基于网络课程本体的语义查询推理系统

针对网络课程知识点相对孤立、共享性与重用性差、学习资源查找困难、不能提供个性化服务等问题,提出使用基于本体查询推理的解决方法,设计了检索、查询和推理的功能模块和工作流程,并综合运用了Jena和SPARQL实现了相应的原型系统。通过具体的课程本体进行测试,验证了该解决方法的有效性和可行性。该系统能够将网络课程知识内容有机组织到一起,达到共享、重用的效果,可以提供个性化的学习服务,为进一步开发智能化的网络课程平台提供了条件。     

基于领域本体的语义查询推理系统

传统的基于关键字的信息检索技术不能满足人们对信息查询的需求,语义网技术是解决这一问题最有前景的方法。本文设计与开发一个基于出版物领域本体的语义查询与推理系统,该系统构建了出版物领域本体,并构造该领域本体的查询语句和推理规则,给出语义查询和推理的结果,并对结果进行测试。结果验证了系统对语义查询和推理的可行性和有效性。      

基于本体的学前教育资源建模研究

由于传统web资源存在着先天的局限性,网络中的资源描述缺乏语义信息,往往很难进行语义层面上的推理和检索。本体是共享概念模型的明确的形式化的规范说明,通过概念之间的关系来描述概念的语义。本文通过将本体构建技术应用于学前教育学科资源的构建中,构建的学前教育学科实验本体,初步实现了对该实验本体的查询和推理。     

本体的查询与推理机制研究

首先介绍了本体的定义并对其查询模型进行了探讨,然后在对已有的本体查询语言进行了比较和评价后认为,要充分利用本体的模式信息进行查询就必须提高查询的推理能力,重点对DQL(OWL-QL)的逻辑基础和推理机制进行分析,并提出本体查询语言的发展应该是在本体Web语言上进行语义规则的扩充。     

基于本体的教育资源推理查询原型系统设计与实现

针对当前教育资源库存在的通用性差和缺乏语义查询等缺陷,将语义Web的重要基础本体及其推理和查询技术应用到教育资源领域,实现一个基于本体的教育资源推理查询原型系统。利用本体构建方法及建模工具protégé,以数据结构课程为例,构建一个基于元数据标准的教育资源领域本体;制定教育资源领域本体知识点推理规则,提出改进的语义相似度算法;设计并实现基于本体的教育资源推理查询原型系统。通过实验验证,该系统的查全率与查准率均高于基于关键字的查询。     

基于BaseX数据库的OWL本体存储查询研究

针对目前基于关系型数据库等存储模式的本体存储查询效率较低的情况,提出使用XML数据库BaseX进行本体的存储,并设计了相应的本体存储查询架构。在对BaseX存储结构与接口的研究基础上,实现对OWL本体的存储。利用BaseX的查询接口和XQuery查询语言对OWL本体进行检索,在建立推理规则库基础上,实现本体查询扩展与推理。实验将提出的存储查询方法与基于关系型数据库的存储查询方法进行对比,验证了提出的方法具备高效的存储查询性能,同时具备本体查询的推理能力。     

基于本体的地理信息查询和排序

建立了一个基于本体的地理信息查询系统(OGIIS)。通过对地理本体实例的语义推理和索引，OGIIS实现了对地理实体中语义关系的查询，解决了传统的地理信息查询中无法对语义关系进行查询和推理的问题，提高了地理空间语义网上对异构数据信息检索和查询的智能性和准确度。     

本体的查询与推理研究

现今,计算机网络被广泛应用于生活的方方面面,而从海量的信息中搜寻出人们所需要的还存在诸多问题,于是产生了本体的概念.而本体的查询和推理是基于本体的应用中重要的组成部分,研究的目的是为了使知识得以充分表达并且对信息的查询更加精确、完备.首先介绍了本体的概念并建立本体模型,然后用本体查询语言SPARQL对已有模型进行查询并用SWRL对模型进行语义规则的扩充;最后介绍了Jena,并对本体模型进行推理,由此获得了更多知识.结论就是,在利用SPARQL和Jena进行查询与推理的过程中,推理将提高查询能力,而规则是提高推理能力的关键     

采用RDF的查询扩展研究

由于传统的P2P查询处理将用户查询作为独立的关键字对待,只考虑其字面符号意义而不考虑其语义。因此,用户的查询需求往往得不到真实的体现。基于本体的P2P资源匹配使用本体对网络资源进行了描述,在很大程度上反映了资源的语义,然而仍需要对查询请求进行语义扩展才适合在资源的本体中进行查询处理。使用RDF三元组描述用户请求,并扩展其语义表达能力,分析本体中概念间的上下位关系,对查询请求进行语义扩展,扩展后的查询适合于利用本体进行查询匹配。分析表明,这种扩展是有效的合理的。     

本体的查询与推理研究

现今,计算机网络被广泛应用于生活的方方面面,而从海量的信息中搜寻出人们所需要的还存在诸多问题,于是产生了本体的概念。而本体的查询和推理是基于本体的应用中重要的组成部分,研究的目的是为了使知识得以充分表达并且对信息的查询更加精确、完备。首先介绍了本体的概念并建立本体模型,然后用本体杳询语言SPARQL对已有模型进行查询并用SWRL对模型进行语义规则的扩充;最后介绍了Jena,并对本体模型进行推理,由此获得了更多知识。结论就足,在利用SPARQL和Jena进行查询与推理的过程中,推理将提高查询能力,而规则是提高推理能力的关键。     

A Query Routing Approach Based on Users’ Satisfaction for Resource Discovery in Service-Oriented Networks

Service oriented networks are distributed computing infrastructures that provide widely distributed resources. These networks are dynamic and their size and complexity continue to increase and allow to users a ubiquitous access to available resources and services. Therefore, efficient query routing approaches in large and highly distributed service oriented networks are required and need to be adaptive in order to cope with a dynamically changing environment. In this paper, a query routing approach based on mobile agents and random walks with a reinforcement learning technique is presented. By enhancing random walks with a reinforcement learning mechanism centered on users’ satisfaction, this approach allows dynamic and self-adaptive location of required resources. Peers incorporate knowledge from past and present queries which will be used during next searches by mobile agents to select their next hops. This approach is analyzed through two query routing techniques using the network simulator ns2.     

基于语义属性特征图的查询扩展方法

传统的查询扩展方法由于忽略了词之间的语义关系,在不规范的短小关键字上补充扩展的词已经无法达到预期目标。Linked Data技术利用资源描述框架(RDF)图模型形成Linked Open Data Cloud,能提供更多语义信息。针对查询扩展忽略语义的问题,提出了一种基于语义属性特征图的查询扩展方法。该方法将语义网与图的思想融合,利用以DBpedia资源为顶点的属性图加以扩展。首先,通过有监督的学习训练出15种语义属性特征的权重,用于表达扩展资源的有用性;然后,在整个DBpedia图上通过标签属性实现查询关键字到DBpedia匹配资源的映射;再根据属性特征广度搜索出邻接点,并将其作为扩展候选词,最后筛选出词相关行分值最高的作为最终扩展词。实验表明,与LOD Keyword Expansion方法相比,基于语义属性特征图的扩展方法召回率达到0.89,平均逆排序(MRR)提高4个百分点,与用户查询更匹配。     

Deep Learning and Semantic Concept Spaceare Used in Query Expansion

In the practice of information retrieval, there are some problems such as the lack of accurate expression of user query requests, the mismatch between document and query and query optimization. Focusing on these problems, we propose the query expansion method based on conceptual semantic space with deep learning, this hybrid query expansion technique include deep learning and pseudocorrelation feedback, use the deep learning and semantic network WordNet to construct query concept tree in the level of concept semantic space, the pseudo-correlation feedback documents are processed by observation window, compute the co-occurrence weight of the words by using the average mutual information and get the final extended words set. The results of experiment show that the expansion algorithm based on conceptual semantic space with deep learning has better performance than the traditional pseudo-correlation feedback algorithm on query expansion.     

Cross-domain video concept detection: A joint discriminative and generative active learning approach

In this work, we study the problem of cross-domain video concept detection, where the distributions of the source and target domains are different. Active learning can be used to iteratively refine a source domain classifier by querying labels for a few samples in the target domain, which could reduce the labeling effort. However, traditional active learning method which often uses a discriminative query strategy that queries the most ambiguous samples to the source domain classifier for labeling would fail, when the distribution difference between two domains is too large. In this paper, we tackle this problem by proposing a joint active learning approach which combines a novel generative query strategy and the existing discriminative one. The approach adaptively fits the distribution difference and shows higher robustness than the ones using single strategy. Experimental results on two synthetic datasets and the TRECVID video concept detection task highlight the effectiveness of our joint active learning approach.     

Dynamic Query Optimization Approach for Semantic Database Grid

Fundamentally, semantic grid database is about bringing globally distributed databases together in order to coordinate resource sharing and problem solving in which information is given well-defined meaning, and DartGrid II is the implemented database gird system whose goal is to provide a semantic solution for integrating database resources on the Web. Although many algorithms have been proposed for optimizing query-processing in order to minimize costs and/or response time, associated with obtaining the answer to query in a distributed database system, database grid query optimization problem is fundamentally different from traditional distributed query optimization. These differences are shown to be the consequences of autonomy and heterogeneity of database nodes in database grid. Therefore, more challenges have arisen for query optimization in database grid than traditional distributed database. Following this observation, the design of a query optimizer in DartGrid II is presented, and a heuristic, dynamic and parallel query optimization approach to processing query in database grid is proposed. A set of semantic tools supporting relational database integration and semantic-based information browsing has also been implemented to realize the above vision.     

Multi-label active learning by model guided distribution matching

Multi-label learning is an effective framework for learning with objects that have multiple semantic labels, and has been successfully applied into many real-world tasks. In contrast with traditional single-label learning, the cost of labeling a multi-label example is rather high, thus it becomes an important task to train an effectivemulti-label learning model with as few labeled examples as possible. Active learning, which actively selects the most valuable data to query their labels, is the most important approach to reduce labeling cost. In this paper, we propose a novel approach MADM for batch mode multi-label active learning. On one hand, MADM exploits representativeness and diversity in both the feature and label space by matching the distribution between labeled and unlabeled data. On the other hand, it tends to query predicted positive instances, which are expected to be more informative than negative ones. Experiments on benchmark datasets demonstrate that the proposed approach can reduce the labeling cost significantly.     

An empirical analysis of ontology-based query expansion for learning resource searches using MERLOT and the Gene ontology

This paper proposes an expansion of queries based on formal domain ontologies in the context of the search for learning resources in repositories. The expansion process uses the relation types that are represented in these models; common ontological relations, and ontological relations specific to domain and traditional terminology relations, typical of thesauri. The tests were conducted using Gene ontology as the knowledge base and MERLOT is used as the test repository. The results of this study case indicate that, at similar levels of precision, expanded queries improve levels of novelty and coverage compared to the original query (without expansion), i.e. expanded queries allow the user to retrieve relevant objects, which might not be obtained without expansion.     

Optimizing queries to remote resources

One key property of the Semantic Web is its support for interoperability. Recent research in this area focuses on the integration of multiple data sources to facilitate tasks such as ontology learning, user query expansion and context recognition. The growing popularity of such machups and the rising number of Web APIs supporting links between heterogeneous data providers asks for intelligent methods to spare remote resources and minimize delays imposed by queries to external data sources. This paper suggests a cost and utility model for optimizing such queries by leveraging optimal stopping theory from business economics: applications are modeled as decision makers that look for optimal answer sets. Queries to remote resources cause additional cost but retrieve valuable information which improves the estimation of the answer set’s utility. Optimal stopping optimizes the trade-off between query cost and answer utility yielding optimal query strategies for remote resources. These strategies are compared to conventional approaches in an extensive evaluation based on real world response times taken from seven popular Web services.     

《智能交通系统》课程网上教学系统的设计与实现

为了充分利用网络教学的优势,提高学生的学习积极性和学习效率,开发了基于B/S模式的《智能交通系统》课程网上教学系统。本系统可以实现课程教学信息和教学资源的发布,教学资源的浏览、查询与下载,学生和教师在线互动答疑、后台数据管理等功能,为课程的教学提供了一种灵活、方便、高效的教学方式。     

A lazy learning approach for building classification models

In this paper, we propose a lazy learning strategy for building classification learning models. Instead of learning the models with the whole training data set before observing the new instance, a selection of patterns is made depending on the new query received and a classification model is learnt with those selected patterns. The selection of patterns is not homogeneous, in the sense that the number of selected patterns depends on the position of the query instance in the input space. That selection is made using a weighting function to give more importance to the training patterns that are more similar to the query instance. Our intention is to provide a lazy learning mechanism suited to any machine learning classification algorithm. For this reason, we study two different methods to avoid fixing any parameter. Experimental results show that classification rates of traditional machine learning algorithms based on trees, rules, or functions can be improved when they are learnt with the lazy learning approach proposed. © 2011 Wiley Periodicals, Inc.