基于关联数据和用户本体的个性化知识服务关键技术研究
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摘要
如今在信息系统上的技术创新同样体现了教育技术的现状。信息时代的网络促进了文化传播和知识共享,电子学习的研究和实践发展迅猛,相对于面对面的传统学习方式,对终身学习而言,电子学习实际上是实景教学的一种替代方式。但同时也面临着“学习迷航”、“认知过载”、“情感缺失”、“场景单一”等难题。究其原因一是现有知识服务系统不能有效理解和标注网络资源所含的语义信息,异构的学习资源未组织成可管理的知识；二是没有为不同基础背景和学习需求的用户提供个性化知识服务。
个性化知识服务是指采用语义网、数据挖掘、信息检索、个性化推荐等技术,研究网上用户的学习行为,挖掘其知识背景、兴趣、情感、社会关系等信息；用户在网上浏览、搜索、提问和自主学习时,通过逻辑推理和语义扩展明确其学习需求,对关联课程数据进行语义检索,准确查找相关资源,以适当的可视化方法展现,为学习者提供导航、推荐、问答和定制等个性化知识服务。
个性化知识服务须要建立一个高效、高内聚、低耦合的知识服务体系架构,以实现模块间领域信息和用户信息的高度共享与互通。目前针对个性化知识服务的研究主要有两大主流,一是面向服务的LMS(Learning Management System)；二是个人学习环境架构(PLE,Personal Learning Environment).但是不管基于哪种方法,将各功能模块以WEB服务单元形式,通过互联网进行互联,构建学习环境的方法明确的把服务的功能进行分隔,并单独成为一个服务系统,这种做法容易割裂服务之间的内部联系,出现重复开发,致使开发效率降低,也使服务失去了整体性,更无法实现模块间领域信息和用户信息的高度共享与互通(包括领域信息和用户信息),因为机器无法理解信息中所隐含的语义,从而使得个性化和自适应无法有效进行。
个性化知识服务还须要建立准确反映用户个性和行为特征的用户模型。目前对用户建模的影响因子分析不够全面准确,存在状态空间复杂度高、变化预测不准等问题。因此,准确及时分析用户行为和社交关系,建立动态用户模型是提高个性化服务质量的关键之一。研究基于用户行为特征和知识本体的用户建模,构建用户本体；在用户本体和知识本体之上构建相应的逻辑规则进行语义推理,动态完善用户本体和明确化用户学习需求。
个性化服务目的在于迅速准确地为每个用户提供合适的知识,并高效全面地完成学习,提高学习效率和学习效果。海量网络学习资源的语义组织和个性化知识服务是一个关键科学问题,采用语义网技术求解网络资源共享和个性化知识服务是一项有理论意义和应用前景的新探索,对促进语义网架构下的电子学习系统研究具有重要的现实意义和实用价值。基于语义数据和用户本体的个性化知识服务对促进网络学习资源进入语义级的组织和共享、知识服务进入个性化推荐和语义搜索；对推动从个人到社区及以上各层次的全社会网络学习、加速学习型社会的构建有深刻的社会意义。本文从服务架构、用户模型、语义搜索引擎、个性化推荐引擎、个性化问答和个性化定制在内的语义个性化知识服务关键技术展开研究。
(1)以资源和用户的语义信息为纽带,提出了一种基于语义网技术的高效、高内聚、低偶合的个性化知识服务系统架构；
(2)针对目前用户建模方法对影响用户学习行为的影响因子分析不够全面准确,存在状态空间复杂度高、变化预测不准等问题,研究基于用户行为特征和知识本体的用户建模方法,构建用户本体；在用户本体和知识本体之上建立进行语义推理的逻辑规则动态完善用户本体和明确化用户学习需求,准确及时分析用户行为和社交关系,建立动态用户模型提高个性化服务质量。
(3)针对服务个性化的需要,深入研究了推荐引擎的系统架构,对影响个性化知识服务中影响推荐准确度的推荐算法进行深入研究。针对推荐过程中面临的数据稀疏问题,研究利用关联检索技术缓解数据稀疏问题的方法,并在个性化推荐算法研究过程中,研究如何引入标签及用户社交关系,改进用户／产品的相似度计算方法,从而改进推荐准确度。
(4)根据个性化学习服务中搜索服务的需要,研究了语义搜索引擎的架构、流程和具体的检索策略,针对用户不同的检索需求,提出了语义标注文档检索、实例检索和关联检索的自适应检索策略,设计了相应的语义索引构建方法、语义识别及扩展方法和个性化排序算法。
(5)针对e-Learning的特定环境,提出了三层问答架构,通过依次从问答库、关联数据和学习资源中匹配答案,为用户推荐个性化的帮助者来提供“多样的解答方式”。根据领域本体层次结构,构建语义导航树,通过导航中内容的选择来确定用户学习目标,通过语义推理找出完成该学习目标需要包含的学习内容,并生成相应的学习路径及学习计划,以课程的形式提供给用户,实现了学习环境的多样化。最后,本文通过大量实验证明了本文算法研究的有效性；原型系统的开发及实验证明了所提架构的有效性；两方面的实验结果表明本文所做的深入研究和提出的实现方案具有创新性,并且是可行和有效的。
Today, technological innovation also reflects the status of educational technology in the information system. The networks facilitate the information exchange, cultural dissemination and knowledge sharing, e-learning become the new direction of development of the education. The reason is the knowledge service system can't effective understanding and tagging the semantic information included in the network resources, the heterogeneous resources can not be organized into management knowledge. The second is not provided persnalized knowledge service for different users, which have different background and learning requirements.
Personalized knowledge service use semantic network, data mining, information retrieval, personalized recommendation technology, to research the online user's learning behavior, to mine the knowledge background, interest, emotion, social relations information. When the user search, question and autonomous learning in internet, through the logical reasoning and semantic extension to clear its learning needs, to retrieval the linked course data, accurately find the related resources, and show to users through the appropriate visualization methods. To provid navigation, recommendation, Q&A, etc., personalized knowledge service for learners.
Personalized knowledge service need to establish a efficient, high cohesion and low coupling knowledge service system architecture, in order to achieve the sharing and communication of the field information and user information between service modules. Now, the research included two main stream. service-oriented LMS (Learning Management System) and personal learning environment architecture (PLE, Personal Learning Environment). But no matter which method, each functional module to the form of Web service unit via the Internet to build learning environments, which lead to the services internal links losing, duplicate development, the development of efficiency decreases, but also to make the service lost its integrity, will not be able to achieve a high degree of sharing and interoperability of the field of information and user information between modules. Which will lead to make the personalized and adaptive cannot be effective.
Personalized knowledge service also need to set up the user model, which can accurately reflect the personality and behavior characteristics. At present, the factor of the user modeling is not enough accurate, there are state space complexity is high, the prediction of the change inaccurate problems. Therefore, accurate and timely analysis user behavior and social relations, to establish dynamic user model to improve the quality of the individualized service is one of the key. User modeling research based on user behavior characteristics and knowledge ontology, constructing user ontology; Constructing the corresponding logical rules based on the user ontology and knowledge on ontology for semantic reasoning, dynamic perfect the user ontology and clear the user learning requirements.
The goal of the personalized service is to provide the right knowledge for each user quickly and accurately, and high efficiency finish the study, to improve the learning efficiency and learning effect. Mass network learning resource semantic organization and personalized knowledge service is a key scientific problems. Using the semantic web technology to solve the network resource sharing and personalized knowledge service is a new exploration with theoretical significance and application prospect, which has important realistic significance and practical value for the semantic web framework of electronic learning system research promotion. Personalized knowledge service based on semantic data and user ontology have the profound social significance for promoting the network learning resources into the semantic level organization and sharing, the knowledge service into the personalized recommendation and semantic search. The research of this paper from the service architecture, the user model, the semantic search engines, personalized recommendation engines, personalized Q&A and personalized custom expansion.
(1) Proposed a efficient, high cohesion and low coupling of personalized knowledge service system architecture based on resources and the user's semantic information;
(2) For the factor of the user modeling is not enough accurate, there are state space complexity is high, the prediction of the change inaccurate problems. Therefore, accurate and timely analysis user behavior and social relations, to establish dynamic user model to improve the quality of the individualized service is one of the key. User modeling research based on user behavior characteristics and knowledge ontology, constructing user ontology; Constructing the corresponding logical rules based on the user ontology and knowledge on ontology for semantic reasoning, dynamic perfect the user ontology and clear the user learning requirements.
(3) According to the needs of the individual services, deeply research the recommendation of the engine system architecture and the recommendation algorithm. Using the associative retrieval techniques to alleviate the data sparseness problem, and introducing the labels and users of social relationships to improve the user/product similarity calculation method, so as to improve the diversity and accuracy of the recommended results.
(3) for different users search needs semantic annotation of document retrieval, case retrieval and associated retrieval adaptive search strategy, and design of the semantic indexing construction method, semantic recognition and extension methods and personalized sorting algorithm to ease the "cognitive overload";
(4) According to the needs of search serive, researched on the architecture, flow and retrieval strategy. Puts forward the semantic tagging document retrieval, case retrieval and associated retrieval strategy. Designed the semantic index construction method, semantic identification and extension methods and personalized sort algorithm. According to the problem of the semantic search accuracy, the corresponding semantic extension methods and algorithm, in order to improve the accuracy of the semantic search.
(5) For a specific environment of e-learning, proposed the three layer Q&A architecture to gain answer from Q&A library, linked data and learning resources in turn. To build the semantic navigation treebased on domain ontology hierarchy. Through the user's selected to determine the learning objectives. The selection of the learning content and the generation of learning path through semantic reasoning. Finally, experiments proved the effectiveness of the proposed algorithm. The development of the prototype system and experiments to prove the effectiveness of the proposed architecture. The results of two experiments show that the in-depth study and the proposed method have innovation, feasible and effective.
个性化知识服务是指采用语义网、数据挖掘、信息检索、个性化推荐等技术,研究网上用户的学习行为,挖掘其知识背景、兴趣、情感、社会关系等信息；用户在网上浏览、搜索、提问和自主学习时,通过逻辑推理和语义扩展明确其学习需求,对关联课程数据进行语义检索,准确查找相关资源,以适当的可视化方法展现,为学习者提供导航、推荐、问答和定制等个性化知识服务。
个性化知识服务须要建立一个高效、高内聚、低耦合的知识服务体系架构,以实现模块间领域信息和用户信息的高度共享与互通。目前针对个性化知识服务的研究主要有两大主流,一是面向服务的LMS(Learning Management System)；二是个人学习环境架构(PLE,Personal Learning Environment).但是不管基于哪种方法,将各功能模块以WEB服务单元形式,通过互联网进行互联,构建学习环境的方法明确的把服务的功能进行分隔,并单独成为一个服务系统,这种做法容易割裂服务之间的内部联系,出现重复开发,致使开发效率降低,也使服务失去了整体性,更无法实现模块间领域信息和用户信息的高度共享与互通(包括领域信息和用户信息),因为机器无法理解信息中所隐含的语义,从而使得个性化和自适应无法有效进行。
个性化知识服务还须要建立准确反映用户个性和行为特征的用户模型。目前对用户建模的影响因子分析不够全面准确,存在状态空间复杂度高、变化预测不准等问题。因此,准确及时分析用户行为和社交关系,建立动态用户模型是提高个性化服务质量的关键之一。研究基于用户行为特征和知识本体的用户建模,构建用户本体；在用户本体和知识本体之上构建相应的逻辑规则进行语义推理,动态完善用户本体和明确化用户学习需求。
个性化服务目的在于迅速准确地为每个用户提供合适的知识,并高效全面地完成学习,提高学习效率和学习效果。海量网络学习资源的语义组织和个性化知识服务是一个关键科学问题,采用语义网技术求解网络资源共享和个性化知识服务是一项有理论意义和应用前景的新探索,对促进语义网架构下的电子学习系统研究具有重要的现实意义和实用价值。基于语义数据和用户本体的个性化知识服务对促进网络学习资源进入语义级的组织和共享、知识服务进入个性化推荐和语义搜索；对推动从个人到社区及以上各层次的全社会网络学习、加速学习型社会的构建有深刻的社会意义。本文从服务架构、用户模型、语义搜索引擎、个性化推荐引擎、个性化问答和个性化定制在内的语义个性化知识服务关键技术展开研究。
(1)以资源和用户的语义信息为纽带,提出了一种基于语义网技术的高效、高内聚、低偶合的个性化知识服务系统架构；
(2)针对目前用户建模方法对影响用户学习行为的影响因子分析不够全面准确,存在状态空间复杂度高、变化预测不准等问题,研究基于用户行为特征和知识本体的用户建模方法,构建用户本体；在用户本体和知识本体之上建立进行语义推理的逻辑规则动态完善用户本体和明确化用户学习需求,准确及时分析用户行为和社交关系,建立动态用户模型提高个性化服务质量。
(3)针对服务个性化的需要,深入研究了推荐引擎的系统架构,对影响个性化知识服务中影响推荐准确度的推荐算法进行深入研究。针对推荐过程中面临的数据稀疏问题,研究利用关联检索技术缓解数据稀疏问题的方法,并在个性化推荐算法研究过程中,研究如何引入标签及用户社交关系,改进用户／产品的相似度计算方法,从而改进推荐准确度。
(4)根据个性化学习服务中搜索服务的需要,研究了语义搜索引擎的架构、流程和具体的检索策略,针对用户不同的检索需求,提出了语义标注文档检索、实例检索和关联检索的自适应检索策略,设计了相应的语义索引构建方法、语义识别及扩展方法和个性化排序算法。
(5)针对e-Learning的特定环境,提出了三层问答架构,通过依次从问答库、关联数据和学习资源中匹配答案,为用户推荐个性化的帮助者来提供“多样的解答方式”。根据领域本体层次结构,构建语义导航树,通过导航中内容的选择来确定用户学习目标,通过语义推理找出完成该学习目标需要包含的学习内容,并生成相应的学习路径及学习计划,以课程的形式提供给用户,实现了学习环境的多样化。最后,本文通过大量实验证明了本文算法研究的有效性；原型系统的开发及实验证明了所提架构的有效性；两方面的实验结果表明本文所做的深入研究和提出的实现方案具有创新性,并且是可行和有效的。
Today, technological innovation also reflects the status of educational technology in the information system. The networks facilitate the information exchange, cultural dissemination and knowledge sharing, e-learning become the new direction of development of the education. The reason is the knowledge service system can't effective understanding and tagging the semantic information included in the network resources, the heterogeneous resources can not be organized into management knowledge. The second is not provided persnalized knowledge service for different users, which have different background and learning requirements.
Personalized knowledge service use semantic network, data mining, information retrieval, personalized recommendation technology, to research the online user's learning behavior, to mine the knowledge background, interest, emotion, social relations information. When the user search, question and autonomous learning in internet, through the logical reasoning and semantic extension to clear its learning needs, to retrieval the linked course data, accurately find the related resources, and show to users through the appropriate visualization methods. To provid navigation, recommendation, Q&A, etc., personalized knowledge service for learners.
Personalized knowledge service need to establish a efficient, high cohesion and low coupling knowledge service system architecture, in order to achieve the sharing and communication of the field information and user information between service modules. Now, the research included two main stream. service-oriented LMS (Learning Management System) and personal learning environment architecture (PLE, Personal Learning Environment). But no matter which method, each functional module to the form of Web service unit via the Internet to build learning environments, which lead to the services internal links losing, duplicate development, the development of efficiency decreases, but also to make the service lost its integrity, will not be able to achieve a high degree of sharing and interoperability of the field of information and user information between modules. Which will lead to make the personalized and adaptive cannot be effective.
Personalized knowledge service also need to set up the user model, which can accurately reflect the personality and behavior characteristics. At present, the factor of the user modeling is not enough accurate, there are state space complexity is high, the prediction of the change inaccurate problems. Therefore, accurate and timely analysis user behavior and social relations, to establish dynamic user model to improve the quality of the individualized service is one of the key. User modeling research based on user behavior characteristics and knowledge ontology, constructing user ontology; Constructing the corresponding logical rules based on the user ontology and knowledge on ontology for semantic reasoning, dynamic perfect the user ontology and clear the user learning requirements.
The goal of the personalized service is to provide the right knowledge for each user quickly and accurately, and high efficiency finish the study, to improve the learning efficiency and learning effect. Mass network learning resource semantic organization and personalized knowledge service is a key scientific problems. Using the semantic web technology to solve the network resource sharing and personalized knowledge service is a new exploration with theoretical significance and application prospect, which has important realistic significance and practical value for the semantic web framework of electronic learning system research promotion. Personalized knowledge service based on semantic data and user ontology have the profound social significance for promoting the network learning resources into the semantic level organization and sharing, the knowledge service into the personalized recommendation and semantic search. The research of this paper from the service architecture, the user model, the semantic search engines, personalized recommendation engines, personalized Q&A and personalized custom expansion.
(1) Proposed a efficient, high cohesion and low coupling of personalized knowledge service system architecture based on resources and the user's semantic information;
(2) For the factor of the user modeling is not enough accurate, there are state space complexity is high, the prediction of the change inaccurate problems. Therefore, accurate and timely analysis user behavior and social relations, to establish dynamic user model to improve the quality of the individualized service is one of the key. User modeling research based on user behavior characteristics and knowledge ontology, constructing user ontology; Constructing the corresponding logical rules based on the user ontology and knowledge on ontology for semantic reasoning, dynamic perfect the user ontology and clear the user learning requirements.
(3) According to the needs of the individual services, deeply research the recommendation of the engine system architecture and the recommendation algorithm. Using the associative retrieval techniques to alleviate the data sparseness problem, and introducing the labels and users of social relationships to improve the user/product similarity calculation method, so as to improve the diversity and accuracy of the recommended results.
(3) for different users search needs semantic annotation of document retrieval, case retrieval and associated retrieval adaptive search strategy, and design of the semantic indexing construction method, semantic recognition and extension methods and personalized sorting algorithm to ease the "cognitive overload";
(4) According to the needs of search serive, researched on the architecture, flow and retrieval strategy. Puts forward the semantic tagging document retrieval, case retrieval and associated retrieval strategy. Designed the semantic index construction method, semantic identification and extension methods and personalized sort algorithm. According to the problem of the semantic search accuracy, the corresponding semantic extension methods and algorithm, in order to improve the accuracy of the semantic search.
(5) For a specific environment of e-learning, proposed the three layer Q&A architecture to gain answer from Q&A library, linked data and learning resources in turn. To build the semantic navigation treebased on domain ontology hierarchy. Through the user's selected to determine the learning objectives. The selection of the learning content and the generation of learning path through semantic reasoning. Finally, experiments proved the effectiveness of the proposed algorithm. The development of the prototype system and experiments to prove the effectiveness of the proposed architecture. The results of two experiments show that the in-depth study and the proposed method have innovation, feasible and effective.
引文
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