基于本体的用户兴趣模型构建研究

针对用户兴趣模型中本体构建和模型更新的难点和不足,提出一种基于本体论的用户兴趣模型构建方法,该方法通过领域本体、用户个性本体、校正本体和本体更新实现模型的构建。对于领域本体的构建,摒弃了训练、学习和聚类的方法,直接从开放目录专案获取类目。对于用户兴趣的更新,采用按照校正本体增加、淘汰和传递原理调整相结合的方式。实验结果表明,该模型较易生成,用户兴趣的准确度和更新的及时性都有所提高。     

基于用户概念视图的本体约减

本体日益受到人们的关注。随着知识的增加，领域本体会变得异常庞大。对于特定用户，其知识水平和工作需要仅涉及该本体的一小部分。因此，对于该用户，根据他所拥有的知识来理解整个领域本体是不现实的，并会对本体的应用带来障碍。文章提出了一种针对特定用户的本体提取和简化算法，根据用户给定的概念集和相应的导出关系集，形成一个既包含用户关注的概念集，又符合原本体知识体系和结构特征的个性化视图。     

领域本体学习资源分配模型设计

领域本体是某领域中信息语义的基本单位，是对领域信息资源进行分类与描述的概念化体系，通过领域本体所定义的严格语义内涵和概念之间的相互关系，可以建立起信息资源在本体层的映射关系。本文介绍了通过引入领域本体，构建智能授导系统中的学习者和学习资源相关本体模型，同时对学习资源进行语义描述，主要针对资源的组成部分的显示形式和操作进行描述。     

基于语义模型的信息检索机制研究

提出了一种基于本体语义模型的信息检索方法。该方法充分利用领域本体提供的概念之间的语义相关性，从语义模型扩展、概念相似度、相关度计算，并以用户反馈等角度探讨了基于语义模型的自动推理方法在信息检索中的应用，文章介绍了系统实现框架。该系统将应用在学习资源的智能检索中。     

基于本体的论文管理系统

由于用户知识领域的局限性，传统的论文管理系统无法为用户提供全面而高效的关键字查询。该文设计了QOFLoDL本体描述语言，在论文管理系统中引入中用它所描述的本体库，对传统的论文管理系统进行了重新构建，提出一种新的论文管理系统——基于本体的论文管理系统(PMSBOO），为用户提供了全面而高效的关键字查询服务，PMSBOO实现了基于知识点的协同学习，通过这种学习方式各个知识领域的用户可以进行知识共享和知识交流从而达到协同学习的目的，很大程度上提高了用户的学习效率。还在PMSBOO中提供论文大纲提取功能，使用户通过先阅读论文大纲来对论文进行有选择的重点阅读，从而提高了用户的学习效率。     

一种信息门户中基于本体的信息查询模型

提出了一种信息门户中的基于本体的信息查询模型，该模型主要由两个模块组成：基于领域本体的改进的查询模块和基于模糊本体的逐步精确的查询模块。前者对应于用户能够清楚地表达自己需要的信息，它可以基于相似度对查询结果进行排序。后者则对应于用户不能够清楚地表达自己需要的信息，它可以向用户提出查询建议，逐步精确用户的查询。     

基于多用户表决的本体自适应演化

本体在应用过程中会面临脱离实际的情形，所以需要获取用户在使用本体过程中的反馈信息加以完善，本文结合信息过滤领域应用的本体，从本体演化含义，本体演化操作，本体演化步骤几个方面，基于多用户对过滤结果的表决判断．提出了一个本体在实际应用环境下的自适应演化模型。     

基于本体的信息检索研究

介绍了本体Ontology的概念和理论知识,提出一种基于本体的Web信息检索模型.该模型利用本体技术对Internet上的各类信息进行领域分类,规范用户信息检索模式,以达到快速、准确找到用户所需信息的目的.     

基于本体驱动的法律信息检索模型

本文提出了基于本体驱动的法律信息检索模型，以解决当前Web信息检索中存在的问题。本文运用到了数据挖掘中的关联规则，并借鉴“七步法”来构建信息检索模型，构建步骤包括文档预处理、构建领域本体、过滤、构造人机接口等。向用户提供基于法律本体的概念查询、语义扩充查询、分类浏览等检索手段。该模型能够改善用户查准率和查全率，实现对该领域资源的智能化检索。     

一种基于领域本体的用户建模方法

针对当前大多数个性化服务系统的不足,以旅游领域为背景,提出了一种新的基于本体的用户模型构建方法,利用领域本体中的概念、实例和属性描述用户兴趣特征,实现了在语义层次上理解用户兴趣。实验表明,该方法能有效提高用户模型的质量。     

Adapting domain ontology for personalized knowledge search and recommendation

This paper presents research on the development of a domain ontology adaptation system for personalized knowledge search and recommendation that adapts a suitable domain ontology according to the previous browsing and reading behavior of users (i.e., usage history log). An adaptive domain ontology can satisfy the future requirements of users and promote use value. In developing the system, a domain ontology adaptation model is first designed. Based on the designed adaptation model, a methodology for domain ontology adaptation is developed. Subsequently, a domain ontology adaptation system is implemented with an illustrative example of securities trading. Finally, a system evaluation for user satisfaction and a methodology evaluation are conducted to demonstrate that the developed methodology and system worked efficiently.     

Towards a Machine of a Process (MOP) ontology to facilitate e-commerce of industrial machinery

Adapting to user's requirements is a key factor for enterprise success. Despite the existence of several approaches that point in this direction, simplifying integration and interoperability among users, suppliers and the enterprise during product lifecycle, is still an open issue. Ontologies have been used in some manufacturing applications and they promise to be a valid approach to model manufacturing resources of enterprises (e.g. machinery and raw material). Nevertheless, in this domain, most of the ontologies have been developed following methodologies based on development from scratch, thus ontologies previously developed have been discarded. Such ontological methodologies tend to hold the interoperability issues in some level. In this paper, a method that integrates ontology reuse with ontology validation and learning is presented. An upper (top-level) ontology for manufacturing was used as a reference to evaluate and to improve specific domain ontology. The evaluation procedure was based on the systemic methodology for ontology learning (SMOL). As a result of the application of SMOL, an ontology entitled Machine of a Process (MOP) was developed. The terminology included in MOP was validated by means of a text mining procedure called Term Frequency–Inverse Document Frequency (TF–IDF) which was carried out on documents from the domain in this study. Competency questions were performed on preexisting domain ontologies and MOP, proving that this new ontology has a performance better than the domain ontologies used as seed.     

基于形式概念分析的领域本体构建方法研究

近年来，本体作为一种有效的、表现概念层次结构和语义的模型，被越采越多的领域所应用。应该说，本体的出现能很好地解决目前计算机应用领域中存在的一些困难，如人机交互或机器与机器之间的通信、自动推理、知识表示和重用等。但是，在能很好地应用本体之前，我们面临一个新的难题：本体的构建。本文对现有的领域本体构建方法做了总体性介绍，并在此基础上详细描述了几种基于形式概念分析的领域本体构建方法，最后时形式概念分析用于领域本体构建方法做了分析、比较和总结。     

Learning the semantics of structured data sources

Information sources such as relational databases, spreadsheets, XML, JSON, and Web APIs contain a tremendous amount of structured data that can be leveraged to build and augment knowledge graphs. However, they rarely provide a semantic model to describe their contents. Semantic models of data sources represent the implicit meaning of the data by specifying the concepts and the relationships within the data. Such models are the key ingredients to automatically publish the data into knowledge graphs. Manually modeling the semantics of data sources requires significant effort and expertise, and although desirable, building these models automatically is a challenging problem. Most of the related work focuses on semantic annotation of the data fields (source attributes). However, constructing a semantic model that explicitly describes the relationships between the attributes in addition to their semantic types is critical.We present a novel approach that exploits the knowledge from a domain ontology and the semantic models of previously modeled sources to automatically learn a rich semantic model for a new source. This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology. Given some sample data from the new source, we leverage the knowledge in the domain ontology and the known semantic models to construct a weighted graph that represents the space of plausible semantic models for the new source. Then, we compute the top k candidate semantic models and suggest to the user a ranked list of the semantic models for the new source. The approach takes into account user corrections to learn more accurate semantic models on future data sources. Our evaluation shows that our method generates expressive semantic models for data sources and services with minimal user input. These precise models make it possible to automatically integrate the data across sources and provide rich support for source discovery and service composition. They also make it possible to automatically publish semantic data into knowledge graphs.     

Agent-based architecture for context-aware and personalized event recommendation

In order to offer context-aware and personalized information, intelligent processing techniques are necessary. Different initiatives considering many contexts have been proposed, but users preferences need to be learned to offer contextualized and personalized services, products or information. Therefore, this paper proposes an agent-based architecture for context-aware and personalized event recommendation based on ontology and the spreading algorithm. The use of ontology allows to define the domain knowledge model, while the spreading activation algorithm learns user patterns by discovering user interests. The proposed agent-based architecture was validated with the modeling and implementation of eAgora? application, which was illustrated at the pervasive university context.     

Efficient retrieval of ontology fragments using an interval labeling scheme

Nowadays very large domain ontologies are being developed in life-science areas like Biomedicine, Agronomy, Astronomy, etc. Users and applications can benefit enormously from these ontologies in very different tasks, such as visualization, vocabulary homogenizing and data classification. However, due to their large size, they are often unmanageable for these applications. Instead, it is necessary to provide small and useful fragments of these ontologies so that the same tasks can be performed as if the whole ontology is being used. In this work we present a novel method for efficiently indexing and generating ontology fragments according to the user requirements. Moreover, the generated fragments preserve relevant inferences that can be made with the selected symbols in the original ontology. Such a method relies on an interval labeling scheme that efficiently manages the transitive relationships present in the ontologies. Additionally, we provide an interval’s algebra to compute some logical operations over the ontology concepts. We have evaluated the proposed method over several well-known biomedical ontologies. Results show very good performance and scalability, demonstrating the applicability of the proposed method in real scenarios.     

基于本体的元搜索引擎技术研究

针对现有搜索引擎的查询结果相关性低和缺少语义理解能力等问题,建立了一种基于本体的元搜索引擎模型。主要应用基于本体的用户个性模型和本体语义分析关联方法来提高元搜索引擎的查询效率。目的通过领域本体的语义理解应用,为用户提供查询意图个性化的有效推测和关键词本体的查询优化。     

基于本体的网络化制造资源检索

针对目前在网络化制造环境下制造资源检索过程中存在语义信息表达不足的问题，提出了一个基于本体的制造资源获取和智能检索系统结构。在此基础上，建立了一个多层次信息智能检索模型，并论述了语义检索相关算法，最后给出一个运行实例。     

Ontology learning: state of the art and open issues

Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneck of ontology acquisition in ontology development. Despite the significant progress in ontology learning research over the past decade, there remain a number of open problems in this field. This paper provides a comprehensive review and discussion of major issues, challenges, and opportunities in ontology learning. We propose a new learning-oriented model for ontology development and a framework for ontology learning. Moreover, we identify and discuss important dimensions for classifying ontology learning approaches and techniques. In light of the impact of domain on choosing ontology learning approaches, we summarize domain characteristics that can facilitate future ontology learning effort. The paper offers a road map and a variety of insights about this fast-growing field.     

Learning and inferencing in user ontology for personalized Semantic Web search

User modeling is aimed at capturing the users’ interests in a working domain, which forms the basis of providing personalized information services. In this paper, we present an ontology based user model, called user ontology, for providing personalized information service in the Semantic Web. Different from the existing approaches that only use concepts and taxonomic relations for user modeling, the proposed user ontology model utilizes concepts, taxonomic relations, and non-taxonomic relations in a given domain ontology to capture the users’ interests. As a customized view of the domain ontology, a user ontology provides a richer and more precise representation of the user’s interests in the target domain. Specifically, we present a set of statistical methods to learn a user ontology from a given domain ontology and a spreading activation procedure for inferencing in the user ontology. The proposed user ontology model with the spreading activation based inferencing procedure has been incorporated into a semantic search engine, called OntoSearch, to provide personalized document retrieval services. The experimental results, based on the ACM digital library and the Google Directory, support the efficacy of the user ontology approach to providing personalized information services.