The information integration method of semantic web based on agent ontology(SWAO method) was put forward aiming at the problems in current network environment,which integrates,analyzes and processes enormous web inform...The information integration method of semantic web based on agent ontology(SWAO method) was put forward aiming at the problems in current network environment,which integrates,analyzes and processes enormous web information and extracts answers on the basis of semantics. With SWAO method as the clue,the following technologies were studied:the method of concept extraction based on semantic term mining,agent ontology construction method on account of multi-points and the answer extraction in view of semantic inference. Meanwhile,the structural model of the question answering system applying ontology was presented,which adopts OWL language to describe domain knowledge from where QA system infers and extracts answers by Jena inference engine. In the system testing,the precision rate reaches 86%,and the recalling rate is 93%. The experimental results prove that it is feasible to use the method to develop a question answering system,which is valuable for further study in more depth.展开更多
基金Projects(60773462, 60672171) supported by the National Natural Science Foundation of ChinaProjects(2009AA12143, 2009AA012136) supported by the National High-Tech Research and Development Program of ChinaProject(20080430250) supported by the Foundation of Post-Doctor in China
文摘The information integration method of semantic web based on agent ontology(SWAO method) was put forward aiming at the problems in current network environment,which integrates,analyzes and processes enormous web information and extracts answers on the basis of semantics. With SWAO method as the clue,the following technologies were studied:the method of concept extraction based on semantic term mining,agent ontology construction method on account of multi-points and the answer extraction in view of semantic inference. Meanwhile,the structural model of the question answering system applying ontology was presented,which adopts OWL language to describe domain knowledge from where QA system infers and extracts answers by Jena inference engine. In the system testing,the precision rate reaches 86%,and the recalling rate is 93%. The experimental results prove that it is feasible to use the method to develop a question answering system,which is valuable for further study in more depth.
文摘单一飞行员驾驶(single-pilot operation,SPO)模式具有领域知识体量庞大、体系复杂、重复使用困难等特点,导致领域知识认知不统一、自适应智能驾驶模式界定模糊等问题。针对这些问题,首先,分析SPO模式协同飞行组织架构并梳理其领域相关知识,基于Protégé构建面向SPO模式的多领域语义网络本体模型。其次,结合SPO模式自适应切换方法推理框架和流程,基于本体和语义网规则语言(semantic web rule language,SWRL)定义本体语义特征规则和数值特征规则,以推理在不同运行场景下的高匹配性自适应驾驶模式。最后,基于指标体系定量评估SPO模式领域本体模型质量。评估结果表明,所设计的SPO模式领域本体模型具有高内聚、低耦合的特性。构建的典型飞行场景案例验证了SPO模式自适应切换逻辑的可行性,以及本体的一致性和自适应切换驾驶模式的正确性。
