基于领域知识的知识发现研究
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摘要
目前,知识发现的研究与应用是学术界和应用领域关注的热点,但是面对通常是领域相关的复杂应用环境,现有的知识发现研究在体系结构和算法方面尚未很好地融入领域知识,应用效果难如人意。如何从面向最终用户的角度出发,实现基于领域知识的知识发现,成为了现阶段知识发现研究和应用的焦点问题之一。随着面向服务的架构及语义网技术的发展,如何在面向服务的架构上实现知识发现应用也成为下一代知识发现系统研究领域内一个新的课题。因此本文主要对基于领域知识的知识发现展开讨论,具体在以下两个方面即:面向服务的、可融入领域知识的知识发现体系结构和基于领域知识的知识发现算法等进行了研究,取得了如下创新性研究成果:
1)、提出了一个新的面向服务的知识发现体系结构SOA4KD,建立了一个知识发现服务的质量评价体系,提出了一个基于示例学习的元学习算法来保障知识发现服务的质量。
2)、提出了一种基于领域本体服务,用户通过自然语言输入知识发现需求的方案及算法。首先对领域本体进行了分层细化,然后提出了一个扩展的知识发现任务本体,将可能的问题元素和知识发现任务联系起来,最后给出了一个语义解释算法,将用户以自然语言方式表达的需求转化为计算机可理解的形式化语言,并证明了该算法是可靠的和完整的。通过该方法,用户可以自助地在SOA4KD上实现知识发现应用。
3)、提出一种基于语义距离的最近邻分类方法SDkNN。该方法基于领域本体服务计算语义距离,并将其应用到最近邻算法中,提高了分类性能。经过在UCI数据集以及实际应用数据集中验证,SDkNN的整体性能要优于传统方法,在数据不完整的情况下效果更为明显,实践证明SDkNN有很好的应用价值。
4)、提出了一种基于本体服务的多层次意外分类规则发现算法。通过逐层知识推送和启发式规则判断意外性,可以有效提高算法效率和精度。实验证明该方法的整体性能要优于传统方法,并存实际应用中得到了验证。
5)、提出了一种基于VSOM的两阶段神经网络模型,实验表明该模型可以克服传统RBFNN需要手工定义聚类个数和中心的不足,并可以解决数据分布不平衡对预测结果的影响。基于上述神经网络模型提出了一种融合领域知识的保险洪灾损失预测模型,该模型将基于DEM的地形等因子抽象出来,并融合当地的领域知识-洪灾风险图,实验证明该方法可以明显提高模型的泛化能力。
6)、基于模型-控制-视图(MVC)设计模式实现了一个面向服务的知识发现系统原型。通过在保险公司内部进行应用,展现了该系统的应用潜力。
最后,对全文进行总结,分析目前研究工作中有待完善的地方,同时指明了进一步研究的方向。
Currently, Research and application of Knowledge Discovery is a hot topic among scientific research and real application. But when facing the always domain-related complicated application environments, Current research fail to achieve satisfying results because they can not incorporate domain knowledge effectively. How to realize domain knowledge based knowledge discovery from enduser's prespective is a hot focus among scientific research and real application. With the rapid development of Service Oriented Architecture and Semantic Web, how to perform service oriented knowledge discovery is a key topic of next generation of knowledge discovery systems. So this thesis is focused on domain knowledge based knowledge discovery, more concretely, research work was carried out around service oriented knowledge discovery architecture and domain knowledge based knowledge discovery algorithms. Following innovative contributions were achieved:1. This thesis proposed a novel service oriented architecture for knowledge discovery-SOA4KD, which embrace a quality evaluation architecture for knowledge discovery service, and an IBL(Instance Based Learning) based meta-learning algorithm is proposed to ensure the quality of knowledge discovery services.2. Based on ontology services, this thesis proposed an approach to enable end-user to input their knowledge discovery requirements using natural language. To achieve this goal, the domain ontology is categorized in further detail, and then an extended knowledge discovery task ontology is proposed, which link the possible problem elements and knowledge discovery tasks. Finally, a semantic interpretation algorithm is proposed to transfer the user requirements expressed with natural language to computer-apprehensible formal language, and the algorithm is proved to be sound and complete. This approach enables end-user to self-build knowledge discovery application on SOA4KD.3. This thesis proposed a novel kNN approach based on semantic
distance------SDkNN. This approach analysis the semantic difference between valuesof an attribute and presents how to calculate the semantic distance based on domain ontologies services, the semantic distance is then used to improve the traditional kNN methods. Experiments on UCI(University of California, Irvine) machine learning repository and real application datasets show that the overall performance of SDkNN outperforms the traditional one, especially when the data is incomplete. SDkNN also has the desirable application value in practice.4. This thesis proposed a method for discovering novel multi-level classification rules using ontology and domain knowledge service. Two heuristic rules are proposed for determining whether an attribute should be divided further and knowledge is pushed layer by layer. An algorithm for discovering unexpected multi-level classification rules using ontology is presented. Experiment shows the feasibility of the method.5. This thesis proposed a domain-knowledge based two-stage neural network model. The contribution is composed of two parts. Firstly, traditional RBFNN model need to determine the numbers and centers of neurons manually and the output of traditional model is always constrained by the intial distribution of source data and is unsatifying especially on unbalanced data. This thesis proposed a VSOM based two-stage neural network model. Experiments show this model can solve above problem effectively. Secondly, based on abovementioned model, an insurance loss prediction model incorprating domain knowledge is proposed, terrain factor based on Digital Elevation Model(DEM) is abstracted as input, an approach is proposed to incorporate local domain knowledge(i.e. flood risk map) with the output results, which improve the generation ability of the model. This is also proved by experiments.6. A prototype system of service oriented knowledge discovery system based on Model-View-Controller design pattern is implemented. The application potential is demonstrated by real application among insurance comp
1)、提出了一个新的面向服务的知识发现体系结构SOA4KD,建立了一个知识发现服务的质量评价体系,提出了一个基于示例学习的元学习算法来保障知识发现服务的质量。
2)、提出了一种基于领域本体服务,用户通过自然语言输入知识发现需求的方案及算法。首先对领域本体进行了分层细化,然后提出了一个扩展的知识发现任务本体,将可能的问题元素和知识发现任务联系起来,最后给出了一个语义解释算法,将用户以自然语言方式表达的需求转化为计算机可理解的形式化语言,并证明了该算法是可靠的和完整的。通过该方法,用户可以自助地在SOA4KD上实现知识发现应用。
3)、提出一种基于语义距离的最近邻分类方法SDkNN。该方法基于领域本体服务计算语义距离,并将其应用到最近邻算法中,提高了分类性能。经过在UCI数据集以及实际应用数据集中验证,SDkNN的整体性能要优于传统方法,在数据不完整的情况下效果更为明显,实践证明SDkNN有很好的应用价值。
4)、提出了一种基于本体服务的多层次意外分类规则发现算法。通过逐层知识推送和启发式规则判断意外性,可以有效提高算法效率和精度。实验证明该方法的整体性能要优于传统方法,并存实际应用中得到了验证。
5)、提出了一种基于VSOM的两阶段神经网络模型,实验表明该模型可以克服传统RBFNN需要手工定义聚类个数和中心的不足,并可以解决数据分布不平衡对预测结果的影响。基于上述神经网络模型提出了一种融合领域知识的保险洪灾损失预测模型,该模型将基于DEM的地形等因子抽象出来,并融合当地的领域知识-洪灾风险图,实验证明该方法可以明显提高模型的泛化能力。
6)、基于模型-控制-视图(MVC)设计模式实现了一个面向服务的知识发现系统原型。通过在保险公司内部进行应用,展现了该系统的应用潜力。
最后,对全文进行总结,分析目前研究工作中有待完善的地方,同时指明了进一步研究的方向。
Currently, Research and application of Knowledge Discovery is a hot topic among scientific research and real application. But when facing the always domain-related complicated application environments, Current research fail to achieve satisfying results because they can not incorporate domain knowledge effectively. How to realize domain knowledge based knowledge discovery from enduser's prespective is a hot focus among scientific research and real application. With the rapid development of Service Oriented Architecture and Semantic Web, how to perform service oriented knowledge discovery is a key topic of next generation of knowledge discovery systems. So this thesis is focused on domain knowledge based knowledge discovery, more concretely, research work was carried out around service oriented knowledge discovery architecture and domain knowledge based knowledge discovery algorithms. Following innovative contributions were achieved:1. This thesis proposed a novel service oriented architecture for knowledge discovery-SOA4KD, which embrace a quality evaluation architecture for knowledge discovery service, and an IBL(Instance Based Learning) based meta-learning algorithm is proposed to ensure the quality of knowledge discovery services.2. Based on ontology services, this thesis proposed an approach to enable end-user to input their knowledge discovery requirements using natural language. To achieve this goal, the domain ontology is categorized in further detail, and then an extended knowledge discovery task ontology is proposed, which link the possible problem elements and knowledge discovery tasks. Finally, a semantic interpretation algorithm is proposed to transfer the user requirements expressed with natural language to computer-apprehensible formal language, and the algorithm is proved to be sound and complete. This approach enables end-user to self-build knowledge discovery application on SOA4KD.3. This thesis proposed a novel kNN approach based on semantic
distance------SDkNN. This approach analysis the semantic difference between valuesof an attribute and presents how to calculate the semantic distance based on domain ontologies services, the semantic distance is then used to improve the traditional kNN methods. Experiments on UCI(University of California, Irvine) machine learning repository and real application datasets show that the overall performance of SDkNN outperforms the traditional one, especially when the data is incomplete. SDkNN also has the desirable application value in practice.4. This thesis proposed a method for discovering novel multi-level classification rules using ontology and domain knowledge service. Two heuristic rules are proposed for determining whether an attribute should be divided further and knowledge is pushed layer by layer. An algorithm for discovering unexpected multi-level classification rules using ontology is presented. Experiment shows the feasibility of the method.5. This thesis proposed a domain-knowledge based two-stage neural network model. The contribution is composed of two parts. Firstly, traditional RBFNN model need to determine the numbers and centers of neurons manually and the output of traditional model is always constrained by the intial distribution of source data and is unsatifying especially on unbalanced data. This thesis proposed a VSOM based two-stage neural network model. Experiments show this model can solve above problem effectively. Secondly, based on abovementioned model, an insurance loss prediction model incorprating domain knowledge is proposed, terrain factor based on Digital Elevation Model(DEM) is abstracted as input, an approach is proposed to incorporate local domain knowledge(i.e. flood risk map) with the output results, which improve the generation ability of the model. This is also proved by experiments.6. A prototype system of service oriented knowledge discovery system based on Model-View-Controller design pattern is implemented. The application potential is demonstrated by real application among insurance comp
引文
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