An Ontology Based Cyclone Tracks Classification Using SWRL Reasoning and SVM

Tropical cyclones(TC)are often associated with severe weather conditions which cause great losses to lives and property.The precise classification of cyclone tracks is significantly important in thefield of weather forecasting.In this paper we propose a novel hybrid model that integrates ontology and Support Vector Machine(SVM)to classify the tropical cyclone tracks into four types of classes namely straight,quasi-straight,curving and sinuous based on the track shape.Tropical Cyclone TRacks Ontology(TCTRO)described in this paper is a knowledge base which comprises of classes,objects and data properties that represent the interaction among the TC characteristics.A set of SWRL(Semantic Web Rule Language)rules are directly inserted to the TCTRO ontology for reasoning and inferring new knowledge from ontology.Furthermore,we propose a learning algorithm which utilizes the inferred knowledge for optimizing the feature subset.According to experiments on the IBTrACS dataset,the proposed ontology based SVM classifier achieves an accuracy of 98.3%with reduced classification error rates.

A Personalized Adverse Drug Reaction Early Warning Method Based on Contextual Ontology and Rules Learning

Background: The fatality of adverse drug reactions (ADR) has become one of the major causes of the non-natural disease deaths globally, with the issue of drug safety emerging as a common topic of concern. Objective: The personalized ADR early warning method, based on contextual ontology and rule learning, proposed in this study aims to provide a reference method for personalized health and medical information services. Methods: First, the patient data is formalized, and the user contextual ontology is constructed, reflecting the characteristics of the patient population. The concept of ontology rule learning is then proposed, which is to mine the rules contained in the data set through machine learning to improve the efficiency and scientificity of ontology rule generation. Based on the contextual ontology of ADR, the high-level context information is identified and predicted by means of reasoning, so the occurrence of the specific adverse reaction in patients from different populations is extracted. Results: Finally, using diabetes drugs as an example, contextual information is identified and predicted through reasoning, to mine the occurrence of specific adverse reactions in different patient populations, and realize personalized medication decision-making and early warning of ADR.

Rules-based object-relational databases ontology construction

To solve the problems of shaving and reusing information in the information system, a rules-based ontology constructing approach from object-relational databases is proposed. A 3-tuple ontology constructing model is proposed first. Then, four types of ontology constructing rules including class, property, property characteristics, and property restrictions ave formalized according to the model. Experiment results described in Web ontology language prove that our proposed approach is feasible for applying in the semantic objects project of semantic computing laboratory in UC Irvine. Our approach reduces about twenty percent constructing time compared with the ontology construction from relational databases.

An Ontology Reasoning Architecture for Data Mining Knowledge Management

In order to realize the intelligent management of data mining （DM） domain knowledge, this paper presents an architecture for DM knowledge management based on ontology. Using ontology database, this architecture can realize intelligent knowledge retrieval and automatic accomplishment of DM tasks by means of ontology services. Its key features include：①Describing DM ontology and meta-data using ontology based on Web ontology language （OWL）.② Ontology reasoning function. Based on the existing concepts and relations, the hidden knowledge in ontology can be obtained using the reasoning engine. This paper mainly focuses on the construction of DM ontology and the reasoning of DM ontology based on OWL DL（s）.

Knowledge Representation and Semantic Inference of Process Based on Ontology and Semantic Web Rule Language

The process inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent inference of expert system.The application mode of ontology and semantic technology on the process parameters recommendation are mainly investigated.Firstly,the content about ontology,semantic web rule language(SWRL)rules and the relative inference engine are introduced.Then,the inference method about process based on ontology technology and the SWRL rule is proposed.The construction method of process ontology base and the writing criterion of SWRL rule are described later.Finally,the results of inference are obtained.The mode raised could offer the reference to the construction of process knowledge base as well as the expert system's reusable process rule library.

Capability-based context ontology modeling and reasoning for C^4ISR communication

Modeling and reasoning on domain contexts play a key role for adding intelligence to communication services, and the approach of capability-based requirement engineering ensures the scientificity and accuracy of requirement elicitation. This paper presents a capability-based context ontology modeling approach for command and control, communication, computer, intelligence, surveillance and reconnaissance（C^4ISR） communication. Primarily, a capability-based C^4ISR communication meta-concept model and a C^4ISR communication context meta-ontology are constructed. The context ontology is established under the constraints of the C^4ISR communication context meta-ontology, and furthermore, algorithms are proposed to support context reasoning based on description logic. A case study is presented to demonstrate applicability of the proposed method.

An OWL-Based Mobile GeoBI Context Ontology Enabling Location-Based and Context-Based Reasoning and Supporting Contextual Business Analysis

Mobile professionals need to be assisted with suitable mobile GeoBI (Geospatial Business Intelligence) systems, which are able to capture, organize and structure the user’s reality into a relevant context model and reason on it. GeoBI context modelling and reasoning are still research issues since there is not yet either a model or a relevant taxonomy regarding GeoBI contextual information. To fill this gap, this paper proposes an extended and detailed OWL-based mobile GeoBI context ontology to provide context-aware applications and users with relevant contextual information and context-based reasoning capabilities. Context quality issues are handled an implementation architecture which is provided.

Construction of Domain Web Classification Ontology Based on Suggested Upper Merged Ontology

Aiming at the problem of merging heterogeneous semantic taxonomy emerged in Web information integration, a method of building Web classification ontology （WCO） has been proposed. A WCO that is logically consistent with the suggested upper merged ontology （SUMO） is defined, together with axioms needed to classify Web pages. WCO can be used as a foundation of merging heterogeneous semantic taxonomy, and could be used to support Web information integration and classification based Web information retrieval.

Ontology-Driven Analytic Models for Pension Management and Decision Support System

Ontology-Driven Analytic Models for Pension Management are sophisticated approaches that integrate the principles of ontology and analytics to optimize the management and decision-making processes within pension systems. While Ontology-Driven Analytic Models offer significant benefits for pension management, there are also challenges associated with implementing and utilizing the models. Developing a comprehensive and accurate ontology for pension management requires a deep understanding of the domain, including regulatory frameworks, investment strategies, retirement planning, and integration of data from heterogenous sources. Integrating these data into a cohesive ontology can be challenging. This research work leverages on semantic ontology as an approach for structured representation of knowledge about concepts and their relationships, and applies it to analyze and optimize decision support for pension management. The proposed ontology presents a formal and explicit specification of concepts (classes), their attributes, and the relationships between them and provides a shared and standardized understanding of the domain;enabling precise communication and knowledge representation for decision-support. The ontology deploys computational frameworks and analytic models to assess and evaluate data, generate insights, predict future pension fund performance as well as assess risk exposure. The research adopts the Reasoner, SPARQL query and OWL Visualizer executed over Java IDE for modelling the ontology-driven analytics. The approach encapsulated and integrated semantic ontologies with analytical models to enhance the accuracy, contextuality, and comprehensiveness of analyses and decisions within pension systems.

基于Ontology的信息抽取

为了提高Internet上的信息搜索效率,基于内容的搜索引擎成为迫切的用户需求。但内容信息的手工获取是一项繁重的工作。从文本或半结构化文档中自动地抽取用户关心的内容信息且表示成计算机能理解的形式是一项极具实用价值的挑战性研究。该文从知识表示与推理的角度研究了提高信息抽取智能性的途径,提出了将Ontology与模板规则相结合的技术,并针对线性模板表示的局限提出了基于二侧树结构的模板规则表示,同时实现了此表示下的假设生成———冲突消解推理。该技术成功地应用于招聘广告的信息抽取。

一种新的基于Ontology的信息抽取方法

把语法分析和Ontology结合起来 ,先利用领域Ontology里的概念、关系、关键字自动生成标注规则(Rule) ,然后对文章、句子的语法结构进行分析 ,再利用语法分析的结果和先前生成的标注规则一起对文档进行信息标注与抽取 。

CRL:对语义Web上的Ontology表示语言DAML+OIL的一种扩充方案

Ontologies在语义Web中起着举足轻重的作用,它不仅能为人类用户而且能为软件Agent提供从语法层次到语义层次上的互操作性。DAML+OIL是专门为语义Web设计的一种Ontology描述语言,它将框架系统、描述逻辑和Web标准这三个不同领域的优点结合了起来,采用面向对象的方法用类和属性来描述领域的结构。但是DAML+OIL也有不足之处,其一是约束表达能力较弱,只能表示属性值的基数约束,不能表示属性值的区间约束;其二是不能表示任何规则,而无论是约束表示还是规则表示对Ontology都是非常重要的。该文提出一种对DAML+OIL的扩充方案,使得扩充后的DAML+OIL可以表示简单的规则和属性值的区间约束。

基于ontology的语义搜索引擎研究

在分析搜索引擎的基础上,提出了一种基于ontology的语义搜索引擎系统设想。这个搜索设想不仅能实现关键词的检索,还能让机器理解Web页面的内容、进行语义推理、完成更繁杂细致的搜索任务。将对于提高Web的查全率和查准率有一定的参考意义。

基于Ontology的平面几何知识库设计

文中给出了Ontology建模方法在平面几何知识库设计中的一个应用实例。先从总体上介绍了Ontology建模方法的思想、框架结构及优点。再详细描述了平面几何知识库的设计、组织结构及其实现。我们提出概念分层 ,等价类存贮 ,启发式双向推理 ,推理经验生成 ,交互解题 ,数字测试等多种平面几何知识库的相关技术 ;能生成与“吴法”等代数方法不同的、传统的、较优的可读证明 ;并提高了系统的重用性、可靠性。

利用Ontology和规则表达式的Web信息抽取

借助Ontology，基于成熟的Perl字符处理规则，提出了一种规则表达式方案来满足Web页面信息的抽取、转换和集成。介绍了实现过程。

互联网Ontology语言和推理的比较和分析

本文从描述逻辑和语义互联网两个角度比较和分析几种Web本体描述语言 (RDF RDFS、DAML +OIL和OWL)的表达力和相关的推理 ,介绍它们的不同应用。然后总结出推理的分类 ,说明推理在本体建设、维护和应用中的作用。

基于Ontology的语义信息检索模型研究

在信息检索过程中引入语义,可以大大提高信息检索准确度,其中Ontology扮演了重要的角色。基于Ontology研究了语义信息检索原理、体系结构及其模型实现的关键技术,设计并实现了基于Ontology的信息检索系统原型,对研究进行了验证。实验结果表明,基于Ontology的检索大大提高了信息的查准率和查全率。

Case-based retrieval approach for machining fixture design utilizingontology

In order to develop an intelligent case-based reasoning （CBR） system to reuse fixture de- sign knowledge, ontology technology was integrated in CBR system by semantic annotation of fixture design case. Domain ontology of fixture design was constructed; concepts and relations were de- fined and represented. The 2-level similarity evaluation approach of domain ontology was presented. The concept similarity of cases was calculated as the first grade case retrieval. Numerical measure- ment was the second grade case retrieval, which adopted various methods to calculate different types of attribute values. The problem of similarity measurement of fixture design case was resolved. Pro- totype system based on the proposed method was illustrated and the retrieval approach was proved to be efficient.

一个多文种语言Ontology框架的构建方法

对语言Ontology进行了研究,提出了一个面向多文种信息处理的语言Ontology,给出了其设计思想、定义和推理机制,还描述了其结构和构建方法。该Ontology采用分层的树形结构,收录了具有语义的四类词:名词、动词、副词和形容词,并以词义和文种为类节点、词为叶子节点进行组织。能表示各个文种词汇的语义,并以语义为轴心进行融合,以提供不同文种间词汇的转换关系。另外,还提供了词汇语义相似度计算方法以及推理机制来对语义进行推理。