Abstract:

The domain concepts are complex, various and hard to capture the development of concepts in software engineering. It's difficult for students to understand and remember. A new effective method which extracts the historical evolution information on software engineering was proposed. Firstly, the candidate sets included entities and entity relationships from Wikipedia were extracted with the Nature Language Processing (NLP) and information extraction technology. Secondly, the entity relationships which being closest to historical evolution from the candidate sets were extracted using TextRank; Finally, the knowledge base was constructed by quintuples composed of the neighboring time entities and concept entities with concerning the key entity relationship. In the process of information extraction, TextRank algorithm was improved based on the text semantic features to increase the accuracy rate. The results verify the effectiveness of the proposed algorithm, and the knowledge base can organize the concepts in software engineering field together according to the characteristics of time sequence.

Key words: software engineering, history evolution, information extraction, keyword extraction, TextRank

摘要：

针对在软件工程的教学过程中,由于领域概念种类多、演变快,导致学生理解记忆困难的问题,提出了通过抽取软件工程领域历史沿革主题信息构建知识库的方法。该方法首先结合自然语言处理技术与Web信息抽取技术从维基百科的自由文本中抽取实体与实体关系构建候选集;再利用关键词抽取方法TextRank从候选集中抽取与历史沿革关系最密切的实体关系;最后以关键实体关系为核心,抽取邻近的时间实体与概念实体组成五元组构建了知识库。在抽取信息的过程中,结合文本的语义信息对TextRank算法进行了改进,提高了抽取的准确率。实验结果表明,该知识库能够将软件工程领域的概念按时序特征组织在一起,验证了所提方法的有效性。

关键词: 软件工程, 历史沿革, 信息抽取, 关键词抽取, TextRank