Yang Deng Yang Deng, Bangchao Wang Yang Deng, Zhongyuan Hua Bangchao Wang, Yong Xiao Zhongyuan Hua, Xingfu Li Yong Xiao
{"title":"A Knowledge Graph Construction Method for Software Project Based on CAJP","authors":"Yang Deng Yang Deng, Bangchao Wang Yang Deng, Zhongyuan Hua Bangchao Wang, Yong Xiao Zhongyuan Hua, Xingfu Li Yong Xiao","doi":"10.53106/160792642023112406006","DOIUrl":null,"url":null,"abstract":"In recent years, there has been increasing interest in using knowledge graphs (KGs) to help stakeholders organize and better understand the connections between various artifacts during software development. However, extracting entities and relationships automatically and accurately in open-source projects is still a challenge. Therefore, an efficient method called Concise Annotated JavaParser (CAJP) has been proposed to support these extraction activities, which are vitally important for KG construction. The experimental result shows that CAJP improves the accuracy and type of entity extraction and ensures the accuracy of relationship exaction. Moreover, an intelligent question-and-answer (Q&A) system is designed to visualize and verify the quality of the KGs constructed from six open-source projects. Overall, the software project-oriented KG provides developers a valuable and intuitive way to access and understand project information.","PeriodicalId":442331,"journal":{"name":"網際網路技術學刊","volume":"7 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"網際網路技術學刊","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53106/160792642023112406006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, there has been increasing interest in using knowledge graphs (KGs) to help stakeholders organize and better understand the connections between various artifacts during software development. However, extracting entities and relationships automatically and accurately in open-source projects is still a challenge. Therefore, an efficient method called Concise Annotated JavaParser (CAJP) has been proposed to support these extraction activities, which are vitally important for KG construction. The experimental result shows that CAJP improves the accuracy and type of entity extraction and ensures the accuracy of relationship exaction. Moreover, an intelligent question-and-answer (Q&A) system is designed to visualize and verify the quality of the KGs constructed from six open-source projects. Overall, the software project-oriented KG provides developers a valuable and intuitive way to access and understand project information.
近年来,人们对使用知识图谱(KG)来帮助利益相关者组织和更好地理解软件开发过程中各种工件之间的联系越来越感兴趣。然而,在开源项目中自动、准确地提取实体和关系仍然是一项挑战。因此,我们提出了一种名为简明注释 JavaParser(CAJP)的高效方法来支持这些提取活动,这对于构建 KG 至关重要。实验结果表明,CAJP 提高了实体提取的准确性和类型,并确保了关系排序的准确性。此外,还设计了一个智能问答(Q&A)系统,用于可视化和验证从六个开源项目中构建的 KG 的质量。总之,面向软件项目的 KG 为开发人员提供了一种访问和理解项目信息的有价值的直观方式。