Skip to main content

Towards Semantic Knowledge Base Definition

  • Conference paper
  • First Online:
Biomedical Engineering and Neuroscience (BCI 2018)

Abstract

The paper is a wide survey over one of the knowledge representation and processing solutions, namely knowledge bases. Due to current terminological inconsistency authors propose the complex definition of knowledge base in the field of knowledge representation. The overview of the most common reality description methods is provided in order to discuss its usefulness in knowledge base design. Authors not only give the definition of the knowledge base but also prove its completeness on the example of Semantic Knowledge Base project. The project aims at developing the general domain knowledge base using ontology base and semantic networks as basic knowledge representation methods.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Notes

  1. 1.

    Web Ontology Language – a family of knowledge representation languages endorsed by the World Wide Web Consortium (W3C).

  2. 2.

    Hypergraph’s edges are called hyperedges; they can be incident to any number of vertices.

References

  1. Aagesen, G., Krogstie, J.: BPMN 2.0 for modeling business processes. In: Handbook on Business Process Management 1, pp. 219–250. Springer, Heidelberg (2015)

    Google Scholar 

  2. Barnes, W.H.F.: The doctrine of connotation and denotation. Mind 54, 254–263 (1945)

    Article  Google Scholar 

  3. Brown, M.S.: Theaetetus: knowledge as continued learning. J. Hist. Philos. 7(4), 359–379 (1969)

    Article  Google Scholar 

  4. Collins, A.M., Quillian, M.R.: Retrieval time from semantic memory. J. Verbal Learn. Verbal Behav. 8(2), 240–247 (1969)

    Article  Google Scholar 

  5. Dudycz, H.: Approach to the conceptualization of an ontology of an early warning system. In: Information Systems in Management XI, Data Bases, Distant Learning, and Web Solutions Technologies, pp. 29–39 (2011)

    Google Scholar 

  6. Duhl, J., Damon, C.: A performance comparison of object and relational databases using the sun benchmark. In: ACM SIGPLAN Notices, vol. 23, pp. 153–163. ACM (1988)

    Google Scholar 

  7. Feng, S., Bose, R., Choi, Y.: Learning general connotation of words using graph-based algorithms. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1092–1103. Association for Computational Linguistics (2011)

    Google Scholar 

  8. French, R.M.: The chinese room: Just say “no!” In: Proceedings of the Cognitive Science Society, vol. 1 (2000)

    Google Scholar 

  9. Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquisition 5(2), 199–220 (1993)

    Article  Google Scholar 

  10. Hao, C.: Research on knowledge model for ontology-based knowledge base. In: 2011 International Conference on Business Computing and Global Informatization (BCGIN), pp. 397–399. IEEE (2011)

    Google Scholar 

  11. Hendrix, G.G.: Encoding knowledge in partitioned networks. In: Associative Networks: Representation and Use of Knowledge by Computers, pp. 51–92 (1979)

    Google Scholar 

  12. Joyce, D.: An identification and investigation of software design guidelines for using encapsulation units. J. Syst. Softw. 7(4), 287–295 (1987)

    Article  Google Scholar 

  13. Kalantari, R., Bryant, C.: Comparing the performance of object and object relational database systems on objects of varying complexity. In: Data Security and Security Data, pp. 72–83 (2012)

    Google Scholar 

  14. Korzynska, A., Zdunczuk, M.: Clustering as a method of image simplification. Inf. Technol. Biomed. 47, 345 (2008)

    Article  Google Scholar 

  15. Krótkiewicz, M.: Asocjacyjny metamodel baz danych. Definicja formalna oraz analiza porównawcza metamodeli baz danych (eng. Association-Oriented Database Metamodel). No. z. 444 in Studia i Monografie, Oficyna Wydawnicza Politechniki Opolskiej, Opole (2016)

    Google Scholar 

  16. Krótkiewicz, M.: Association-oriented database model - n-ary associations. Int. J. Softw. Eng. Knowl. Eng. 27, 281 (2017)

    Article  Google Scholar 

  17. Krótkiewicz, M., Wojtkiewicz, K., Jodłowiec, M., Pokuta, W.: Semantic knowledge base: quantiers and multiplicity in extended semantic networks module. In: Knowledge Engineering and Semantic Web: 7th International Conference, KESW 2016, Prague, Czech Republic, 21–23 September 2016, Proceedings. Springer, Cham (2016)

    Google Scholar 

  18. Lange, K.J.: Complexity and structure in formal language theory. Fundam. Inf. 25(3, 4), 327–352 (1996)

    MathSciNet  MATH  Google Scholar 

  19. Lin, K.J.: Consistency issues in real-time database systems. In: Proceedings of the Twenty-Second Annual Hawaii International Conference on System Sciences, 1989. Vol. II: Software Track, vol. 2, pp. 654–661. IEEE (1989)

    Google Scholar 

  20. Macewen, G.H., Martin, T.P.: Abstraction hierarchies in top-down design. J. Syst. Softw. 2(3), 213–224 (1981)

    Article  Google Scholar 

  21. OMG: Unified Modeling Language\(^{\rm TM}\) (UML®) Version 2.5 (2013). http://www.omg.org/spec/UML/2.5/www.omg.org/spec/UML/2.5/Beta2/PDF/

  22. Przepiórkowski, A.: Slavonic information extraction and partial parsing. In: Proceedings of the Workshop on Balto-Slavonic Natural Language Processing: Information Extraction and Enabling Technologies, pp. 1–10. Association for Computational Linguistics (2007)

    Google Scholar 

  23. Przepiórkowski, A., Górski, R.L., Lewandowska-Tomaszyk, B., Lazinski, M.: Towards the national corpus of polish. In: LREC (2008)

    Google Scholar 

  24. Przepiórkowski, A., Marcińczuk, M., Degórski, Ł.: Dealing with small, noisy and imbalanced data. In: Text, Speech and Dialogue, pp. 169–176. Springer, Heidelberg (2008)

    Google Scholar 

  25. Schärli, N., Black, A.P., Ducasse, S.: Object-oriented encapsulation for dynamically typed languages. In: ACM SIGPLAN Notices, vol. 39, pp. 130–149. ACM (2004)

    Google Scholar 

  26. Seligman, L.J., Kerschberg, L.: Knowledge-base/database consistency in a federated multidatabase environment. In: Proceedings of the Third International Workshop on Research Issues in Data Engineering: Interoperability in Multidatabase Systems, RIDE-IMS 1993, pp. 18–25. IEEE (1993)

    Google Scholar 

  27. Soutou, C.: Modeling relationships in object-relational databases. Data Knowl. Eng. 36(1), 79–107 (2001)

    Article  MATH  Google Scholar 

  28. Stroustrup, B.: What is object-oriented programming? IEEE Softw. 5(3), 10–20 (1988)

    Article  MathSciNet  Google Scholar 

  29. Su, H., Bouridane, A., Crookes, D.: Scale adaptive complexity measure of 2D shapes. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 2, pp. 134–137. IEEE (2006)

    Google Scholar 

  30. Voigt, J., Irwin, W., Churcher, N.: Class encapsulation and object encapsulation: an empirical study (2010)

    Google Scholar 

  31. Wislicki, J., Kuliberda, K., Adamus, R., Subieta, K.: Relational to object-oriented database wrapper solution in the data grid architecture with query optimisation issues. Int. J. Bus. Process Integ. Manag. 2(1), 17–25 (2007)

    Article  Google Scholar 

  32. Zhangbing, L., Wujiang, C.: A new algorithm for data consistency based on primary copy data queue control in distributed database. In: 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN), pp. 207–210. IEEE (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Krystian Wojtkiewicz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Krótkiewicz, M., Wojtkiewicz, K., Jodłowiec, M. (2018). Towards Semantic Knowledge Base Definition. In: Hunek, W., Paszkiel, S. (eds) Biomedical Engineering and Neuroscience. BCI 2018. Advances in Intelligent Systems and Computing, vol 720. Springer, Cham. https://doi.org/10.1007/978-3-319-75025-5_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-75025-5_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75024-8

  • Online ISBN: 978-3-319-75025-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics