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OntoNaviERP:ERP软件文档中本体支持的导航

OntoNaviERP: Ontology-supported Navigation in ERP Software Documentation
课程网址: http://videolectures.net/iswc08_hepp_oosniesd/  
主讲教师: Martin Hepp
开课单位: 慕尼黑联邦国防军大学
开课时间: 2008-11-24
课程语种: 英语
中文简介:
企业研究规划(ERP)系统的文档通常(1)非常大,(2)结合了业务和技术实现角度的各种视图。此外,一个非常具体的词汇表也在发展,特别是在SAP领域(例如SAP解决方案映射或SAP软件模块名称)。这个词汇表没有清晰地映射到业务管理术语和概念。在实践中,在SAP ERP文档中搜索很困难是一个众所周知的问题,因为它需要对大量专有术语有深入的了解。我们建议使用本体论和这种大型HTML软件文档的自动注释,以提高ERP帮助文件的可用性和可访问性。为了实现这一点,我们为SAP ERP 6.0开发了一个本体和原型。我们的方法集成了来自(1)业务管理术语、(2)SAP业务术语、(3)SAP系统术语和(4)Wordnet系统集的概念和词汇资源。我们使用标准的GATE/KIM技术对SAP帮助文档进行注释,并分别引用我们的本体。最终,我们的方法在概念层面上整合了SAP帮助功能中包含的知识。这允许用户使用他们熟悉的术语来表达他们的查询,例如参考通用管理术语。尽管有一个广泛自动化的本体构建过程和一个简单化的注释策略,只需最少的人工干预,但我们还是取得了令人信服的结果。对于链接到动作和主题的平均查询,我们的技术返回3个以上的相关资源,而基于天真术语的搜索平均只返回约0.2个相关资源。
课程简介: The documentation of Enterprise Research Planning (ERP) systems is usually (1) extremely large and (2) combines various views from the business and the technical implementation perspective. Also, a very specific vocabulary has evolved, in particular in the SAP domain (e.g. SAP Solution Maps or SAP software module names). This vocabulary is not clearly mapped to business management terminology and concepts. It is a well-known problem in practice that searching in SAP ERP documentation is difficult, because it requires in-depth knowledge of a large and proprietary terminology. We propose to use ontologies and automatic annotation of such large HTML software documentation in order to improve the usability and accessibility, namely of ERP help files. In order to achieve that, we have developed an ontology and prototype for SAP ERP 6.0. Our approach integrates concepts and lexical resources from (1) business management terminology, (2) SAP business terminology, (3) SAP system terminology, and (4) Wordnet synsets. We use standard GATE/KIM technology to annotate SAP help documentation with respective references to our ontology. Eventually, our approach consolidates the knowledge contained in the SAP help functionality at a conceptual level. This allows users to express their queries using a terminology they are familiar with, e.g. referring to general management terms. Despite a widely automated ontology construction process and a simplistic annotation strategy with minimal human intervention, we experienced convincing results. For an average query linked to an action and a topic, our technology returns more than 3 relevant resources, while a naïve term-based search returns on average only about 0.2 relevant resources.
关 键 词: 企业系统; 研究规划; 业务管理
课程来源: 视频讲座网
数据采集: 2023-07-20:chenxin01
最后编审: 2023-07-20:chenxin01
阅读次数: 21