本体匹配中基于词义组合的词法分析算法

针对本体匹配中相似性词法分析算法的不足,提出一种基于词义组合的词法分析算法.该算法首先通过WordNet发现本体中单词的合适词义,并扩展它们,然后基于本体里的语义元素形式化的定义实体的词法信息标记,最后推理出实体词法信息间的包含关系.针对一组工业本体的测试结果表明该算法有助提高系统的覆盖率.     

结合DL-safe规则发现日志本体频繁模式的方法

为发现语义Web使用记录中所蕴含的有效信息,本文提出了一种挖掘日志本体频繁Web访问模式的方法。该方法引入应用访问规则集和观察集分别表示日志信息动态变化的语义规则和使用事实,并在DL安全的限定下将日志本体和应用访问规则集相结合构成一个推理过程可判定的混合知识库。在此基础上,利用日志本体中事件整分关系的语义构建访问模式学习的事务模型,并采用ILP的方法学习生成频繁用户访问模式树,解决了推理访问模式中非描述逻辑原子的问题。实验结果表明该方法的可用性和有效性。     

一种基于语义的UDDI服务发现机制

针对目前UDDI缺乏对语义推理支持的情况,用OWL-S本体语言描述服务,并给出一种在传统UDDI注册中心之外新建一层Sem-UDDI注册中心的架构。使用Sem-UDDI注册中心灵活地扩展服务的语义信息,并由服务匹配引擎计算服务匹配的语义相似度,记录到服务匹配表中。通过直接从服务匹配表中查找服务,提高匹配效率增强服务发现的能力。     

跨语言信息检索中的最关联英文语义翻译选取

针对跨语言信息检索中关联英文翻译的选择准确度不高的问题,提出一种基于最关联语义本体模型匹配的跨语言信息检索英文翻译选取方法。首先构建跨语言信息检索中最关联英文语义的本体结构模型,采用语义指向性信息索引方法进行英文翻译的上下文语义映射;然后根据语义本体之间的词语知识和本体片段映射方法进行英文语义翻译的特征提取,实现最关联英文语义翻译选取;最后进行实验测试分析。结果表明,采用该方法进行跨语言信息检索,英文语义翻译选取的召回性能较好,查全率、查准率较高。     

基于动态词典匹配的语义增强中文命名实体识别算法

在命名实体识别任务中,运用词典匹配的方法能够添加丰富的文本特征,但匹配到的词组信息多使用静态归一化的方法,缺乏自动推理能力.提出了基于动态词典匹配的语义增强中文命名实体识别方法.对输入句子中的字符,在词典中进行动态词组匹配,利用神经网络对词组加权,结合word2vec与ALBERT得到字符的增强特征表示;在序列建模层运...     

基于知识图谱信息协同传播的推荐模型

针对当前基于知识图谱的推荐模型没有充分挖掘知识图谱语义结构信息的问题,提出一种融合知识图谱表示学习方法和信息协同传播机制的推荐模型KCOD。KCOD基于经典的知识图谱表示学习模型DistMult与TransR建模并推理实体三元组的语义关系,然后通过交叉计算每一阶历史交互实体向量推理结果与候选物品实体向量推理结果的相似度,进行模型训练及偏好预测。实验结果显示KCOD的性能优于经典对比模型。     

基于本体和贝叶斯网络的Deep Web集成系统研究

Deep Web指无法简单地通过搜索引擎或网络爬虫能够检索到的隐藏在后台数据库中,而往往这些内容具有丰富的信息和数据.获取Deep Web所蕴含的丰富信息的有效方法是构建Deep Web集成框架,而查询接口作为Deep Web的唯一访问接口,所以Deep Web集成系统的关键就是构建Deep Web集成接口.研究的目标是通过自动构建特定领域的本体来表示Deep Web接口信息,从而能够自动识别该领域Deep Web接口来建立索引,提取数据库中丰富的资源.在没有人为干预的情况下展开整个过程.本文的方法能完全自动地提取Deep Web接口信息并派生领域本体,并通过本体贝叶斯网络识别新Deep Web接口,进行匹配.在特定领域,通过一种新的自动从Deep Web接口中提取属性方法,通过WordNet构建成本体语义树,运用得到的领域语义本体树结合贝叶斯网络完成领域分类,并在分类后进行查询接口与集成接口的模式匹配.本文提出的方法通过对比人工提取属性构成的语义树在分类和模式匹配的结果进行对比,验证了该方法的可用性和适用性.     

基于本体的语义Web服务注册平台研究

提出了一种基于本体的语义Web服务注册模型,结合Jena实现语义Web服务的语义查询和推理.应用元模型互操作框架(MFI)中的本体注册元模型标准,以实现与其他注册模型的语义互操作,并给出了一个基于该模型的语义Web服务注册平台.     

基于SVSM的装备故障案例相似度匹配算法

分析和总结了电子防护装备故障案例的特点,设计了电子防护装备故障诊断的领域本体。利用本体在表示案例时所体现的语义关联信息,提出了基于领域本体的语义特征向量空间模型(Semantic Vector Space Model,SVSM),弥补了传统的向量空间模型在描述故障特征项语义关联度时忽略了文档词条的位置和结构因素的缺陷。引入自定义权值的语义特征项标志位,提出了一种改进的基于案例推理的电子防护装备故障诊断算法,提高了SVSM各维度值的准确性和相似度匹配算法的自由度。通过实验,验证了算法的有效性和可靠性。     

基于本体的面向短文本的饰品设计知识服务

针对产品设计过程中大量知识是以短文本的方式存储,难以进行高效、准确的知识服务的问题,提出了一种基于本体的短文本知识集成方法.首先,构建产品设计的领域本体;然后,根据本体所蕴含的语义信息,对短文本进行分析,从而找到蕴含用户所需知识的相关短文本,并通过知识服务将其提供给设计人员.并在某企业的饰品三维辅助设计环境下进行应用验证,结果表明,该方法能够提供高效、准确的饰品辅助设计知识.     

Social Networking Ontology Engineering Walkthrough: Practical Approach for Non-Expert User Learning

The utilization of ontologies in knowledge management has become very well accepted in many approaches for instance database, semantics, agent, natural language processing, etc. Artificial intelligent researchers have originally applied the word “ontology” from Philosophy fields which means “being existence”, then the word spread in many scientific domain and ontologies are now used in several developments. The purpose of this research is to present the simple methodologies and methods used to build/adapt/integrate ontologies from the system requirement or by reusing and re-engineering other ontologies; also a process of merging approach will be addressed. The normal ontology engineering will not be suitable for non-expert user; and there are some tools for helping the user to accomplish the ontology engineering task, however, it is not that simple. This paper mentioned the observation and figure out the practical methodology for non-expert user to learn and create simple ontology with little background of them. This paper mentioned the ontology and ontology engineering revisits. Also the main section in this paper mentioned the scenario walkthrough about reality of how non-expert user creates the simple ontology by using suggested and existing methodologies. The last section is recommendation and suggestion from the research experimental which explain the proper ontology developing methodology for non-expert user.     

基于概念语义协商的服务Agent划分

 服务参与者Agent之间使用异构的私有本体概念构造消息并进行交互,会由于消息语义的错误理解而影响服务发现的效率和结果的准确性.提出使用概念语义协商的方法改善服务Agent之间的理解能力;定义了服务Agent模型和本体概念解释元组,设计了服务Agent协商行为原语以及协商请求和响应协议;根据概念语义协商结果对私有本体进行更新并根据本体概念理解的相似度对参与服务的Agent进行划分,使功能相近且能互相理解的Agent处于相同的服务社区.实验表明,通过语义协商的服务Agent划分方法,能够有效提高同一服务社区服务Agent之间的概念理解程度,在协商概念数为2和8时,该方法较其他方法能够将概念理解错误率减少平均大约30%.     

Ontology Management in Enterprises

In today's increasingly competitive business market, many large organisations have begun to research and develop flexible enterprises in order to be able to quickly respond to market opportunities. They are seeking ways to maximise the power of information assets stored across hundreds of their databases and application programs by bringing them into open interoperable environments. However, this effort has been seriously hindered by various kinds of heterogeneity. Each system has its own domain model for its environment and efficient task operations. However, in order for systems to communicate and co-operate effectively, a shared domain model is required. The semantics of the common, and each local, model must be captured explicitly and formally to enable meaningful information exchange.The semantics of diverse information resources are captured by ontologies — definitions of terms as used in data sources, i.e. concepts and the relationships between concepts. When defining the relationships between data sources, we rely on ontologies to make the meaning of the different vocabularies used explicit. This paper explores the role of ontologies in enterprises, and proposes a methodology for managing enterprise ontology resources and a suite of support tools.     

Measuring Semantic Similarity Between Biomedical Concepts Within Multiple Ontologies

Most of the intelligent knowledge-based applications contain components for measuring semantic similarity between terms. Many of the existing semantic similarity measures that use ontology structure as their primary source cannot measure semantic similarity between terms and concepts using multiple ontologies. This research explores a new way to measure semantic similarity between biomedical concepts using multiple ontologies. We propose a new ontology-structure-based technique for measuring semantic similarity in single ontology and across multiple ontologies in the biomedical domain within the framework of Unified Medical Language System (UMLS). The proposed measure is based on three features: 1) cross-modified path length between two concepts; 2) a new feature of common specificity of concepts in the ontology; and 3) local granularity of ontology clusters. The proposed technique was evaluated relative to human similarity scores and compared with other existing measures using two terminologies within UMLS framework: Medical Subject Headings and Systemized Nomenclature of Medicine Clinical Term. The experimental results validate the efficiency of the proposed technique in single and multiple ontologies, and demonstrate that our proposed measure achieves the best results of correlation with human scores in all experiments.      

Mixing semantic networks and conceptual vectors application to hyperonymy

In this paper, we focus on lexical semantics, a key issue in natural language processing that tends to converge with conceptual knowledge representation and ontologies. When ontological representation is needed, hyperonymy, the closest approximation to the is-a relation, is at stake. In this paper we describe the principles of our vector model (conceptual vector model) and show how to account for hyperonymy within the vector-based frame for semantics. We show how hyperonymy diverges from is-a and what measures are more accurate for hyperonymy representation. Our demonstration results in initiating a cooperation process between semantic networks and conceptual vectors. Text automatic rewriting or enhancing, ontology mapping with natural language expressions, are examples of applications that can be derived from the functions we define in this paper.     

基于演化传播模型的本体内聚程度分析

本体演化是本体工程领域的研究热点之一,当前研究大多侧重于演化策略或演化过程中使用的技术的研究,对于如何最化本体适应变化的能力至今没有明确结论,本文从一个全新的角度来解决这一问题,通过计算给定本体的内聚程度来量化本体适应变化的能力.本文提出了一种包含本体变更影响信息的演化传播模型;定义了能使该模型形式化表示的编码ETMC...     

Design and development of dynamic Agri-ontology for IoT interoperability

In recent years, the usage and applications of Internet of Things (IoT) have increased exponentially. IoT connects multiple heterogeneous devices like sensors, micro controllers, actuators, smart devices like mobiles, watches, etc. IoT contributes the data produced in the context of data collection, including the domains like military, agriculture, healthcare, etc. The diversity of possible applications at the intersection of the IoT and the web semantics has prompted many research teams to work at the interface between these two disciplines. This makes it possible to collect data and control various objects in transparent way. The challenge lies in the use of this data. Ontologies address this challenge to meet specific data needs in the IoT field. This paper presents the implementation of a dynamic agriculture ontology-building tool that parses the ontology files to extract full data and update it based on the user needs. The technology is used to create the angular library for parsing the OWL files. The proposed ontology framework would accept user-defined ontologies and provide an interface for an online updating of the owl files to ensure the interoperability in the agriculture IoT.     

本体的继承及一致性分析

本体的继承理论是目前尚未解决的重要问题.该文通过扩展继承的数学理论,系统研究了本体的继承语义、继承机制等问题.主要工作有:(1)继承系统的数学理论是描述继承系统和探讨其一致性、二义性等问题的一个重要基础理论.该理论已被广泛引用.但使用该理论描述允许例外的类/属性继承系统会出现假冲突.本文发现并定义了假冲突,扩展了原理论的单词表,重新定义了结论集、可继承等概念,使继承断言的接地扩展集不因假冲突而产生二义.从而解决了假冲突的问题.(2)证明原继承系统中的大部分重要定理在扩展后的继承系统中仍然成立,并且提出了新的定理3)将本体(D,P,U,V,H)转换为三元组(F,E,I)的表示形式,证明这种转换不削弱本体的表示能力,并且将本体映射到扩展后的继承系统,利用继承系统的定义、定理分析本体继承的一致性问题,并证明这种映射对本体的包含断言的判定是完备的.4)设计了上、下扫描算法,通过检测系统变化可能引起的冲突,保持系统的一致性.对本体继承的一致性分析为发展迅速的面向本体工程方法提供了有力支持.