. Findler (ed): Associative Networks. New York: Academic Press, 1979, pp.121–175.

    Google Scholar 

  67. Schwarcz, R.M. / Burger, J.F./ Simmons, R.F.: A Deductive Question-Answerer for Natural Language Inference. In: Communications of the ACM, Vol.13, No.3, 1970, pp.167–183.

    Article  MATH  Google Scholar 

  68. Schwartz, F. / Rouse, R.O.: The Activation and Recovery of Associations. In: Psychological Issues, Vol.III, No.1, 1961(=Monograph 9).

    Google Scholar 

  69. Shapiro, S.C.: A Net Structure for Semantic Information Storage and Retrieval. In: Proc. Int. Joint Conf. on Artificial Intelligence, 1971, pp.512–523.

    Google Scholar 

  70. Shapiro, S.C. / Rapaport, W.J.: SNePS Considered as a Fully Intensional Propositional Semantic Network. In: N. Cercone, G. McCalla (eds): The Knowledge Frontier. Essays in the Representation of Knowledge. New York: Springer-Verlag, 1987, pp.262–315.

    Google Scholar 

  71. Shapiro, S.C. / Woodmansee, G.H.: A Net Structure Based Relational Question Answerer: Description and Examples. In: Proc. Int. Joint Conf. on Artificial Intelligence, 1969, pp.325–346.

    Google Scholar 

  72. Shastri, L.: Semantic Networks: An Evidential Formalization and its Connectionist Realization. London: Pitman, 1988.

    MATH  Google Scholar 

  73. Simmons, R.F. / Bruce, B.C.: Some Relations Between Predicate Calculus and Semantic Net Representations of Discourse. In: Proc. Int. Joint Conf. on Artificial Intelligence, 1971, pp.524–530.

    Google Scholar 

  74. Smith, J.M. / Smith, D.C.P.: Database Abstraction: Aggregation and Generalization. In: ACM Transactions on Database Systems, Vol.2, No.2, 1977, pp.105–133.

    Article  Google Scholar 

  75. Soergel, D.: Indexing Languages and Thesauri: Construction and Maintenance. New York: Wiley, 1974.

    Google Scholar 

  76. Sowa, J.F.: Definitional Mechanisms for Conceptual Graphs. In: V. Claus, H. Ehrig, G. Rozenberg (eds): Graph-Grammars and Their Application to Computer Science and Biology. Berlin: Springer-Verlag, 1979, pp.426–439.

    Chapter  Google Scholar 

  77. Sowa, J.F.: Conceptual Structures: Information Processing in Mind and Machine. Reading/Mass.: Addison-Wesley, 1984.

    MATH  Google Scholar 

  78. Thomason, R.H. / Horty, J.F. / Touretzky, D.S.: A Calculus for Inheritance in Monotonic Semantic Nets. In: Z.W. Ras, M. Zemankova (eds): Methodologies for Intelligent Systems. New York: North-Holland, 1987, pp.280–287.

    Google Scholar 

  79. Touretzky, D.S.: The Mathematics of Inheritance Systems. London: Pitman, 1986.

    MATH  Google Scholar 

  80. Tsichritzis, D.C. / Lochovsky, F.H.: Data Models. Englewood Cliffs/N.J.: Prentice-Hall, 1982.

    Google Scholar 

  81. Wettler, M.: Wissensrepräsentation: Typen und Modelle. In: I.S. Bator, W. Lenders, W. Putschke (eds): Computerlinguistik. Ein internationales Handbuch zur computergestützten Sprachforschung und ihrer Anwendungen. Berlin: W. de Gruyter, 1989, pp.317–336.

    Google Scholar 

  82. Wobcke, W.: A Global Theory of Inheritance. In: ECAI’88. Proceedings of the Eighth European Conference on Artificial Intelligence, 1988, pp.214–219.

    Google Scholar 

  83. Woods, W.A.: What’s in a Link: Foundations for Semantic Networks. In: D.G. Bobrow, A. Collins (eds): Representation and Understanding. New York: Academic Press, 1975, pp.35–82. (auch in (Brachman/Levesque 85))

    Google Scholar 

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Reimer, U. (1991). Semantische Netze. In: Einführung in die Wissensrepräsentation. Leitfäden der angewandten Informatik. Vieweg+Teubner Verlag, Wiesbaden. https://doi.org/10.1007/978-3-663-05970-7_3

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  • DOI: https://doi.org/10.1007/978-3-663-05970-7_3

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