基于NNV关联规则的非分类关系提取方法及其应用研究

针对传统的非分类关系提取方法无法获得非分类关系的名称的不足,提出基于NNV(noun-noun-verb,名词名词动词)关联规则的非分类关系提取方法。给出NNV关联规则的相关概念及方法的实现过程,提取了民航突发事件应急管理领域本体中的非分类关系,完善了民航突发事件应急管理领域本体。与传统的非分类关系提取方法相比,有效获取了非分类关系的名称,保证了结果的准确率和召回率。     

一种领域概念非分类关系的获取方法

领域概念非分类关系的获取是本体学习的一项重要任务,提出了一种基于非监督学习的非分类关系自动获取方法。该方法首先通过关联规则获取特定领域概念对,然后将概念对之间的高频动词作为候选的非分类关系标签,接着利用VF*ICF度量法来确定非分类关系标签,最后通过对数似然比评估方法将得到的非分类关系标签分配给对应的领域概念对。实验结果表明该方法可以有效提高非分类关系抽取的准确率和召回率。     

基于文本集的中文本体学习

本文探讨了如何利用领域文本集来自动构建领域本体的技术,以辅助知识工程师方便快捷地构建领域本体.文中提出一种利用概念之间的语义相似度,通过蚁群聚类算法对概念集进行聚类,最后利用知网的义原层次结构抽取分类关系的算法,通过非对称簇分析函数评价概念间的关联度,以提取非分类关系,最终生成领域本体.实验证明了该本体学习系统的有效性.     

一种本体学习中分类关系提取方法的研究

本体学习技术是利用本体工程技术和机器学习技术等众多学科技术来实现本体的自动半自动构建,可解决本体手工构建的不足。根据本体学习目前的研究现状,提出了一种从文本中半自动获取本体中分类关系的实现,讨论了本体学习中概念抽取和概念间分类关系抽取等关键技术。实现了本体中分类关系提取,对于非分类关系的提取还有待研究。     

一种本体学习中分类关系提取方法的研究

本体学习技术是利用本体工程技术和机器学习技术等众多学科技术来实现本体的自动半自动构建，可解决本体手工构建的不足。根据本体学习目前的研究现状，提出了一种从文本中半自动获取本体中分类关系的实现，讨论了本体学习中概念抽取和概念间分类关系抽取等关键技术。实现了本体中分类关系提取，对于非分类关系的提取还有待研究。     

一种基于Web的通用本体学习框架

提出一种通用的多策略本体学习框架,通过对Web上各专业领域文档集进行挖掘来实现本体自动构建。讨论本体学习中本体概念的抽取、概念之间语义关系的抽取和分类体系的自动构建等关键技术,通过实验对算法进行测试和评价。由于集成了多种机器学习算法,该方法在概念抽取和语义关系学习方面具有更高的准确性,采用通用本体WordNet和HowNet作为语料库,可适用于不同的专业领域。通过按需获取Web文档,该方法能实时生成本体。     

基于本体的文本分类方法

文本分类是智能科学研究中的霞要问题,如何能使分类结果更精确一直是研究的重点.本体技术兴起之后,利用本体作为词典,从而在语义上进行扩展的方法使分类的精确度有丁改善.但是,本体自身的结构并没有得到应用,多类别多标签的分类也没有因此得到大幅度改进.作为领域内公认的概念体系,本体自身的结构也是一种非常好的分类标准,为多类别多标签的分类提供了很好的载体.针对多类别多标签分类问题,提出了以本体为类别指导,基于模糊数学规划思想的分类方法.将本体概念作为分类标准,将文本散列到相应的本体概念类别中,并给出其相关程度.实验验证了该方法能够做到更细致的分类结果,改善了多类别多标签分类的能力.此外,该方法还能够改善数据在语义Web网中的传输效果.     

一种改进的领域本体分类算法

本体学习技术的研究目前还处于探索阶段。研究了知识生产领域中本体学习技术的应用,提出了本体学习中领域本体的分类算法。本算法同时考虑了概念之间的语义相似度与结构相关度,并以"分类量化值"作为领域本体的分类标准。实验证明,本算法较之其它相关算法更为有效。     

基于网站结构的领域本体学习方法

为了完善问答系统、搜索平台等信息检索系统对本体的应用需求,提出了一种基于网站结构的领域本体学习方法,该方法对网站结构进行分析,通过挖掘网站结构中所隐藏的知识进行本体学习,实践证明该方法可以更快、更准确地获取特定领域中的概念、概念层次以及概念间的非分类关系,最终帮助构建本体。     

Pellet本体概念分类算法优化

针对Pellet1.4(简称Pellet)推理机的本体概念分类算法效率较低,为了通过减少调用ABOX推理概念间包含关系来提高分类性能,提出了一种全局标记方法.方法首先对所有节点嵌入一个二维散列mark,用于节点存储与其他节点的包含关系标记.然后在推理过程中通过ABOX的推理结果.动态修改相关节点中mark的标记信息.最后充分利用以前的推理结果所修改的标记信息来减少ABOX一致性检测次数.通过使用全局标记方法的算法替代Pellet推理机中原有概念分类算法对实际本体分类进行仿真,实验结果表明该方法可以有效地减少调用ABOX执行一致性检测次数,提高本体概念分类性能.     

基于扩展关联规则的中文非分类关系抽取

提出一种扩展的关联规则法用于抽取中文非分类关系,在利用普通关联规则抽取出非分类关系概念对后,通过语言学规则抽取相应的非分类关系名称。该方法克服了普通关联规则法无法得出具体非分类关系名称的缺点,能够确定非分类关系的定义域和值域。实验结果表明仅使用扩展的关联规则法进行非分类关系抽取即可完成任务,无须对概念对与＂谓语＂动词之间的紧密程度进行验证。     

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.     

A multi-phase correlation search framework for mining non-taxonomic relations from unstructured text

Over the last decade, ontology engineering has been pursued by “learning” the ontology from domain-specific electronic documents. Most of the research works are focused on extraction of concepts and taxonomic relations. The extraction of non-taxonomic relations is often neglected and not well researched. In this paper, we present a multi-phase correlation search framework to extract non-taxonomic relations from unstructured text. Our framework addresses the two main problems in any non-taxonomic relations extraction: (a) the discovery of non-taxonomic relations and (b) the labelling of non-taxonomic relations. First, our framework is capable of extracting correlated concepts beyond ordinary search window size of a single sentence. Interesting correlations are then filtered using association rule mining with lift interestingness measure. Next, our framework distinguishes non-taxonomic concept pairs from taxonomic concept pairs based on existing domain ontology. Finally, our framework features the usage of domain related verbs as labels for the non-taxonomic relations. Our proposed framework has been tested with the marine biology domain. Results have been validated by domain experts showing reliable results as well as demonstrate significant improvement over traditional association rule approach in search of non-taxonomic relations from unstructured text.     

基于多策略本体构建方法研究

针对手工构建本体工作量大、效率低以及更新维护困难等问题,文章提出了一种中文环境下多策略本体学习方法。使用统计分析和语义关联抽取术语,并利用构词模式发现分类关系,针对不同类型的非分类关系,分别采用句法模式、统计方法和基于规则方法,从而解决了现有本体学习方法对词典依赖性、处理中文效果差等问题。实验结果表明,该方法有较好地有效性和稳定性。     

Comparing relationships in conceptual modeling: mapping to semantic classifications

Much of the research that deals with understanding the real world and representing it in a conceptual model uses some form of the entity-relationship model as a means of representation. This research proposes an ontology for classifying relationship verb phrases based upon the domain and context of the application within which the relationship appears. The classification categories to which the verb phrases are mapped were developed based upon prior research in databases, ontologies, and linguistics. The usefulness of the ontology for comparing relationships when used in conjunction with an entity ontology is discussed. Together, these ontologies can be effective in comparing two conceptual database designs for integration and validation. Empirical testing of the ontology on a number of relationships from different application domains and contexts illustrates the usefulness of the research.     

基于多特征表示的本体概念挂载

该文研究了一种基于多特征表示的本体概念挂载方法。以中国大百科知识体系作为本体体系结构,抽取网络知识库条目作为本体概念,通过分析条目中文本内容、语义标签和半结构化信息获得本体概念间层级关系。该文将中国大百科知识体系扩展为百万级概念的多领域中文本体,为进一步抽取本体概念的属性、概念之间的非层级关系以及支持问答服务等应用建立了良好的基础。实验证明该方法相对于单一特征方法能够提高11.8%的挂载精度。     

基于事件本体的突发事件领域知识建模

基于本体的知识建模方法有很多,在某些特定领域采用传统的本体建模方法存在着一些不足。以突发事件领域为例,提出了基于事件本体的知识建模方法。该模型分为上层事件类、下层事件类和事件实例,上层事件类描述的抽象的事件的分类体系,下层事件类是通过事件类关系组成的事件格结构。该模型不仅可以描述事件的时间、地点、对象等要素,还能描述事件类之间的关系。采用本体建模工具Protégé来构建突发事件领域本体,并以“恐怖袭击”作为实例验证了该模型的可用性。研究结果表明,该模型可以清晰地描述事件类的完整性,语义清晰,扩展性强。     

一种领域本体构建方法及其在相片管理中的应用

自20世纪90年代以来,本体逐渐被应用到很多领域,本体的构建方法论也成为国内外机构的热门研究课题。在分析和总结现有的本体构建经验的基础上,提出了一种领域本体的构建方法,它符合人们的思维认知规律,可操作性和扩展性强。并使用该方法为相片管理领域进行本体建模,实验表明了该方法的科学性和有效性。     

Automated ontology construction for unstructured text documents

Ontology is playing an increasingly important role in knowledge management and the Semantic Web. This study presents a novel episode-based ontology construction mechanism to extract domain ontology from unstructured text documents. Additionally, fuzzy numbers for conceptual similarity computing are presented for concept clustering and taxonomic relation definitions. Moreover, concept attributes and operations can be extracted from episodes to construct a domain ontology, while non-taxonomic relations can be generated from episodes. The fuzzy inference mechanism is also applied to obtain new instances for ontology learning. Experimental results show that the proposed approach can effectively construct a Chinese domain ontology from unstructured text documents.     

Learning non-taxonomic relationships from web documents for domain ontology construction

In recent years, much effort has been put in ontology learning. However, the knowledge acquisition process is typically focused in the taxonomic aspect. The discovery of non-taxonomic relationships is often neglected, even though it is a fundamental point in structuring domain knowledge. This paper presents an automatic and unsupervised methodology that addresses the non-taxonomic learning process for constructing domain ontologies. It is able to discover domain-related verbs, extract non-taxonomically related concepts and label relationships, using the Web as corpus. The paper also discusses how the obtained relationships can be automatically evaluated against WordNet and presents encouraging results for several domains.