Author(s)

Hui Wei, Zhi-long Dai

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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.

Knowledge Acquisition, Knowledge Representation, Text Mining, Production Rules

H. Wei and Z. Dai, "A New Algorithm for the Acquisition of Knowledge from Scientific Literature in Specific Fields Based on Natural Language Comprehension," International Journal of Intelligence Science, Vol. 1 No. 2, 2011, pp. 35-45. doi: 10.4236/ijis.2011.12005.