Abstract
The power industry usually records equipment failures, defects and other information in the form of text, which contains lots of regular patterns. Knowledge extraction in fault text is of great significance to improve efficiency and reduce the labor cost in the power industry. However, the research for knowledge extraction of text information in this field is rare, it is even more difficult to use machine learning algorithms to mine the deep patterns. To solve this problem, a method of knowledge extraction is proposed in this field. We use power equipment fault texts and relevant guidance as raw materials. Firstly, the knowledge base of this field is designed and constructed based on the ontology concepts, including ontology concept base, description base and regular expression base. Then, the knowledge extraction algorithm is designed according to the knowledge base. After that we conduct the knowledge merge operation to make the extraction results more accurate. Experiments on the real fault texts shows the feasibility and the high accuracy of our method when compared with artificial extraction.
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