A COMPREHENSIVE SOLUTION TO PROCEDURAL KNOWLEDGE ACQUISITION USING INFORMATION EXTRACTION

Ziqi Zhang, Victoria Uren, Fabio Ciravegna

Abstract

Procedural knowledge is the knowledge required to perform certain tasks. It forms an important part of expertise, and is crucial for learning new tasks. This paper summarises existing work on procedural knowledge acquisition, and identifies two major challenges that remain to be solved in this field; namely, automating the acquisition process to tackle bottleneck in the formalization of procedural knowledge, and enabling machine understanding and manipulation of procedural knowledge. It is believed that recent advances in information extraction techniques can be applied compose a comprehensive solution to address these challenges. We identify specific tasks required to achieve the goal, and present detailed analyses of new research challenges and opportunities. It is expected that these analyses will interest researchers of various knowledge management tasks, particularly knowledge acquisition and capture.

References

  1. Ando, R., (2004). Semantic Lexicon Construction Learning from Unlabeled Data via Spectral Analysis. Proceedings of CoNLL'04.
  2. Aouladomar, F., (2005). A Preliminary Analysis of the Discursive and Rhetorical Structure of Procedural Texts. In Symposium on the Exploration and Modelling of Meaning
  3. Aouladomar, F., Saint-Dizier, P., (2005). An Exploration of the Diversity of Natural Argumentation in Instructional Texts. In Proceedings of IJCAI'05 Workshop on Computational Models of Natural Argument. p.69-72
  4. Benamara, F., (2004). Cooperative question answering in restricted domains: the WEBCOOP experiment. The ACL Workshop on QA in Restricted Domains.
  5. Bielsa, S., Donnell, M., (2002). Semantic Functions in Instructional Texts: A Comparison between English and Spanish. In Proceedings of the 2nd International Contrastive Linguistics Conference, p.723-732
  6. Brasser, M., Linden, K., (2002). Automatically Eliciting Task Models From Written Task Narratives. In Proceedings of the 4th International Conference on Computer-Aided Design of User Interfaces, p.83-90
  7. Cimiano, P., (2006). Ontology learning and population from text: algorithms, evaluation and applications, Springer.
  8. Cimiano, P., Völker, J., (2005). Towards large-scale, open-domain and ontology-based named entity classification, Proceedings of RANLP'05.
  9. Ananiadou, S. (1994) A methodology for automatic term recognition. Proceedings of COLING 7894.
  10. Kosseim, L., (2000). Choosing Rhetorical Structures to Plan Instructional Texts. In Journal of Computational Intelligence, Vol. 16, p408-455
  11. Max Mühlhäuser, (2008) Smart Products: An Introduction. In: Constructing Ambient Intelligence - AmI 2007 Workshops, pp. 158-164, Springer Verlag.
  12. Murdock, V., Kelly, D., Croft, W., Belkin, N., Yuan, X., (2007). Identifying and improving retrieval for procedural questions. In Information Processing & Management, Vol. 43 (1), pp. 181-203
  13. Oh, H., Myaeng, S., Jang, M., (2007). Semantic passage segmentation based on sentence topics for question answering. Journal of Information Sciences, Vol. 177
  14. Paris, C., Colineau, N., Lu, S. (2005) Automatically Generating Effective Online Help. In International Journal on E-Learning. Association for the Advancement of Computing in Education
  15. Paris, C., Linden, K., Lu, S. (2002). Automated Knowledge Acquisition for Instructional Text Generation. In Proceedings of SIGDOC'02.
  16. Power, R., Scott, D., Evans, R. (1998). What You See Is What You Meant: direct knowledge editing with natural language feedback. In ECAI'98.
  17. Preece, A., Hui, K., Gray, A., Marti, P., Bench-Capon, T, Jeans, D., Cui, Z. (1999). The KRAFT architecture for knowledge fusion and transformation. In Expert Systems. Springer
  18. Sabou, M., Kantorovitch, J., Nikolov, A., Tokmakoff, A., Zhou, X., and Motta, E., (2009). Position Paper on Realizing Smart Products: Challenges for Semantic Web Technologies, In: The 2nd International Workshop on Semantic Sensor Networks, collocated with ISWC'09
  19. Tam, R., Maulsby, D., Puerta, A. (1998). U-TEL: A Tool for Eliciting User Task Models from Domain Experts. Proceedings IUI'98, ACM
  20. Tiedemann, J., (2007). Comparing document segmentation strategies for passage retrieval in question answering. In Proceedings of RANLP 07.
  21. Tsuruoka, Y., Tateishi, Y., Kim, J., Ohta, T., McNaught, J., Ananiadou, S., Tsujii, J. (2005) Developing a Robust Part-of-Speech Tagger for Biomedical Text, Advances in Informatics, 10th Panhellenic Conference on Informatics
  22. Wang, M., Si, L., (2008). Discriminative probabilistic models for passage based retrieval. Proceedings of the 31st annual international ACM SIGIR
  23. Welty, C., Murdock, J., (2006). Towards Knowledge Acquisition from Information Extraction. ISWC2006.
Download


Paper Citation


in Harvard Style

Zhang Z., Uren V. and Ciravegna F. (2010). A COMPREHENSIVE SOLUTION TO PROCEDURAL KNOWLEDGE ACQUISITION USING INFORMATION EXTRACTION . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010) ISBN 978-989-8425-28-7, pages 432-437. DOI: 10.5220/0003091104320437


in Bibtex Style

@conference{kdir10,
author={Ziqi Zhang and Victoria Uren and Fabio Ciravegna},
title={A COMPREHENSIVE SOLUTION TO PROCEDURAL KNOWLEDGE ACQUISITION USING INFORMATION EXTRACTION},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)},
year={2010},
pages={432-437},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003091104320437},
isbn={978-989-8425-28-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)
TI - A COMPREHENSIVE SOLUTION TO PROCEDURAL KNOWLEDGE ACQUISITION USING INFORMATION EXTRACTION
SN - 978-989-8425-28-7
AU - Zhang Z.
AU - Uren V.
AU - Ciravegna F.
PY - 2010
SP - 432
EP - 437
DO - 10.5220/0003091104320437