【摘要】 在知识管理系统中,为有效地解决用户查询与文档之间相同概念的不同表达形式造成的失配问题,提出一种基于本体、以面向任务情景的结构化描述作为信息体内容的语义索引的双向扩展检索方法,通过相容匹配和知识联网2种机制实现了扩展检索,分别对应于自上而下的和自下而上的2种途径;并采用查询重写模板(QRT)来搜索与当前任务相关的知识.基于原始查询和本体,QRT生成大量的子查询,同时将与原始查询相关度的权重传递给子查询式.自上而下方法或知识联网机制通过组织、任务本体检索到相关知识项.自下而上方法在任务情景中搜索相似任务,并获取包含该任务描述的知识项.2种方法都应用QRT实现基于本体的知识检索.实验结果表明:文中方法提高了知识管理系统的检索效率和准确率.更多还原

【Abstract】 To cope with the mismatch problem of various expressions of the same concept in different information resources, an approach of structural description adaptive to the task circumstance as semantic indexes to the information body content is presented to meet the requirement of expanded query. Two mechanisms to realize expanded query, namely knowledge networking and compatible matching are introduced, correlating to the top-down access and bottom-up access respectively. Moreover, query rewriting template (QRT) is exploited to search the useful knowledge related to the present task. QRT produces a large number of sub-queries based on the original query and ontology, at the same time passes the weight values to sub-queries which score their degree of relativity to the original query. Top-down approach or knowledge networking mechanism retrieves the related knowledge items by organization and task ontology, while bottom-up approach searches the similar task in task circumstance and retrieves the knowledge items containing the task description. In both cases query rewriting template is applied to realize the ontology-based knowledge retrieval. Experiments show that the proposed approach improves the retrieval efficiency and accuracy of knowledge management system.更多还原