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。     

移动信息服务中用户兴趣建模研究

本文充分考虑到移动设备的特点,对移动环境下用户兴趣模型的建立和更新方法进行了详细论述。通过爬取用户已下载浏览的WAP页面,分析用户对Wap页面的兴趣度,挖掘用户兴趣。基于ODP建立用户兴趣领域本体,采用基于领域本体的加权关键词用户兴趣表示方法。该模型能准确描述移动用户的兴趣及其动态变化过程,为移动个性化服务打下基础。     

基于本体的数字图书馆个性化用户模型表示

本文针对当前个性化服务中基于关键词的用户兴趣表示方法在语义上的不足,结合本体语义信息丰富的特点,提出了一种基于本体的用户模型表示方法。在数字图书馆领域内,介绍了本体形式化描述并构建了数字图书馆领域本体,给出了用户模型的表示方法。并以个性化信息检索为例,说明了利用用户兴趣本体表示中的同义,上下位等关系给用户提供服务的方法。实验表明基于本体的表示方法能够给用户提供更加个性化的信息。     

基于本体的用户兴趣模型的更新方法

本文描述了用户兴趣变化,给出了用户兴趣模型初始化方法。基于领域本体,提出了单向激活扩散的用户兴趣模型的更新方法,最后通过一个实例说明了更新过程。     

基于本体的个性化信息检索技术研究

针对传统的采用关键词的信息检索方式在个性化方面的不足,提出了一种根据领域本体的个性化信息检索模型.首先获取用户的特征信息,接着利用用户的特征信息构建基于本体的用户兴趣模型,在检索过程中通过领域本体概念和用户兴趣模型对检索请求进行分析并对其进行扩展,获得符合检索意图的结果,在此基础上按兴趣度大小处理后将最终个性化信息检索结果反馈给用户.通过搭建关于本体的个性化检索原型系统,仿真结果验证了改进模型的有效性.     

个性化信息检索中用户兴趣建模与更新研究

个性化信息检索系统的实时性关键在于如何动态更新用户兴趣模型。针对原有方法的不足,改进用户兴趣模型的描述与更新方式。首先根据网页文档的特征改进TF-IDF(Term Frequency-Inverse Document Frequency)算法,以此作为用户兴趣特征词的权重,同时通过引入领域本体,将用户兴趣特征项进行语义扩展,并根据用户浏览行为,改进其用户兴趣主题计算方式,并在此基础上提出用户兴趣模型的更新与遗忘机制。实验对比结果表明,该方法能够捕捉用户兴趣的变化,进一步提高个性化信息检索的准确度与用户满意度。     

基于领域本体的跨系统个性化服务用户模型

目前大多数个性化服务系统采用关键词或关键词向量空间模型表示用户个人偏好,该方法严重制约了用户模型在不同系统间的共享和重用。针对上述问题,提出一种基于领域本体的用户模型,给出其形式化描述,通过引入个性化兴趣度实现用户个人偏好的量化,介绍该模型在群体用户建模中的应用过程。实验表明,该用户模型能准确反映用户兴趣且更新能力和可扩展性较强。     

基于领域本体的用户模型的研究*

目前大多数知识管理系统采用基于关键词或关键词向量空间模型表示用户的兴趣偏好。针对该方法不包含语义信息,很难准确表示用户感兴趣的信息,并且难于扩展,提出一种基于领域本体的用户模型。该模型利用用户访问量,采用改进的相似度算法,实现用户分类建立用户模型,体现用户个人偏好。最后将该模型应用于齐齐哈尔货车快速设计系统中,应用表明该模型能准确地反映用户兴趣,且提高了信息检索效率。     

基于Ontology的用户模型研究

针对现有本体用户模型的难点与不足，提出了一种改进的基于领域本体的用户模型（OBUM），利用文本挖掘技术构建领域本体，通过本体学习来完成用户模型的学习和更新。     

基于本体的领域知识搜索研究

对当前互联网的搜索引擎中无法准确、高效地返回用户真正所需的数据信息等缺点进行分析,利用本体构建领域知识库和本体推理机等方法,设计实现了一个基于本体的领域知识检索系统模型。用户通过对用户本体的选择,可得到相关的目标本体,继续对其追踪,通过本体推理,搜索引擎能准确地搜索到用户所关心的本领域内的相关知识。讨论了该系统结构中各个模块的功能和该系统的特点。     

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.     

基于农业本体的用户兴趣模型研究

为了有效解决传统用户兴趣模型查不全,查不准等问题,引入农业本体技术构建用户兴趣模型。该模型能在语义层次上理解用户的兴趣,因而在检索时能获取较满意的查全率和查准率,能更好的体现农户的个性化需求。     

A personalized query expansion approach for engineering document retrieval

Engineers create engineering documents with their own terminologies, and want to search existing engineering documents quickly and accurately during a product development process. Keyword-based search methods have been widely used due to their ease of use, but their search accuracy has been often problematic because of the semantic ambiguity of terminologies in engineering documents and queries. The semantic ambiguity can be alleviated by using a domain ontology. Also, if queries are expanded to incorporate the engineer’s personalized information needs, the accuracy of the search result would be improved. Therefore, we propose a framework to search engineering documents with less semantic ambiguity and more focus on each engineer’s personalized information needs. The framework includes four processes: (1) developing a domain ontology, (2) indexing engineering documents, (3) learning user profiles, and (4) performing personalized query expansion and retrieval. A domain ontology is developed based on product structure information and engineering documents. Using the domain ontology, terminologies in documents are disambiguated and indexed. Also, a user profile is generated from the domain ontology. By user profile learning, user’s interests are captured from the relevant documents. During a personalized query expansion process, the learned user profile is used to reflect user’s interests. Simultaneously, user’s searching intent, which is implicitly inferred from the user’s task context, is also considered. To retrieve relevant documents, an expanded query in which both user’s interests and intents are reflected is then matched against the document collection. The experimental results show that the proposed approach can substantially outperform both the keyword-based approach and the existing query expansion method in retrieving engineering documents. Reflecting a user’s information needs precisely has been identified to be the most important factor underlying this notable improvement.     

Interest based selection of user generated content for rich communication services

The last few years, we have witnessed an exponential growth in available content, much of which is user generated (e.g. pictures, videos, blogs, reviews, etc.). The downside of this overwhelming amount of content is that it becomes increasingly difficult for users to identify the content they really need, resulting into considerable research efforts concerning personalized search and content retrieval.On the other hand, this enormous amount of content raises new possibilities: existing services can be enriched using this content, provided that the content items used match the user's personal interests. Ideally, these interests should be obtained in an automatic, transparent way for an optimal user experience.In this paper two models representing user profiles are presented, both based on keywords and with the goal to enrich real-time communication services. The first model consists of a light-weight keyword tree which is very fast, while the second approach is based on a keyword ontology containing extra temporal relationships to capture more details of the user's behavior, however exhibiting lower performance. The profile models are supplemented with a set of algorithms, allowing to learn user interests and retrieving content from personal content repositories.In order to evaluate the performance, an enhanced instant messaging communication service was designed. Through simulations the two models are assessed in terms of real-time behavior and extensibility. User evaluations allow to estimate the added value of the approach taken. The experiments conducted indicate that the algorithms succeed in retrieving content matching the user's interests and both models exhibit a linear scaling behavior. The algorithms perform clearly better in finding content matching several user interests when benefiting from the extra temporal information in the ontology based model.     

基于本体的军事装备知识建模及分析

针对本体建模过程中不同受众具有不同应用需求的问题,结合本体建模七步法和软件开发螺旋模型提出基于需求的螺旋反馈法.首先,明确领域知识范畴和用户需求,进行本体总体设计;其次,领域专家依据本体构建原则评估知识体系;再次,组装知识体系模型,定义本体属性,创建实例,完成本体详细设计;最后,用户评估反馈,进行新一轮的增量迭代,直至...     

An experimental evaluation of ontology-based user profiles

In recent years, a number of research works have been carried out to improve the information retrieval process by exploiting external knowledge, e.g. by employing ontologies. Even though ontologies seem to be a promising technique to improve the retrieval process, hardly any study has been performed to evaluate the use of ontologies over a longer time period to model user interests. In this work we introduce an ontology based video recommender system that exploits implicit relevance feedback to capture users’ evolving information needs. The system exploits a generic ontology to organise users’ interests. We evaluate the recommendations by performing a user-centred multiple time-series study where participants were asked to include the system into their daily news gathering routine. The results of this study suggest that the system can be successfully employed to improve personal information seeking tasks in news domain.     

基于本体的个性化协同推荐

推荐系统已成为减轻信息过载时用户负担的关键工具,由于要处理不同形式的用户交互,因此协同推荐要与用户的具体情况和不断变化的兴趣相关。基于此,提出建立上下文相关的协同推荐,以领域本体的形式包含语义知识,把用户配置定义为一个本体。文章描述用户配置本体如何学习、增量更新和如何用于协同推荐。     

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.     

基于情境感知的餐饮O2O推荐系统研究

餐饮O2O推荐具有情境敏感性,而普适计算和移动互联网的发展为更全面、更实时的情境信息的获取提供了基础,也使得对情境与推荐对象进行知识表示和推理成为提高推荐质量的关键。充分考虑移动商务活动中情境对用户需求的影响,设计了基于情境感知的领域本体模型结构并研究模型的实例化,通过规则推理实现餐饮O2O推荐。在此基础上,提出基于关联分析的情境规则生成方法,根据用户的历史行为挖掘情境与推荐对象的通用关联模式。并通过基于内容推荐的用户兴趣模型与菜品特征模型来表示个人对菜品的特殊兴趣偏好,构建了基于情境和基于内容相融合的混合推荐系统。实验结果表明,该方法有效解决了基于内容推荐的用户冷启动问题,并可以提高餐饮O2O推荐的准确性。