Abstract
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.
Highlights
Knowledge acquisition (KA) has long been perceived as the most difficult bottleneck in the construction of knowledge-based systems (KBS)
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
The algorithm proposed in this paper, natural language comprehension for rule extraction (NLCRE), is designed to obtain IF- rules from scientific articles by labeling the causal statements in those, extracting antecedence and consequence using causal templates, and generating rules automatically using a syntax parsing tree
Summary
Knowledge acquisition (KA) has long been perceived as the most difficult bottleneck in the construction of knowledge-based systems (KBS). Labeling the summary statements in abstracts and conclusions, may greatly improve the effectiveness and accuracy of knowledge acquisition These statements tend to have significant causal relationships, which makes them easier to represent using automatic production rules and easier to translate into field knowledge that can be used by KBS. There has been the development of techniques that improve KA automation and shorten the acquisition cycle [13,14,15,16] These include pattern recognition, machine learning, and text mining techniques, such as the automatic KA method based on inductive learning, the incremental approach to discovering knowledge from text, and knowledge discovery [17,18,19]. The algorithm proposed in this paper, natural language comprehension for rule extraction (NLCRE), is designed to obtain IF- rules from scientific articles by labeling the causal statements in those, extracting antecedence and consequence using causal templates, and generating rules automatically using a syntax parsing tree
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