A New Algorithm for the Acquisition of Knowledge from Scientific Literature in Specific Fields Based on Natural Language Comprehension
Hui Wei, Zhi-long Dai
DOI: 10.4236/ijis.2011.12005   PDF    HTML     4,066 Downloads   10,243 Views   Citations


The acquisition of knowledge and the representation of that acquisition have always been viewed as the bottleneck in the construction of knowledge-based systems. The traditional methods of acquiring knowledge are based on knowledge engineering and communication with field experts. However, these methods cannot produce systematic knowledge effectively, automatically construct knowledge-based systems, or benefit knowledge reasoning. It has been noted that, in specific professional fields, experts often use fixed patterns to describe their expertise in the scientific articles that they publish. Abstracts and conclusions, for example, are key components of the scientific article, containing abundant field knowledge. This paper suggests a method of acquiring production rules from the abstracts and conclusions of scientific articles in specific fields based on natural language comprehension. First, the causal statements in article abstracts and conclusions are extracted using existing techniques, such as text mining. Next, antecedence and consequence fragments are extracted using causal template matching algorithms. As the final step, part-of-speech-tagging production rules are automatically generated according to a syntax parsing tree from the speech pair sequence. Experiments show that this system not only improves the efficiency of knowledge acquisition but also simultaneously generates systematic knowledge and guarantees the accuracy of acquired knowledge.

Share and Cite:

Wei, H. and Dai, Z. (2011) A New Algorithm for the Acquisition of Knowledge from Scientific Literature in Specific Fields Based on Natural Language Comprehension. International Journal of Intelligence Science, 1, 35-45. doi: 10.4236/ijis.2011.12005.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] J. G. Gammack and R. M. Young, “Psychological Techniques for Eliciting Expert Knowledge,” Cambridge University Press, London, 1985, pp. 105-112.
[2] R. R. Hoffman, “The Prob-lem of Extracting the Knowledge of Experts from the Perspec-tive of Experimental Psychology,” AI Magazine, Vol. 8, No. 2, 1987, pp. 53- 67.
[3] A. L. Kidd, “Knowledge Acquisition—An Introductory Framework,” Knowledge Acquisition for Expert Systems, Plenum Press, New York, 1987, pp. 1-16.
[4] J. R. Quinlan, “Fundamentals of the Knowledge Engineering Problem,” In: D. Michie, Ed., Introductory Read-ings in Expert Systems, Gordon and Breach, London, 1984, pp. 33-46.
[5] B. J. Wielinga, B. Bredeweg and J. A. Breuker, “Knowledge Acquisition for Expert Systems,” In: R. T. Nossum, Ed., Advanced Topics in Artificial Intelligence, Springer- Verlag, Berlin, 1988, pp. 25-58.
[6] E. A. Feigenbaum, “Ex-pert System in the 1980s,” In: A. Bond, Ed., Infotech State of the Art Report on Machine Intelligence, Pergamon Infotch Ltd, Maidenhead, 1981, pp. 27-52.
[7] J. Wei, R. K. Srihari, H. H. Hay and W. Xin, “Improving Knowledge Discovery in Docu-ment Collections through Combining Text Retrieval and Link Analysis Techniques,” Seventh IEEE International Conference on Data Mining, Omaha, 28-31 October 2007, pp. 193-202.
[8] F. Kennerh and P. Frederick, “Knowledge Acquisition from Repertory Grids Using a Logic of Confirma-tion,” Knowledge Acquisition Special Issue, Vol. 108, 1989, pp. 146-147.
[9] C. Osvaldo, “KAMET: A Comprehensive Methodology for Knowledge Acquisition from Multiple Knowledge Sources,” Expert Systems with Applications, Vol. 14, No. 1-2, 1998, pp. 1-16. doi:10.1016/S0957-4174(97)00064-X
[10] S.-C. Lin, S.-C. Lin, S.-S. Tseng and C.-W. Teng, “Dynamic EMCUD for Knowledge Acquisition,” Expert Systems with Applications, Vol. 34, No. 2, 2008, pp. 833- 844. doi:10.1016/j.eswa.2006.10.041
[11] C. Wang, H. Lan and H. Xie, “An Integrated Model of Knowledge Acquisition: Empirical Evidences in China,” International Conference on In-formation Management, Innovation Management and Indus-trial Engineering, Taipei, 19-21 December 2008, pp. 335-338.
[12] B. J. Wielinga, A. T. Schreiber and J. A. Breu-ker, “KADS: A Modeling Approach to Knowledge Engineering,” Knowledge Acquisition, Vol. 4, No. 1, 1992, pp. 5-53. doi:10.1016/1042-8143(92)90013-Q
[13] D. Pedro, “Knowledge Discovery via Multiple Models,” Intelligent Data Analysis, Vol. 2, No. 1-4, 1998. pp. 187- 202. doi:10.1016/S1088-467X(98)00023-7
[14] N. Lavrac and I. Mozetic, “Second Generation Knowledge Acquisition Methods and Their Application to Medicine,” Deep Models for Medical Knowledge Engineering, Elsevier, New York, 1992, pp. 177-198.
[15] S. Potter, “A Survey of Knowledge Acquisition from Natural Language,” 2003. http://www.aiai.ed.ac.uk/project/akt/work/stephenp/TMA %20 of %20 KA from NL. pdf
[16] J. Xing and T. Ah-Hwee, “Mining Ontological Knowledge from Domain-Specific Text Documents,” Fifth IEEE International Conference on Data Mining, Washington DC, 24-30 November 2005, pp. 665-668.
[17] J. Li and D. Keith, “KDMAS: A Multi-Agent System for Knowledge Discovery via Planning,” American Association for Artificial Intelligence, 2006, pp. 1877-1878.
[18] ?. Akg?bek, Y. S. Aydin, E. ?ztemel and M. S. Aksoy, “A New Algorithm for Automatic Knowledge Ac-quisition in Inductive Learning,” Knowledge-Based Systems, Vol. 19, No.6, 2006, pp. 388-395. doi:10.1016/j.knosys.2006.03.001
[19] R. Valencia-Garc??a, J. M. A. Ruiz-Sánchez, P. J. Vivancos-Vicente, et al., “An Incremental Approach for Discovering Medical Knowledge from Texts,” Expert Systems with Applications, Vol. 26, No. 3, 2004, pp. 291- 299. doi:10.1016/j.eswa.2003.09.001
[20] L. Xu, W. Dong, J. H. Wang and S. S. Gu, “A Method of the Knowledge Acquisition Using Rough Set Knowledge Reduction Algorithm Based on PSO,” 7th World Congress on Intelligent Control and Automation, Chongqing, 25-27 June 2008, pp. 5321-5326.
[21] H. T. Wang, G. C. Cao and Y. Gao, “Design and Implementation of a System for Ontology-Mediated Knowledge Acquisition from Semi-Structured Text,” Chinese Journal of Computers, Vol. 12, No. 4, 2005, pp. 2010- 2018.
[22] V. Nastase and M. Strube, “Decoding Wikipedia Categories for Knowledge Acquisition,” Proceedings of the 23rd National Conference on Artificial Intelligence, Chicago, 13-17 July 2008, pp. 1219-1224.
[23] W. Liu, “Introduction and Implementation of Drools-a Rule Engine Based Java,” Microcomputer Applica-tion, No. 6, 2005, pp. 717-721.
[24] Q. J. Jiao and J. C. Li, “Effect of RDX Particle Size on Properties of CMDB Propel-lant,” Chinese Journal of Energetic Materials, Vol. 15, No. 3, pp. 220-223.
[25] J. Z. Li, X. Z. Fan and X. G. Lu, “Influence of Ammonium Perchlorate and Aluminum Powder on the Combustion Characteristics of AP-CMDB Propellant,” Chinese Journal of Explosive & Propellants, Vol. 4, No. 31, 2008, pp. 61-63.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.