OntoNaviERP: Ontology-supported Navigation in ERP Software Documentation

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This presentation explains how ontologies and automated annotation techniques can improve users\' access to SAP help documentation. The original talk was given at ISWC 2008; for the full paper, see http://dblp.uni-trier.de/rec/bibtex/conf/semweb/HeppW08 or http://www.heppnetz.de/publications/.

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OntoNaviERP: Ontology-supported Navigation in ERP Software Documentation

  1. 1. OntoNaviERP: Ontology-supported Navigation in ERP Software Documentation Martin Hepp1,2 and Andreas Wechselberger1 1E-Business and Web Science Research Group, Bundeswehr University Munich, Germany 2STI Innsbruck, Austria {mhepp@computer.org | andreas.wechselberger@unibw.de} Martin Hepp 1 mhepp@computer.org
  2. 2. ERP: Enterprise Resource Planning MRP: Material Requirements Planning MRP-II: Manufacturing Resource Planning CIM: Computer Integrated Comprehensive --> Manufacturing Complex Martin Hepp 2 mhepp@computer.org
  3. 3. SAP Help Files Martin Hepp 3 mhepp@computer.org
  4. 4. Search is important, because ... • Users must align their mental models of processes and data with those underlying the ERP Document ? Function software Role Event • Intensive usage Function • More efficient usage of ERP documentation means more efficient business operations. Martin Hepp 4 mhepp@computer.org
  5. 5. Search is difficult, because ... Martin Hepp 5 mhepp@computer.org
  6. 6. Search is difficult, because ... Martin Hepp 5 mhepp@computer.org
  7. 7. Search is difficult, because ... Textbook Business Terminology SAP Business SAP IT/System Terminology Terminology Industry Domain Terminology Martin Hepp 5 mhepp@computer.org
  8. 8. In other words: An ideal showcase for semantic technology... Martin Hepp 6 mhepp@computer.org
  9. 9. Requirements • Cost-efficient Ontology Construction • Cost-efficient Annotation • Speed • User Interface Martin Hepp 7 mhepp@computer.org
  10. 10. Competency Question Martin Hepp 8 mhepp@computer.org
  11. 11. Overview: OntoNaviERP Martin Hepp 9 mhepp@computer.org
  12. 12. Which Terms Are Most Valuable - The Most Popular, the Frequent, or the Rare? Frequency vs. Information Value vs. Likelihood of Relevance Web-wide Number of Occurrences + Likelihood of relevant documents + Likelihood of usage in queries - limited information value balance between frequency and specificity + higher information value + highest information value if needed - likelihood of occurrence in query low - likelihood of relevant documents low Term 1..N (similar pattern for: Tags, Ontology Elements) Martin Hepp 10 mhepp@computer.org
  13. 13. Approach: Conceptual Model Martin Hepp 11 mhepp@computer.org
  14. 14. Approach: Conceptual Model Semi-automatically Traditional derived from ontology term lists engineering Martin Hepp 11 mhepp@computer.org
  15. 15. Approach: Ontology Construction (1) • Script • Protégé • WordNet Plug-In • OWL Annotation Property • .NT Generator Martin Hepp 12 mhepp@computer.org
  16. 16. Approach: Ontology Construction (2) Martin Hepp 13 mhepp@computer.org
  17. 17. Approach: Ontology Construction (3) 5 * 127 = 635 126 * 257 = 32,382 Martin Hepp 14 mhepp@computer.org
  18. 18. Approach: Annotation (1) Martin Hepp 15 mhepp@computer.org
  19. 19. Approach: Annotation (2) Martin Hepp 16 mhepp@computer.org
  20. 20. Approach: User Interface and System Martin Hepp 17 mhepp@computer.org
  21. 21. Evaluation Methodology Martin Hepp 18 mhepp@computer.org
  22. 22. Results (1) Median of retrieved documents • Term-based: 0 >=50% of the action-topic pairs yield no result • OntoNaviERP: 5.5 50% of of the action-topic pairs return at least 5 result pages, of which 2 are relevant Martin Hepp 19 mhepp@computer.org
  23. 23. Results (2) Martin Hepp 20 mhepp@computer.org
  24. 24. Findings and Lessons Learned • Understanding of ontology engineering and annotation under economic constraints • In our setting, consolidation of synonyms and lexical variants does a great deal of the job Martin Hepp 21 mhepp@computer.org
  25. 25. Future Extensions • Task and Context • Annotate individual sections of resources • Skills / Users’ Competencies vs. Target Audience of a Resource • Hybrid Search: Combining term-based and conceptual search E.g. see Bhagdev/Chapman/Ciravegna, Vitaveska Lanfranchi, Daniela Petrelli: Hybrid Search: Effectively Combining Keywords and Semantic Searches, ESWC 2008 Martin Hepp 22 mhepp@computer.org
  26. 26. Reference and Links Hepp, Martin and Wechselberger, Andreas: OntoNaviERP: Ontology- supported Navigation in ERP Software Documentation, in: A. Sheth et al. (eds.): ISWC 2008. Proceedings of the 7th International Semantic Web Conference, Germany, October 26-30, 2008, Karlsruhe, Germany, Springer LNCS, Vol. 5318, pp. 764-776. This paper and other papers are available at http://www.heppnetz.de/publications/ The .NT Generator for KIM/GATE Gazetteer Lists is at: http://ebusiness-unibw.org:8180/ntfilegenerator/ Martin Hepp 23 mhepp@computer.org
  27. 27. Thank you! Martin Hepp and Andreas Wechselberger {mhepp@computer.org | andreas.wechselberger@unibw.de} http://www.unibw.de/ebusiness/ Martin Hepp 24 mhepp@computer.org

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