Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 69 tok/s
Gemini 2.5 Pro 52 tok/s Pro
GPT-5 Medium 28 tok/s Pro
GPT-5 High 28 tok/s Pro
GPT-4o 75 tok/s Pro
Kimi K2 191 tok/s Pro
GPT OSS 120B 402 tok/s Pro
Claude Sonnet 4.5 34 tok/s Pro
2000 character limit reached

Method for the semantic indexing of concept hierarchies, uniform representation, use of relational database systems and generic and case-based reasoning (1910.01539v3)

Published 3 Oct 2019 in cs.AI

Abstract: This paper presents a method for semantic indexing and describes its application in the field of knowledge representation. Starting point of the semantic indexing is the knowledge represented by concept hierarchies. The goal is to assign keys to nodes (concepts) that are hierarchically ordered and syntactically and semantically correct. With the indexing algorithm, keys are computed such that concepts are partially unifiable with all more specific concepts and only semantically correct concepts are allowed to be added. The keys represent terminological relationships. Correctness and completeness of the underlying indexing algorithm are proven. The use of classical relational databases for the storage of instances is described. Because of the uniform representation, inference can be done using case-based reasoning and generic problem solving methods.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (31)
  1. The University of Manchester Website - FaCT++ reasoner. http://owl.cs.manchester.ac.uk/tools/fact/. Accessed: 2019-07-26.
  2. W3C RDFDAML+OIL Website. https://www.w3.org/TR/daml+oil-reference, Dec. 2001. Accessed: 2019-07-26.
  3. Loom Project Website. https://www.isi.edu/isd/LOOM/, July 2007. Accessed: 2019-07-27.
  4. W3C RDF Schema Website. https://www.w3.org/TR/rdf-schema/, Feb. 2014. Accessed: 2019-07-26.
  5. W3C RDF Website. https://www.w3.org/RDF/, Feb. 2014. Accessed: 2019-07-29.
  6. CYC Homepage. https://www.cyc.com/documentation/ontologists-handbook/cyc-basics/syntax-cycl/, 2019. Accessed: 2019-07-26.
  7. Semafora Systems Website - Produkt OntoStudio. http://www.semafora-systems.com, Dec. 2019. Accessed: 2019-07-27.
  8. F. Baader. What’s new in Description Logics. Informatik-Spektrum, 34:434–442, 2011.
  9. The Description Logic Handbook - Theory, Implementation and Applications. Cambridge University Press, 2003.
  10. F. Baader and B. Hollunder. KRIS: Knowledge Representation and Inference System, System Description. ACM SIGART Bulletin, 2(3):8–14, June 1991.
  11. An essential hybrid reasoning system: knowledge and symbol level accounts of KRYPTON. In In Proceedings of the 9th International Joint Conference on Artificial Intelligence, pages 532–539. Morgan Kaufmann, 1985.
  12. An overview of the KL-ONE Knowledge Representation System. Cognitive Science, 9(2):171–216, 1985.
  13. W. W. Cohen and H. Hirsh. Learning the CLASSIC Description Logic: Theoretical and Experimental Results. In In Principles of Knowledge Representation and Reasoning: Proceedings of the Fourth International Conference (KR94, pages 121–133. Morgan Kaufmann, 1994.
  14. Extending LiteMat toward RDFS++. In LASCAR Workshop on Large Scale RDF Analytics, Protoroz, Slovenia, June 2019.
  15. OIL: An Ontology Infrastructure for the Semantic Web. Intelligent Systems, IEEE, 16:38–45, 04 2001.
  16. Knowledge representation and diagnostic inference using Bayesian networks in the medical discourse. CoRR, abs/1909.08549, 2019.
  17. T. R. Gruber. Ontolingua: A Mechanism to Support Portable Ontologies. Knowledge Systems Laboratory - Stanford University, 1992.
  18. Optimizing Similarity Search in the M-Tree. Datenbanksysteme für Business, Technologie und Web (BTW 2017), pages 485–504, March 2017.
  19. Reducing the Distance Calculations when Searching an M-Tree. Datenbank-Spektrum 17(2), pages 155–167, 2017.
  20. V. Haarslev and R. Möller. RACER System Description. In R. Goré, A. Leitsch, and T. Nipkow, editors, International Joint Conference on Automated Reasoning, IJCAR’2001, June 18-23, Siena, Italy, pages 701–705. Springer-Verlag, 2001.
  21. XR-Tree: Indexing XML Data for Efficient Structural Joins. In In ICDE, pages 253–263, 2003.
  22. M. Kifer and G. Lausen. F-logic: A Higher-order Language for Reasoning About Objects, Inheritance, and Scheme. In Proceedings of the 1989 ACM SIGMOD International Conference on Management of Data, SIGMOD ’89, pages 134–146, New York, NY, USA, 1989. ACM.
  23. J. Kolodner. Case-Based Reasoning. Morgan Kaufmann, San Francisco, Calif, 1993.
  24. W. Oertel and U. Petersohn. Managing Episodes, Cases, Concepts, and Rules - an Integration Approach for Medical Problem Solving. In D. Aha and J. Daniels, editors, Case-Based Reasoning Integrations: Papers from the 1998 Workshop, Madison, Wisconsin, USA, Menlo Park, 1998. AAAI Press.
  25. Causal statistical modeling and calculation of distributions by classificatory features. arXiv 2019, in preparation.
  26. Problem solving with Concept- and Case-based Reasoning. In T. Burczynski, W. Cholewa, and W. Moczulski, editors, Proceedings of the Conference Developments in Artifical Intelligence Methods, pages 259–270, 2009. AI-METH Series on Artifcial Intelligence Methods, Gliwice, Poland.
  27. CML: The commonKADS conceptual modelling language. In L. Steels, G. Schreiber, and W. Van de Velde, editors, A Future for Knowledge Acquisition, pages 1–25, Berlin, Heidelberg, 1994. Springer Berlin Heidelberg.
  28. S. Staab and R. Studer. Handbook on Ontologies. Springer Science & Business Media, Berlin Heidelberg, 2013.
  29. Ontosaurus: A Tool for Browsing and Editing Ontologies. http://ksi.cpsc.ucalgary.ca/KAW/KAW96/swartout/ontosaurus_demo.html, 2019. Accessed: 2019-07-27.
  30. S. Tobies. A PSpace-algorithm for ALCQI-satisfiability. LTCS-Report LTCS-99-09, LuFG Theoretical Computer Science, RWTH Aachen, Germany, 1999. See http://www-lti.informatik.rwth-aachen.de/Forschung/Papers.html.
  31. Handbuch der medizinischen Informatik, chapter Medizinische Dokumentation, Terminologie und Linguistik, pages 89–144. Carl Hanser Verlag, München, Wien, 2., vollständig neu bearbeitete auflage edition, 2005.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up