Skip to main content
SpringerLink
Log in

Addressing the Ontology Acquisition Bottleneck Through Reverse Ontological Engineering

  • Ontology Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

The use of ontologies in knowledge engineering arose as a solution to the difficulties associated with acquiring knowledge, commonly referred to as the knowledge acquisition bottleneck. The knowledge-level model represented in an ontology provides a much more structured and principled approach compared with earlier transfer-of-symbolic-knowledge approaches but brings with it a new problem, which can be termed the ontology-acquisition (and maintenance) bottleneck. Each ontological approach offers a different structure, different terms and different meanings for those terms. The unifying theme across approaches is the considerable effort associated with developing, validating and connecting ontologies. We propose an approach to engineering ontologies by retrospectively and automatically discovering them from existing data and knowledge sources in the organization. The method offered assists in the identification of similar and different terms and includes strategies for developing a shared ontology. The approach uses a human-centered, concept-based knowledge processing technique, known as formal concept analysis, to generate an ontology from examples. To assist classification of examples and to identify the salient features of the example, we use a rapid and incremental knowledge acquisition and representation technique, known as ripple-down rules. The method can be used as an alternative or complement to other approaches.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Baader F (1995) Computing a minimal representation of the subsumption lattice of all conjunctions of concepts defined in a terminology. In: Proceedings of the international symposium on knowledge retrieval, use, and storage for efficiency, KRUSE 95, Santa Cruz, CA, 1995

  2. Balzer R (1991) Tolerating inconsistency. In: Proceedings of 13th international conference on software engineering (ICSE-13), Austin, Texas, USA, 13–17 May 1991. IEEE Computer Society Press, pp 158–165

  3. Bareiss ER (1989) Exemplar-based knowledge acquisition: a unified approach to concept representation, classification, and learning. Academic, Boston

  4. Beydoun G, Hoffmann A (1997) Acquisition of search knowledge. In: Plaza E, Benjamins R (eds) Knowledge acquisition, modeling and management. 10th European Workshop, EKAW’97, Lecture notes in artificial intelligence 1319. Springer, Berlin Heidelberg New York, pp 1–16

  5. Cai Y, Cerone N, Han J (1991) Attribute-oriented induction in relational databases. In: Knowledge discovery in databases. AAAI/MIT Press

  6. Chalupsky H (2000) OntoMorph: a translation system for symbolic knowledge. In: Proceedings of knowledge representation 2000 (KR’00), Breckenridge, CO, pp 471–482

  7. Compton P, Edwards G, Kang B, Lazarus L, Malor R, Preston P, Srinivasan A (1992) Ripple down rules: turning knowledge acquisition into knowledge maintenance. Artif Knowl Med 4:463–475

    Article  Google Scholar 

  8. Deogun J, Raghavan V, Sever H (1998) Association mining and formal concept analysis. In: Proceedings of the 6th international workshop on rough sets, data mining and granular computing 1:335–338

  9. Easterbrook S, Nuseibeh B (1996) Using viewpoints for inconsistency management. BCSEEE Softw Eng J January 1996:31–43

    Article  Google Scholar 

  10. Edwards G (1996) Reflective expert systems in clinical pathology. MD Thesis, University of New South Wales

  11. Edwards G, Compton P, Malor R, Srinivasan A, Lazarus L (1993) PEIRS: a pathologist maintained expert system for the interpretation of chemical pathology. Rep Pathol 25:27–34

    Article  Google Scholar 

  12. Eriksson H, Fergerson RW, Shahar Y Musen MA (1999) Automatic generation of ontology. In: Proceedings of the 12th workshop on knowledge acquisition, modeling and management (KAW’99), 16–21 October 1999, Banff

  13. Fernandez Lopez M, Gomez-Perez A, Pazos Sierra A, Pazos Sierra J (1999) Building a chemical ontology using methontology and the ontology design environment. IEEE Intell Syst 14(1)

  14. Fortin S, Liu L, Goebel R (1996) Multi-level association rule mining: an object-oriented approach based on dynamic hierarchies. Technical Report TR 96-15, Department of Computing Science, University of Alberta

  15. Gaines BR, Compton P (1993) Induction of meta-knowledge about knowledge discovery. IEEE Trans Knowl Data Eng 5(6):990–992

    Article  Google Scholar 

  16. Ganter B (1988) Composition and decomposition of data. In: Bock H (ed) Classification and related methods of data analysis, pp 561–566. North-Holland, Amsterdam

  17. Ganter B, Wille R (1989) Conceptual scaling. In: Roberts F (ed) Applications of combinatorics and graph theory to the biological sciences. Springer, Berlin Heidelberg New York, pp 139–167

  18. Genesereth M, Fikes R (1992) Knowledge interchange format, Version 3.0 reference manual. Technical Report Logic-92-1, Computer Science Department, Stanford University

  19. Gomez-Perez A (1999) Evaluation of taxonomic knowledge in ontologies and knowledge bases. In: Proceedings of the 12th workshop on knowledge acquisition, modeling and management (KAW’99), 16–21 October 1999, Banff

  20. Gruninger M, Fox MS (1995) Methodology for the design and evaluation of ontologies. Workshop on basic ontological issues in knowledge sharing, 19–20 August, Montreal, Quebec

  21. Guha TV, Lenat DB (1990) CYC: a mid-term report. AI Mag 11(3):32–59

    Google Scholar 

  22. Hochman Z, Compton P, Blumenthal M, Preston P (1996) Ripple-down rules: a potential tool for documenting agricultural knowledge as it emerges. In: Proceedings of the 8th Australian Agronomy Conference, Toowoomba, 1996, pp 313–316

  23. Hovy E (1998) Combining and standardising large-scale, practical ontologies for machine translation and other uses. In: Proceedings of the 1st international conference on language resources and evaluation, Granada, Spain

  24. Humphreys BL, Lindberg DA (1993) The UMLS project: making the conceptual connection between users and the information they need. Bull Med Library Assoc 81(2):170

    Google Scholar 

  25. Jasper R, Uschold M (1999) A framework for understanding and classifying ontology applications. In: Proceedings of the 12th workshop on knowledge acquisition, modeling and management (KAW’99), 16–21 October, 1999, Banff

  26. Kang B, Compton P, Preston P (1995) Multiple classification ripple down rules: evaluation and possibilities. In: Proceedings of the 9th Banff knowledge acquisition for knowledge based systems workshop, 26 Feb–3 March 1995, Banff, pp 17.1–17.20

  27. Kang B, Yoshida K, Motoda H, Compton P (1997) Help desk with intelligent interface. Appl Artif Intell 11:611–631

    Article  Google Scholar 

  28. Karp PD (1993) A qualitative biochemistry and its application to the regulation of the trytophan operon. In: Hunter L (ed) Artificial intelligence and molecular biology. AAAI Press, Menlo Park, CA, pp 289–325

  29. Kent RE, Neuss C (1995) Creating a 3D web analysis and visualization environment. Comput Net ISDN Syst 28:109–117

    Article  Google Scholar 

  30. Kietz J-U, Maedche A, Volz R (2000) A method for semi-automatic ontology acquisition from a corporate intranet. WS “Ontologies and Text”, co-located with EKAW’2000, Juan-les-Pins, French Riviera, 2–6 October 2000. Knowledge Systems Laboratory (2001) http://www.ksl.stanford.edu/software/ontolingua/

  31. Kolodner J (1993) Case-based Reasoning. Morgan Kaufmann, San Mateo, CA

  32. Langlotz CP, Shortliffe EE (1983) Adapting a consultation system to critique user plans. Int J Man-Mach Stud 19:479–496

    Google Scholar 

  33. Lazarus L (2000) Clinical decision support systems: background and role in clinical support. http://www.pks. com.au/CDSS_White_Paper_doc.pdf

  34. Martinez-Bejar R, Benjamins R, Compton P, Preston P, Martin-Rubio F (1998) A formal framework to build domain knowledge ontologies for ripple-down rules-based systems. In: Gaines B, Musen M (eds) 11th Workshop on knowledge acquisition, modeling and management (KAW’98), Banff, 18–23 April, 1998, Vol 2: SHARE13

  35. McCarthy J (1991) Notes on formalizing context. Technical Report, Computer Science Department, Stanford University

  36. McGuiness D, Fikes R, Rice J, Wilder S (2000) An environment for merging and testing large ontologies. Proceedings of the knowledge representation 2000 (KR’00), Breckenridge, CO, pp 483–493

  37. Miller GA (1990) WordNet: an on-line lexical database. Int J Lexicogr 3–4:235–312

    Google Scholar 

  38. Narayanaswamy K, Goldman N (1992) “Lazy consistency”: a basis for Cooperative software development. Proceedings of International conference on computer-supported cooperative work (CSCW’92), Toronto, 31 October–4 November, ACM SIGCHI & SIGOIS, pp 257–264

  39. Noy NF, Hafner CD (1997) The state of the art in ontology design: a survey and comparative review. AI Mag Fall 1997; 18(3):53–74

    Google Scholar 

  40. Noy NF, Musen MA (2000) PROMPT: algorithm and tool support for automated ontology merging and alignment. In: Proceedings of AAAI’00, pp 450–455

  41. NSW Agriculture (2002) http://www.agric.nsw.gov.au/reader/3483

  42. Omelayenko B (2001) Learning of ontologies for the web: the analysis of existent approaches. In: Proceedings of the international workshop on Web dynamics, held in conjunction with the 8th International conference on database theory (ICDT’01), London, UK, 3 January 2001

  43. Pasquier N (2000): Mining association rules using formal concept analysis. In: Stumme G (ed) Working with conceptual structures. Contributions to ICCS 2000. Verlag Shaker, Aachen, pp 259–264

  44. Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer, Dordrecht

    Google Scholar 

  45. Predigger S, Stumme G (1999) Theory-driven logical scaling. In: Proceedings of the 6th international workshop on knowledge representation meets databases, CEUR workshop proceedings, 1999

  46. Predigger S, Wille R (1999) The lattice of concept graphs of a relationally scaled context. In: Tepfenhart W, Cyre W, Proceedings of the 7th international conference on conceptual structures; standards and practices (ICCS-99), Blacksburg, VA, 12–15 July 1999. Lecture notes in computer science (Lecture notes in artificial intelligence), vol 1640. Springer, Berlin Heidelberg New York, pp 401–414

  47. Pressman R (2000) Software engineering: a practitioner’s approach, 5th ed. (European adaptation). McGraw Hill, Berkshire

    Google Scholar 

  48. Richards D (1998) An evaluation of the formal concept analysis line diagram. In: Slaney J, Antoniou G, Maher MJ (eds) Poster proceedings of the 11th Australian joint artificial intelligence conference AI’98, 13–17 July 1998, Griffith University, Nathan Campus, Brisbane, Australia, pp 121–133

  49. Richards D (2000a) Reconciling conflicting sources of expertise: a framework and illustration. In: Compton P, Hoffmann A, Motoda H, Yamaguchi1 T (eds) Proceedings of the 6th Pacific knowledge acquisition workshop, Sydney, 11–13 December, 2000, pp 275–296

  50. Richards D (2000b) The visualisation of multiple views to support knowledge reuse. In: Intelligent information processing conference (IIP’2000). In conjunction with the 16th IFIP world computer congress WCC2000, 21–25 August, 2000, Beijing, China, pp 47–54

  51. Richards D, Chellen V, Compton C (1996) The reuse of ripple down rule knowledge bases: using machine learning to remove repetition. In: Compton P, Mizoguchi R, Motoda H, Menzies T (eds) Proceedings of Pacific knowledge acquisition workshop PKAW’96, 23–25 October 1996, Coogee, Australia, pp 293–312

  52. Richards D, Compton P (1997) Uncovering the conceptual models in ripple down rules. In: Lukose D, Delugach H, Keeler M, Searle L, Sowa JF (eds) (1997) Conceptual structures: fulfilling Peirce’s dream, Proceedings of the 5th international conference on conceptual structures (ICCS’97), 3–8 August, University of Washington, Seattle. Lecture notes in computer science (Lecture notes in artificial intelligence), vol 1257. Springer, Berlin Heidelberg New York, pp 198–212

    Google Scholar 

  53. Richards D, Compton P (1999) An alternative verification and validation technique for an alternative knowledge representation and acquisition technique. J Knowl Based Syst 12:55–73

    Article  Google Scholar 

  54. Richards D, Menzies T (1998) Extending the SISYPHUS III experiment from a knowledge engineering to a requirements engineering task. In: Gaines B, Musen M (eds) 11th Workshop on knowledge acquisition, modeling and management (KAW’98), Banff, Canada, SRDG Publications, Department of Computer Science, University of Calgary, Calgary, Canada, 1:SIS-6

  55. Schmitt I, Saake G (1998) Merging inheritance hierarchies for database integration. In: Proceedings of CoopIS’98. IEEE Computer Science Press, pp 322–331

  56. Shen W, Ong K, Mitbander B, Zaniolo C (1995) Metaqueries for data mining in Fayad U, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining. AAAI/MIT Press

  57. Singh L, Scheuermann P, Chen B (1997) Generating association rules from semistructured documents using an extended concept hierarchy. In: Proceedings of the 6th international conference on information and knowledge management, Las Vegas, Nevada, 1997, pp 193–200

  58. Soloway E, Bachant J, Jensen K (1987) Assessing the maintainability of XCON-in-RIME: coping with problems of a very large rule Base. In: Proceedings of the 6th international conference on artificial intelligence. Morgan Kaufmann, Seattle, WA 2:824–829

  59. Sowa JF (2000) http://users.bestweb.net/∼sowa/ontology/

  60. Stumme G, Maedche A (2001) FCA-merge: bottom-up merging of ontologies. In: IJCAI’01 – Proceedings of the 17th international joint conference on artificial intelligence, Seattle, 1–6 August 2001. Morgan Kaufmann, San Francisco, CA

  61. Suryanto H, Compton P (2000) Discovery of class relations in exception structured knowledge bases. In: Proceedings of the 8th international conference on conceptual structures; logical, linguistic and computational issues (ICCS-2000), Darmstadt, Germany, 14–18 August. Springer, Berlin Heidelberg New York

  62. Suryanto H, Richards D, Compton P (1999) The automatic compression of multiple classification ripple down rules. In: The 3rd international conference on knowledge based intelligent information engineering systems (KES’99), 31 August–1 September 1999, Adelaide

  63. Taylor M, Stoffel K, Hendler J (1997) Ontology-based induction of high level classification rules. In: SIGMOD data mining and knowledge discovery workshop proceedings, Tucson, AZ, 1997

  64. Uschold M (1998) Where are the killer apps? In: Proceedings of the ECAI’98 workshop on applications of ontologies and problem solving methods http://delicias.dia.fi.upm.es/ WORKSHOP /ECAI98/accepted-articles.html

  65. Uschold M, Gruninger M (1996) Ontologies: principles, methods and applications. Knowl Eng Rev 11(2):93–155

    Article  Google Scholar 

  66. Van de Vet PE, Speel P-H, Mars NJ (1994) The PLINIUS ontology of ceramic materials. 11th European conference on artificial intelligence (ECAI’94) workshop on comparison of implemented ontologies, 8–12 August, Amsterdam, The Netherlands

  67. Vogt F, Wille R (1995) TOSCANA – a graphical tool for analyzing and exploring data. Knowledge Organisation 22(2):78–81

    Google Scholar 

  68. Walker A (1980) On retrieval from a small version of a large database. In: Proc 6th Int Conf on VLDB, pp 47–54

  69. Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival (ed) Ordered sets. Reidel, Dordrecht, pp 445–470

  70. Wille R (1989) Knowledge acquisition by methods of formal concept analysis. In: Diday E (ed) Data analysis, learning symbolic and numeric knowledge. Nova Science, New York, pp 365–380

  71. Wille R (1992) Concept lattices and conceptual knowledge systems. Comput Math Appl 23(6–9):493–515

    Google Scholar 

  72. Zalta EN (1988) Intensional logic and the metaphysics of intentionality. MIT Press, Cambridge, MA

Download references

Author information

Authors and Affiliations

  1. Department of Computing, Division of Information and Communication Sciences, Macquarie University, North Ryde, 2109, Australia

    Debbie Richards

Authors
  1. Debbie Richards
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Debbie Richards.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Richards, D. Addressing the Ontology Acquisition Bottleneck Through Reverse Ontological Engineering. Know. Inf. Sys. 6, 402–427 (2004). https://doi.org/10.1007/s10115-003-0136-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-003-0136-3

Keywords

Search

Navigation