知识网格中基于领域本体的智能检索

本文提出知识网格环境下基于领域本体的智能检索模型,采用OWL DL语言对领域知识进行形式化描述,支持推理和深层语义检索.＂标注＂和＂查询优化＂是检索的两个关键技术.通过规范的概念和概念间语义关系对文档片段进行标注,并针对＂一词多义＂问题提出＂主题-概念＂两阶段消歧算法.＂查询优化＂过程中,基于OWL DL推理的优化算法实现查询概念的自动扩展,提高了查全率和查准率.基于以上方法,建立航天领域本体,利用网上数据库开放资源作为测试集进行评测.实验显示,与传统基于     

一种改进的本体语义相似度计算及其应用

词语相似度研究,是知识表示以及信息检索领域中的一个重要内容.词语相似度的计算方法一般是利用大规模的语料库来统计.本体给词语间相似度计算带来了新的机会.利用本体结构上的ISA关系,提出了本体内部概念之间的相似度计算方法.实验结果表明,该方法能充分利用本体特点来计算相关概念之间的相似度.结合一个简单本体,介绍了如何计算概念间的相似度,及其在智能检索系统中的应用.     

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。     

基于多相关本体的模糊信息检索模型

根据概念及概念之间的语义,提出一种多相关本体的模糊信息检索模型,用本体的关系表示模糊关系。描述本体信息检索模型的处理过程及检索机制,讨论应用不同类型本体的检索效果和影响,并采用TREC的评价方法评估该模型。结果证明该模型具有较好的整体性能比,能改善用户需要的检索结果。     

一种基于本体语义的信息检索模型

对基于向量空间模型的检索方法进行改进,提出基于本体语义的信息检索模型。将WordNet词典作为参照本体来计算概念之间的语义相似度,依据查询中标引项之间的相似度,对查询向量中的标引项进行权值调整,并参照Word-Net本体对标引项进行同义和上下位扩展,在此基础上定义查询与文档间的相似度。与传统的基于词形的信息检索方法相比,该方法可以提高语义层面上的检索精度。     

基于本体的概念相似度计算

概念相似度的计算是信息检索领域的研究热点。本体在信息检索和人工智能领域的广泛应用,为概念相似度计算带来新的方法。该文提出一种利用本体来计算概念间相似度的方法,综合考虑语义距离和本体库统计特征。加入概念的深度、语义重合度和概念间强度的辅助影响。实验结果表明,该方法对概念相似度的计算有效,可应用于面向Web的信息检索。     

改进的本体语义相似度计算方法

概念的语义相似度研究,是知识表示以及信息检索领域中的一个重要内容。通过分析两种传统的语义相似度计算方法,对它们存在的问题进行改进,提出了一种综合的基于本体的概念语义相似度计算方法。该方法结合本体的DAG网状结构特征和语义距离计算中的多种语义影响因素,充分利用本体中概念的语义来计算概念间的语义相似度。实验结果比较合理,验证了该方法的有效性。     

一种基于领域本体的混合信息检索模型

针对语义检索中本体不能提供所有知识的问题,提出一种基于领域本体的混合信息检索模型。该模型利用领域本体中概念间的语义关系,结合关键词检索和语义检索,建立关键词基础矩阵和语义扩展矩阵两层索引矩阵,使系统检索在没有可用本体知识时能自动调整为关键词检索,保证一定的检索性能。两者的结合有效改善了检索性能。     

基于本体映射的零件工艺实例重用方法研究*

针对目前计算机辅助工艺设计中基于实例的工艺相似性重用问题,提出一种基于本体映射的零件工艺实例重用方法。在分析零件工艺实例信息的基础上,建立了新零件工艺知识本体和零件库零件工艺实例本体。基于本体映射的方法,通过本体间相似度计算,完成新零件工艺实例的检索和重用,并以轴类零件来说明该方法的有效性。     

基于应用本体的多卫星遥感数据检索

基于本体的遥感数据检索系统目前尚处于起步阶段,本体论(ontology)、语义网(semantic web)概念的提出推动了智能信息检索技术的发展,使得该类检索可以充分挖掘信息的深度与广度。如何构建一个关于遥感数据共享的应用本体,基于应用本体如何实现遥感数据检索系统的基础架构,是本篇文章所要讨论的重点内容。     

一种基于本体的概念相似度计算及其应用

概念的语义相似度研究,是知识表示以及信息检索领域中的一个重要内容。本文提出了基于语义相似度和相关度的综合概念相似度计算方法,考虑了语义距离和本体库特征,加入概念的信息重合度、概念的深度、概念的密度和不对称因子的辅助影响。通过实验和两种传统的语义相似度计算方法进行对比,本方法能更好地区分本体树中不同关系的概念对,验证了该方法的有效性。     

Image retrieval with a multi-modality ontology

Ontologies represent domain concepts and relations in a form of semantic network. Many research works use ontologies in the information matchmaking and retrieval. This trend is further accelerated by the convergence of various information sources supported by ontologies. In this paper, we propose a novel multi-modality ontology model that integrates both the low-level image features and the high-level text information to represent image contents for image retrieval. By embedding this ontology into an image retrieval system, we are able to realize intelligent image retrieval with high precision. Moreover, benefiting from the soft-coded ontology model, this system has good flexibility and can be easily extended to the larger domains. Currently, our experiment is conducted on the animal domain canine. An ontology has been built based on the low-level features and the domain knowledge of canine. A prototype retrieval system is set up to assess the performance. We compare our experiment results with traditional text-based image search engine and prove the advantages of our approach.     

A Legal Ontology Refinement Support Environment Using a Machine-Readable Dictionary

This paper discusses how to refine a given initial legal ontology using an existing MRD (Machine-Readable Dictionary). There are two hard issues in the refinement process. One is to find out those MRD concepts most related to given legal concepts. The other is to correct bugs in a given legal ontology, using the concepts extracted from an MRD. In order to resolve the issues, we present a method to find out the best MRD correspondences to given legal concepts, using two match algorithms. Moreover, another method called a static analysis is given to refine a given legal ontology, based on the comparison between the initial legal ontology and the best MRD correspondences to given legal concepts. We have implemented a software environment to help a user refine a given legal ontology based on these methods. The empirical results have shown that the environment works well in the field of Contracts for the International Sale of Goods.     

A Methodology to Create Legal Ontologies in a Logic Programming Based Web Information Retrieval System

Web legal information retrieval systems need the capability to reason with the knowledge modeled by legal ontologies. Using this knowledge it is possible to represent and to make inferences about the semantic content of legal documents. In this paper a methodology for applying NLP techniques to automatically create a legal ontology is proposed. The ontology is defined in the OWL semantic web language and it is used in a logic programming framework, EVOLP+ISCO, to allow users to query the semantic content of the documents. ISCO allows an easy and efficient integration of declarative, object-oriented and constraint-based programming techniques with the capability to create connections with external databases. EVOLP is a dynamic logic programming framework allowing the definition of rules for actions and events. An application of the proposed methodology to the legal web information retrieval system of the Portuguese Attorney General’s Office is described.     

基于本体的智能信息检索系统的构建方法

为了解决目前传统的信息检索工具返回大量无关的信息或漏检有用信息的问题,首先引入了本体的基本概念及其在信息检索中的用途,在此基础上提出了一种基于本体的智能信息检索系统模型.该模型首先使用基于SOM神经网络和分层聚类的两阶聚类算法自动的产生本体,免除了人工构造本体的繁琐,然后利用本体中概念及概念之间明确的关系描述,将用户提出的检索要求进行语义上的扩充,使信息检索过程更加智能化,大大提高了检索的查全率和查准率.     

Fuzzy reasoning framework to improve semantic video interpretation

A video retrieval system user hopes to find relevant information when the proposed queries are ambiguous. The retrieval process based on detecting concepts remains ineffective in such a situation. Potential relationships between concepts have been shown as a valuable knowledge resource that can enhance the retrieval effectiveness, even for ambiguous queries. Recent researches in multimedia retrieval have focused on ontology modeling as a common framework to manage knowledge. Handling these ontologies has to cope with issues related to generic knowledge management and processing scalability. Considering these issues, we suggest a context-based fuzzy ontology framework for video content analysis and indexing. In this paper, we focused on the way in which we modeled our fuzzy ontology: First, we populate automatically the generated ontology by gathering various available video annotation datasets. Then, the ontology content was used to infer enhanced video semantic interpretation. Finally, considering user feedback, the content of the ontology was improved. Experimental results showed that our approach achieves the goal of scalability while at the same time allowing better video content semantic interpretation.     

利用本体的视频语义概念检测

视频数据的不断丰富以及人们对视频检索的要求越来越复杂,使得视频语义信息建模和高层语义概念提取逐渐成为视频检索中的重要组成部分.本文提出一种基于本体的视频语义概念检测方法,利用贝叶斯网络构造视频中概念语义关系的检测本体,构建了视频中概念之间的层次关系,并能够通过推理完成复合语义概念的检测.该方法从语义信息学的角度对视频内容进行分析,在一定程度上削弱了语义鸿沟的影响,并且取得了较好的查询结果.     

Relating ontologies with a fuzzy information model

More people than ever before have access to information with the World Wide Web; information volume and number of users both continue to expand. Traditional search methods based on keywords are not effective, resulting in large lists of documents, many of which unrelated to users’ needs. One way to improve information retrieval is to associate meaning to users’ queries by using ontologies, knowledge bases that encode a set of concepts about one domain and their relationships. Encoding a knowledge base using one single ontology is usual, but a document collection can deal with different domains, each organized into an ontology. This work presents a novel way to represent and organize knowledge, from distinct domains, using multiple ontologies that can be related. The model allows the ontologies, as well as the relationships between concepts from distinct ontologies, to be represented independently. Additionally, fuzzy set theory techniques are employed to deal with knowledge subjectivity and uncertainty. This approach to organize knowledge and an associated query expansion method are integrated into a fuzzy model for information retrieval based on multi-related ontologies. The performance of a search engine using this model is compared with another fuzzy-based approach for information retrieval, and with the Apache Lucene search engine. Experimental results show that this model improves precision and recall measures.     

多民族语言本体知识库构建技术

语义本体是共享概念模型的显示的形式化规范说明,其目标是将杂乱无章的信息源转变为有序易用的知识源。语义本体知识库的构建是文本自动处理的一个重要环节,跨语言信息检索、信息抽取、自动翻译等领域中都有广泛的应用。该文旨在描述统一标准、统一接口的多民族语言本体知识库的创建思路,以及包含的若干问题,例如 多民族语言中共有概念的一般表示与各民族语言特有的事物表达方式的规律,基于词汇语义的、包括汉语、英语及少数民族语言在内的多民族语言语义本体的表示理论与方法等。     

Mapping queries to the Linking Open Data cloud: A case study using DBpedia

We introduce the task of mapping search engine queries to DBpedia, a major linking hub in the Linking Open Data cloud. We propose and compare various methods for addressing this task, using a mixture of information retrieval and machine learning techniques. Specifically, we present a supervised machine learning-based method to determine which concepts are intended by a user issuing a query. The concepts are obtained from an ontology and may be used to provide contextual information, related concepts, or navigational suggestions to the user submitting the query. Our approach first ranks candidate concepts using a language modeling for information retrieval framework. We then extract query, concept, and search-history feature vectors for these concepts. Using manual annotations we inform a machine learning algorithm that learns how to select concepts from the candidates given an input query. Simply performing a lexical match between the queries and concepts is found to perform poorly and so does using retrieval alone, i.e., omitting the concept selection stage. Our proposed method significantly improves upon these baselines and we find that support vector machines are able to achieve the best performance out of the machine learning algorithms evaluated.