基于Protégé的领域本体构建研究

Protege构建本体笔记Protégé构建本体13种OWL语言OWL可以分为三种子语言：OWL-Lite，OWL-DL，OWL-Full。

子语言的特征是由它的描述能力来分类的。

其中，OWL-Lite描述能力最弱，OWL-Full描述能力最强，OWL-DL 的能力属于中间，同时，OWL-Full可以视为是OWL-DL的一个扩展。

1.1O WL-Lite在语法上，OWL-Lite是最简单的语言。

一般用于只有一个简单的类层次和定义的约束比较简单的情况。

比如，根据一个现有的百科全书建立的本体。

1.2O WL-DLOWL-DL是建立在描述逻辑基础上的的，描述能力比OWL-Lite 强得多。

描述逻辑是第一顺序逻辑的决定性部分，可以进行自动推理。

因此，可以自动的计算分类层次，并且检查本体的一致性。

1.3O WL-FullOWL-Full的表达能力是最强的。

OWL-Full可以适用于需要很强的表达能力的情况。

2OWL本体的组成OWL本体由个体、关联和类组成，三者分别和实例（Instances）、扩展连接点（Slot）、类（Classes）相通信。

2.1个体（Individuals）个体就是在领域中，我们所感兴趣的物体。

Protégé和OWL之间有一个显著的区别，就是OWL没有独立名字假定（Unique Name Assumption, UNA）。

这意味着两个不同的名字可以指向同一个个体。

个体就是我们常说的实例，个体可以被理解为“类的实例”。

2.2关联（Properties）关联指的是两个个体之间的二元关系，比如，一个关联可以把两个个体连接在一起。

例：关联hasSibling，因为Matthew和Gemma是两兄弟，就可以通过hasSibling这个关系把Matthew和Gemma连在了一起，关联也可以只有一个参数，如使某种功能化的关联，如transitive （传递）或symmetric （对称）。

本体构建方法与应用马旭明　王海荣（北方民族大学 计算机科学与工程学院，宁夏 银川　750000）摘　要：自从本体的概念被广泛地引入计算机领域之后，领域专家和相关机构提出了众多本体的构建方法，但每种方法都有各自的适用领域，且不同的领域知识概念具有不同特点，使得构建方法的实用性和通用性大大降低。

笔者在七步法的基础之上结合了高校领域的相关概念实现了一个简单的可推理的领域本体。

最后利用Protégé5.0.0自带的推理机结合SWRL规则对所实现本体进行了测试，测试结果显示，七步法适合高校领域本体的构建，且能够根据已有知识获取新知识。

关键词：本体构建方法；七步法；高校领域；推理机；语义Web规则语言中图分类号：TP18 文献标识码：A 文章编号：1003-9767（2018）05-033-04Ontology Construction Method and ApplicationMa Xuming, Wang Hairong(College of Computer Science and Engineering, North Minzu University, Ningxia Yinchuan 750000, China) Abstract: Since the concept of ontology has been widely introduced into computer field, domain experts and related agencieshave proposed many ontology building methods, but each method has its own applicable field. Because different domain knowledge concepts have different characteristics, making the practicability and universality of the construction method greatly reduced. Based on the seven-step method, this paper combines the relevant concepts of the university field to realize a simple and deductive domain ontology. At last, the ontology is tested by using Protégé5.0.0 inference engine combined with SWRL rules. The test results show that the seven-step method is suitable for the construction of ontology in the university field and can obtain new knowledge based on the existing knowledge.Key words: ontology construction method; seven-step method; university field; inference engine; semantic Web rule language１　概述Web发展已进入了Web3.0的阶段，语义Web是Web3.0的一个重要组成部分，在语义Web发展的过程中面临的一个技术难题是如何让机器和人一样进行“思考”和“推断”，这涉及本体、逻辑和规则等若干方面。

基于Protégé的工程装备维修保障领域本体构建方法
曾拥华;严骏;苏正炼;刘立
【期刊名称】《工兵装备研究》
【年(卷),期】2017(036)004
【摘要】针对工程装备维修保障领域知识点多、面广、关系复杂、共享重用困难等问题,参考领域本体建构方法,首先明确了工程装备维修保障领域本体的专业领域及其范畴,其次分析了工程装备维修保障领域本体的知识来源,然后提取了产品、损伤等九个核心概念,形成了概念层次模型,接着分析了核心概念的数据属性与对象属性,最后运用Protégé软件初步构建了工程装备维修保障领域本体.研究成果为建立相应的工程装备维修保障应用本体及相关知识库奠定了基础.
【总页数】5页(P55-59)
【作者】曾拥华;严骏;苏正炼;刘立
【作者单位】解放军理工大学野战工程学院,江苏南京210007;解放军理工大学野战工程学院,江苏南京210007;解放军理工大学野战工程学院,江苏南京210007;解放军理工大学野战工程学院,江苏南京210007
【正文语种】中文
【中图分类】E92
【相关文献】
1.基于Protégé的装备保障知识本体构建方法
2.基于Protégé的学科本体构建研究
3.基于Protégé的领域本体构建研究
4.基于Protégé的成熟度模型本体构建方法研究
5.基于protégé的中医证候本体构建方法研究
因版权原因，仅展示原文概要，查看原文内容请购买。

webprotege案例案例一：构建领域本体在某个研究机构中，研究人员希望构建一个公共卫生领域的本体。

他们使用WebProtégé来创建本体，并在此基础上建立领域知识图谱。

首先，研究人员定义了一些领域概念，如“疾病”、“症状”、“治疗方法”等，并使用WebProtégé的类编辑器创建了这些类。

然后，他们定义了这些类之间的关系，如“疾病”和“症状”之间的关系是“引发”、“治疗方法”和“疾病”之间的关系是“可用于治疗”等，并使用WebProtégé的关系编辑器创建了这些关系。

随后，研究人员添加了一些实例，如“流感”、“咳嗽”和“抗生素”等，并将它们分类到相应的类中。

他们还为这些实例定义了一些属性，如“流感”具有的症状是“咳嗽”和“发热”，“抗生素”可用于治疗的疾病是“细菌感染”等，并使用WebProtégé的实例编辑器实现了这些定义。

最后，研究人员利用WebProtégé生成了一个OWL本体文件，并利用该文件生成了一个领域知识图谱。

这个知识图谱可以供他们进行进一步的研究、分析和应用。

通过使用WebProtégé，研究人员成功构建了一个公共卫生领域的本体，并基于此建立了一个领域知识图谱，为公共卫生领域的研究和应用提供了有价值的资源。

案例二：领域标注工具在某个文化遗产保护组织中，为了管理和展示文化遗产信息，研究人员使用WebProtégé来构建一个文化遗产本体，并利用它作为领域标注工具。

首先，研究人员定义了一些文化遗产概念，如“古迹”、“文物”、“博物馆”等，并使用WebProtégé的类编辑器创建了这些类。

然后，他们定义了这些类之间的关系，如“古迹”和“文物”之间的关系是“属于”、“博物馆”和“文物”之间的关系是“收藏”等，并使用WebProtégé的关系编辑器创建了这些关系。

基于本体的数据结构课程知识表示研究与实现随着信息技术的飞速发展，人们对于知识的需求越来越高，尤其是在教育领域。

数据结构课程是计算机科学与技术专业中重要的一门课程，对于学生的计算机科学素养和编程能力的提高具有重要的作用。

然而，数据结构课程的知识点繁多，难度大，学生往往难以全面理解和掌握。

如何有效地表示和组织数据结构课程的知识点，是一个亟待解决的问题。

本文提出了一种基于本体的数据结构课程知识表示方法，并对其进行了实现和验证。

本体是一种形式化的知识表示语言，它能够用于描述领域知识的概念、属性、关系等。

在本体的基础上，我们将数据结构课程的知识点进行了建模和表示，形成了一个完整的知识结构。

首先，我们对数据结构课程的知识点进行了分析和分类。

根据知识点的性质和层次，我们将其分为基础概念、线性结构、树形结构、图结构等四个部分。

在每个部分中，我们又将知识点进行了细分和归纳，形成了一个层次化的知识结构。

其次，我们使用OWL（Web Ontology Language）语言对数据结构课程知识进行了建模。

OWL是一种基于本体的知识表示语言，能够描述概念、属性、关系等。

我们将数据结构课程的知识点用OWL 语言进行了建模，形成了一个本体结构。

在本体中，我们定义了课程的概念、知识点的概念、知识点之间的关系、知识点的属性等。

这些概念和关系能够准确地描述数据结构课程的知识结构，帮助学生更好地理解和记忆知识点。

最后，我们使用Protégé软件对本体进行了实现和验证。

Protégé是一种开源的本体编辑器，能够帮助用户创建和编辑本体。

我们将OWL语言表示的本体导入到Protégé软件中，进行了实现和验证。

在实现过程中，我们发现本体的表示能够帮助学生更好地理解数据结构课程的知识点，同时也能够帮助教师更好地组织和教授知识点。

在验证过程中，我们对本体进行了测试和调试，发现其表示能够准确地描述数据结构课程的知识结构，能够满足学生和教师的需求。

Protégé4.1构建考试资源领域本体研究
谢明山;邓艳芳
【期刊名称】《现代工业经济和信息化》
【年(卷),期】2014(004)012
【摘要】文章以全国计算机等级考试的复习资源为研究对象,初步探讨了考试资源领域本体的构建原则,并详细介绍了protégé 4.1环境下考试资源本体构建的具体步骤.
【总页数】3页(P83-84,92)
【作者】谢明山;邓艳芳
【作者单位】海口经济学院,海南海口571127;海南工商职业学院,海南海口570203
【正文语种】中文
【中图分类】TP319
【相关文献】
1.基于本体的数字图书馆检索模型研究(Ⅲ)——历史领域资源本体构建 [J], 董慧;余传明;杨宁;陈亮;徐国虎;张继东;彭翠萍
2.基于Protégé的领域本体构建研究 [J], 朱丹翔;王璐;郝孝倞;潘宽
3.基于Protégé的大学英语教学资源本体构建 [J], 林喆;刘艺
4.应用Protégé构建临床检验诊断学领域本体的探索 [J], 杜志银;刘芳
5.基于Protégé的航空不安全事件的本体知识构建方法实例研究 [J], 周峰;胡雯
因版权原因，仅展示原文概要，查看原文内容请购买。

软件测试信息领域本体构建研究摘要：为了对软件测试领域的信息进行有效管理，对软件测试领域进行了深入分析；引入本体技术并总结了领域本体的构建方法，探索了测试信息领域本体构建方法；为软件测试建立测试信息领域本体，实现信息的有效表示、存储和共享。

关键词：软件测试；领域本体；本体构建；信息共享0引言软件测试是保障软件质量的有效手段，其过程实质上是测试知识共享和重用的过程。

因此，对软件测试信息的有效收集、表示和存储，不仅能为重复测试提供方便，也可以为评估软件质量提供参考。

近年来，作为知识表示工具的本体论（Ontology）由于其具备良好的概念层次结构和逻辑推理能力，使其在信息检索等多个领域得到了广泛应用。

因此，为软件测试信息建立领域本体，可以为信息的表示、存储和共享提供知识管理框架，也可以为软件的复用者提供参考。

1本体和构建方法本体论源于哲学上的概念，广泛认可的定义是Studer等人在前人基础上提出：本体是共享概念模型、明确形式化的规范说明^[1，2]，包含概念模型、明确性、形式化和共享性4个含义^[3]。

本体的建模元语有类（classes 或concepts）、关系（retations）、函数（functions）、公理（axioms）和实例（instance）^[4]。

概念并非单纯意义上的概念，可以是任务、功能、行为、策略、推理过程等。

关系表示概念之间的关联关系，可形式化表示为R：C1×C2×…×Cn表示概念类C1，C2，…，Cn之间存在n元关系R。

函数是一种特殊的关系。

公理用于表示永真式。

实例是某概念类的基本元素，即某概念类所指的具体对象。

为了便于对本体的有效分类，Guarino提出以详细程度、领域依赖程度作为本体划分的基础^[4]。

领域本体的构建方法与应用研究领域本体的构建方法与应用研究摘要：领域本体作为知识表示和知识共享的重要手段，在各个领域的应用中起着重要作用。

本文主要探讨了领域本体的构建方法及其在各个领域的应用研究，并分析了当前存在的问题和未来发展方向。

1. 引言随着互联网时代的到来，知识的多源化、异构化和面向应用的需求越来越明显。

传统的知识表示方式往往面临着信息孤岛问题和语义表达不准确等挑战。

而领域本体作为一种语义表示的机制，可以有效解决这些问题，并为知识的共享和应用提供了基础。

2. 领域本体的构建方法2.1 本体建模本体建模是构建领域本体的重要一环。

在本体建模中，可以采用概念建模、属性建模和关系建模等方法，将领域知识分解为不同的概念、属性和关系，并进行层次化的组织。

同时，还可以通过本体学习和推理等技术，自动从文本中抽取并构建本体。

2.2 本体对齐本体对齐是将不同来源的本体进行匹配和融合的过程。

通过本体对齐，可以实现不同本体之间的语义一致性和知识共享，提高各个领域的信息互通和交流效率。

本体对齐技术可以利用词汇、语义相似度等方法进行匹配，并结合推理和学习等技术进行融合。

3. 领域本体的应用研究3.1 领域本体在智能推荐系统中的应用智能推荐系统通过对用户的需求和偏好进行分析，实现个性化的推荐服务。

领域本体可以将用户的个人信息和商品信息等进行语义表示和关联，提高推荐的准确性和精准度。

3.2 领域本体在医疗领域中的应用医疗领域的知识非常庞大复杂，利用领域本体可以将医疗知识进行表达和表示，帮助医生和病人更好地获取和理解医疗信息。

领域本体可以应用于病历管理、疾病诊断和知识推理等方面，提高医疗服务的质量和效率。

4. 领域本体的问题与挑战4.1 本体构建的语义问题本体构建过程中，由于语义的多样性和歧义性，可能出现语义表达不准确或者不一致的问题。

如何准确地表示领域知识，是一个重要的研究方向。

4.2 本体对齐的可扩展性问题随着本体规模的增大，本体对齐的效率和可扩展性成为一个挑战。

基于Protégé的装备保障知识本体构建方法作者：胡金强冀亚林孟妍杨斌来源：《现代电子技术》2010年第06期摘要:针对装备保障领域知识体系复杂、知识难以共享和重用等问题,在分析装备保障知识本体的需求和作用的基础上,提出一种适合装备保障知识体系的、面向生命周期的装备保障知识本体构建方法,以此为指导对后方军械仓库知识进行分类,利用Protégé软件构建了一个实验性知识本体,为建立后方军械仓库知识库打下了基础。

关键词:装备保障;知识本体;本体构建;后方军械仓库中图分类号:TP182文献标识码:A文章编号:1004-373X(2010)06-207-04Equipment Support Knowledge Ontology Construction Based on ProtégéHU Jinqiang1,JI Yalin1,MENG Yan2,YANG Bin1(1.Research Institute of Ordnance Technology,Ordnance EngineeringCollege,Shijiazhuang,050003,China;2.North China Electric Power University,Baoding,071000,China)Abstract:Equipment support knowledge system is complex and difficult to share and reuse,after analyzing the demands and functions of the equipment support knowledge ontology.A construction method that orients to the lifecycle knowledge ontology is introduced and it is suitable for the equipment support system.Following this method,rear ordnance depot knowledges are sorted and a tested knowledge ontology using Protégé is constructed.Keywords:equipment support;knowledge ontology;ontology construction;rear ordnance depot随着我军装备保障信息化程度的提高,装备保障领域所积累的知识资源在飞速增长。

基于领域本体的知识检索系统研究作者：唐斌谢爱南董坚峰来源：《电脑知识与技术》2012年第31期摘要：为了解决传统信息检索存在的效率低、精度不高以及无法为用户提供个性化服务等问题，提出了将领域本体引入到信息检索的思路，并构建了基于领域本体的知识检索模型。

在探讨基于领域本体的知识检索实现关键技术基础上，以毕业生求职招聘知识检索系统为例进行了知识检索性能的实验论证。

实验结果表明该检索系统能有效的提高信息检索的准确率和效率。

关键词：领域本体；知识检索；本体构建中图分类号：TP311 文献标识码：A 文章编号：1009-3044（2012）31-7423-04随着计算机技术的不断发展以及互联网技术的日新月异，使得万维网上的信息每天都以爆炸式增长，如何快速高效地在海量的数据中获取有价值的信息已成为了当前信息检索研究所面临的重大挑战。

传统的基于关键字的语法匹配和全文检索方式，在早些年以其简单、快捷和容易实现等优点受到用户的亲睐，但随着社会的发展，这种传统的检索方式出现的漏检、误检以及无法为用户提供个性化的检索需求等问题逐渐显现出来。

通过对传统检索系统的研究，总结出传统检索方式存在以下几个较突出的问题：第一，忠实表达问题。

大多数检索系统主要是借助于目录、索引和关键字等方法来实现，结构单调统一，很多情况下，用户很难通过简单的几个关键词就能够真正表达出他所需要检索的内容，因此表达上的困难导致检索质量的降低。

第二，一词多义问题。

不同的检索用户有着不同的检索目的，当以同一组关键词进行检索时会得到同样的检索结果，无法实现用户的特殊检索需求。

第三，同义词问题。

基于关键字匹配的检索技术，是严格按照用户提交的查询请求在全文中进行关键字匹配的检索方式，没有理解和处理信息的能力，因此许多与关键词的同义词信息就无法检索出来。

第四，词汇孤岛问题。

在人的大脑中，概念之间存在着各种各样的联系，而在基于关键字的检索系统中，这种概念之间的语义联系很难进行描述。

I.J. Intelligent Systems and Applications, 2013, 09, 67-75Published Online August 2013 in MECS (/)DOI: 10.5815/ijisa.2013.09.08Ontology Development and Query Retrievalusing Protégé ToolVishal JainResearch Scholar, Computer Science and Engineering Department, Lingaya’s University, Faridabad, IndiaE-mail: vishaljain83@Dr. Mayank SinghAssociate Professor, Krishna Engineering College, Ghaziabad, IndiaE-mail: mayanksingh2005@Abstract—This paper highlights the explicit description about concept of ontology which is concerned with the development and methodology involved in building ontology. The concept of ontologies has contributed to the development of Semantic Web where Semantic Web is an extension of the current World Wide Web in which information is given in a well-defined meaning that translates the given unstructured data into knowledgeable representation data thus enabling computers and people to work in cooperation. Thus, we can say that Semantic Web is information in machine understandable form. It is also called as Global Information Mesh (GIM). Semantic Web technology can be used to deal with challenges including traditional search engines and retrieval techniques within given organizations or for e-commerce applications whose initial focus is on professional users. Ontology represents information in a manner so that this information can also be used by machines not only for displaying, but also for automating, integrating, and reusing the same information across various applications which may include Artificial Intelligence, Information Retrieval (IR) and many more. Ontology is defined as a collection of set of concepts, their definitions and the relationships among them represented in a hierarchical manner that is termed as Taxonomy. There are various tools available for developing ontologies like Hozo, DOML, and AltovaSemantic Works etc. We have used protégéwhich is one of the most widely used ontology development editor that defines ontology concepts (classes), properties, taxonomies, various restrictions and class instances. It also supports several ontology representation languages, including OWL. There are various versions of protégéavailable like WebProtege 2.0 beta, Protégé3.4.8, Protégé4.1 etc. In this paper, we have illustrated ontology development using protégé3.1 by giving an example of Computer Science Department of University System. It may be useful for future researchers in making ontology on protégéversion 3.1. Index Terms— Semantic Web, Ontology Development, OWL, Protégé 3.1I.IntroductionWorld Wide Web is the largest database in the Universe which is mostly understandable by human users and not by machines. WWW is human focused web. It discovers documents for the people. It lacks the existence of a semantic structure which maintains interdependency and scalability of its components. It returns results of given query with the help of hyperlinks between resources. It produces large number of results that may or may not satisfy user’s query. It results in the presentation of irrelevant information to the user. In the current web, resources are accessible through hyperlinks to web content spread throughout the world. The content of information is machine readable but not machine understandable. Use of current www does not support the concept of ontologies and users cannot make inferences due to unavailability of complete data. An enormous collection of unstructured data present on web leads to problems in extracting information about a particular domain. Hence information extraction is a logical step to retrieve relevant data and the extracted information. The word Information Retrieval is explicitly defined as process of extracting relevant results in context of given query. It is described as the task of identifying documents on the basis of properties assigned to the documents by various users requesting for retrieval. There are many Information Retrieval techniques for extracting keywords like NLP based extraction techniques. Content-based image retrieval system requires users to adopt new and challenges search strategies based on the visual pictures of images [1]. Multimedia information retrieval provides retrieval capabilities of text images and different dimensions like form, content and structure. When text annotation is nonexistent and incomplete content-based method must be used. Retrieval accuracy can be improved by content-based methods [2].68Ontology Development and Query Retrieval using Protégé ToolThe remaining sections of paper are as follows. Section 2 makes readers aware of Semantic Web including its architecture and its importance as future web technology. In this section, we have also discussed about Ontology and its components. A list of differences is shown on Relational Database and Ontology. Section 3 defines development of ontology on “Computer Science Department” using Protégé tool via Case Study.II.Semantic Web2.1ImportanceThis futuristic concept of Semantic Web is needed to make our present web more precise and effective by increasing the structure and size of current web. Semantic Web (SW) uses Semantic Web documents (SWD’s) that must be combined with Web based Indexing. The idea of Semantic Web (SW) as envisioned by Tim Bermers Lee came into existence in 1996 with the aim to translate given information into machine understandable form.2.2DefinitionSemantic Web is the new-generation Web that tries to represent information such that it can be used by machines not just for display purposes, but for automation, integration, and reuse across applications [3]. The emerging Semantic Web technology has revolutionized the way we use the Web to find and organize information. It is defined as framework of expressing information because we can develop various languages and approaches for increasing IR effectiveness. Semantic Web (SW) uses Semantic Web documents (SWD’s) that are written in SW languages like OWL, DAML+OIL. We can say that Semantic Web documents are means of information exchange in Semantic Web (SW).The Semantic Web (SW) is an extension of current www in which documents are filled by annotations in machine understandable markup language. Semantic Web technology can be used first to address efficiency, productivity and scalability challenges within Enterprises or for e-commerce applications and the initial focus is on professional users [4].Tim Berner Lee (Inventor of Web, HTTP, & HTML) says that Semantic web will be the next generation of Current Web and the next IT revolution [6, 7, and 8]. It is treated as future concept or technology. In the Fig. 1, at the bottom of the architecture we find XML, a language that lets enables us to write structured documents according to predefined guidelines or syntax. XML is particularly suitable for sending documents across the Web [9]. RDF is a basic data model for writing simple statements about Web objects (resources). RDF Model has three components: Resource, Property and Statement. Both XML and RDF follow same syntax in writing properties. Therefore, it is located on top of the XML layer [10]. RDF Schema (rdfs)provides modeling primitives for organizing Web objects into hierarchies. Its key primitives are classes and properties, subclass and sub property relationships, and domain and range restrictions [11]. RDF Schema is based on RDF. RDF Schema is RDF vocabulary description language. It represents relationship between groups of resources. The Logic layer is used in development of ontology and producing a knowledgeable representation document written in either XML or RDF. The Proof layer involves the actual deductive process as well as the representation of proofs in Web languages (from lower levels) and proof validation [12]. Finally, the Trust layer will emerge through the use of digital signatures and other kinds of knowledge, based on recommendations. The Semantic Web is envisioned as a collection of information linked in a way that can be easily processed by machine. This whole vision depends on agreeing upon common standards - something that is used and extended everywhere [13, 14].Fig. 1: “Semantic Web layered Architecture [5]”Berners-lee outlined the architecture of the Semantic Web in the following 3 layers [15]:The metadata layer:It contains the concepts of resource and properties and RDF (Resource Description Framework), most popular data model for the metadata layer.The schema layer: Web ontology languages (OWL) are introduced here to define a hierarchical description of concepts (is-a hierarchy) and properties and RDFS (RDF Schema) is a popular schema layer language. The logical layer: Set of web ontology languages are introduced at this layer to provide a richer set of modeling primitives in which Semantic Web plays a very important role to replace slow, ineffective, inefficient, & non intelligent web processes by fast, effective and inexpensive automatic processes. We can make our web more precise and increase retrieval capacity by adding annotations to documents. TheSemantic Web will allow both humans and machines to find and make use of data in modern ways that previously haven't been possible by www.Both Semantic Web (SW) and World Wide Web (www) are different from each other in various aspects which are described in the form of table as shownTable 1: “Comparison between Web and Semantic Web” [16]The WWW consists primarily of content for humanconsumption. Content links to other content on theWWW via the universal Resource Locator (URL). TheURL relies on surrounding context (if any) to communicate the purpose of the link that it represents; usually the user infers the semantics. Web content typically contains formatting instructions for a nice presentation, again for human consumption [17]. WWW content doesnot have any formal logical constructs. Correspondingly, the Semantic Web consists primarily of statements for application consumption. The statements link together via constructs that can form semantics, the meaning of the link. Thus, link semantics provide a defined meaningful path rather than a user-interpreted one. The statements may also contain logic that allows further interpretation and inference of the statements.2.3OntologyThe term ontology can be defined in many different ways. Genesereth and Nilsson defined Ontology as an explicit specification of a set of objects, concepts, and other entities that are presumed to exist in some area of interest and the relationships that hold them. It enables the Web for software components can be ideally supported through the use of Semantic Web technologies [18]. This helps in understanding the concepts of the domain as well as helps the machine to interpret the definitions of concepts in the domains and also the relations between them. Ontologies can be broadly divided into two main types: lightweight and heavyweight. Lightweight Ontologies involve taxonomy (or class hierarchy) that contains classes, subclasses, attributes and values. Heavy weight Ontologies model domains in a deeper way and include axioms and constraints [19]. Ontology layer consists of hierarchical distribution of important concepts in the domain and describing about the Ontology concepts, relationships and constraints. Fig. 2 displays the Ontology and its Constituents parts.Fig. 2: “Ontology and its components [20]”AdvantagesThere are many advantages of using ontology in the Semantic Web technology. Some of them are as follows [21, 22]:∙Sharing common understanding of the structure of information among people or software agents is one of the more common goals in developing Ontologies [23].∙Ontology enables reusability of domain knowledge in representing concepts and their relationships.∙Making explicit domain assumptions underlying an implementation makes it possible to change these assumptions easily if our knowledge about the domain changes [24].∙Separating the domain knowledge from the operational knowledge is another common use of ontologies. We can describe a task of configuring a product from its components according to a requiredspecification and implement a program that does this configuration independent of the products and components themselves [25].∙Use of ontology enables to analyze domain knowledge on basis of declared terms in a document. ∙Each user has its defined attributes and relationships between other users.∙Ontology is considered as backbone of Software. Since SW translates the given data into machine understandable language using concept of ontologies [26].∙Ontology development is a cooperative process; it allows different peoples to express their views on given domain.∙Ontology language editors helps to build SW.2.4Ontology Languages and EditorsIt is defined as formal language used to encode ontology. Various languages are listed below:∙DAML+OIL: - DAML stands for DARPA Agent Markup Language. DARPA stands for Defense Advanced Research project Agency. OIL stands for Ontology Interchange Language. This language uses Description Logic (DL) to express this language. ∙SWRL: - It stands for Semantic Web Rule Language. It adds rules to OWL+DL.∙OWL: - It stands for Web Ontology Language. It is used to represent relations between entities by using formal semantics and vocabulary.Ontology Editors: - They are applications designed to assist modifications of ontology. Various editors are listed below:∙Protégé: - It is free, open source and knowledge requisition system. It is written in Java and uses Swings to create the complex user interface.∙DOME: - It stands for DERI Ontology Management Environment. It is designed to create effective management of ontologies.∙Onto Lingua: - It is an ontology developed by OnTO Knowledge Project. It implements Ontology construction process.∙Altova SemanticWorks: - It is an RDF document editor and ontology development IDE. It creates and edits RDF documents, RDF Schema and OWL ontologies.Table 2: “Comparison between RDBMS and Ontology”III.Case StudyThe Computer Science Department Ontology describes various terms used in a computer science department. It shows the terms and their inheritance but not the relationships. For example, A Professor inherits from a Teaching which inherits from the Staff which is a generalization of a Person. Similarly Assistant inherits from Non Teaching which in turn inherits from Staff which in turn Person. The Screen Shot of Computer Science Department is shown in Fig. 3.3.1Ontology DevelopmentTool: Protégé is an open-source tool for editing and managing Ontologies. It is the most widely used domain-independent, freely available, platform-independent technology for developing and managing terminologies, Ontologies, and knowledge bases in a broad range of application domains. There are various versions of protégéavailable out of which the frequently used ones are: protégé2000, protégé3.1, protégé3.4 beta, protégé3.4(released recently) and protégé 4.0 beta.Computer Science Department OntologyComputer SciencePersonStaffTeaching (faculty)ProfessorReaderLecturerNon-TeachingAssistantTechnicianStudentPost GraduateGraduatePublicationBooksJournalsIt provides a rich set of knowledge modelingstructures. We have used the protégé version 3.1 to develop my Ontology on Computer Science Department. It provides the facility to support for multi user system, class trees on different tabs are synchronized by default, standard max memory allocation is 100 MB, RDF backend validates frame names, improved handling of sub slots and database backend correctly identifies MSSQL server and optimizes table creation accordingly.Fig. 3:“Computer Science Department Ontology”Fig.3, shows the Ontology on “Computer Science Department with the help of Protégé tool.3.2 Code SnippetsFollowing are different various Code snippet of Computer Science Department Ontology, developed in Protégé 3.1XML Code Snippet <knowledge_basexmlns="/xml" xmlns:xsi="/2001/XMLSchema-instance"xsi:schemaLocation="/xml /xml/schema/protege.xsd"><class><name>:SYSTEM-CLASS</name> <type>:STANDARD-CLASS</type> <own_slot_value><slot_reference>:ROLE</slot_reference> <value value_type="string">Abstract</value> </own_slot_value><superclass>:THING</superclass> </class> <class><name>Staff</name><type>:STANDARD-CLASS</type> <own_slot_value><slot_reference>:ROLE</slot_reference><value value_type="string">Concrete</value> </own_slot_value><superclass>Person</superclass><template_slot>ID</template_slot><template_slot>Sal</template_slot></class><class><name>Teaching</name><type>:STANDARD-CLASS</type><own_slot_value><slot_reference>:ROLE</slot_reference><value value_type="string">Concrete</value> </own_slot_value><superclass>Staff</superclass><template_slot>specialisation</template_slot> </class><class><name>Professor</name><type>:STANDARD-CLASS</type><own_slot_value><slot_reference>:ROLE</slot_reference><value value_type="string">Concrete</value> </own_slot_value><superclass>Teaching</superclass></class><class><name>Lecturer</name><type>:STANDARD-CLASS</type><own_slot_value><slot_reference>:ROLE</slot_reference><value value_type="string">Concrete</value> </own_slot_value><superclass>Teaching</superclass></class><class><name>TeachingAssistant</name><type>:STANDARD-CLASS</type><own_slot_value><slot_reference>:ROLE</slot_reference><value value_type="string">Concrete</value></own_slot_value><superclass>Teaching</superclass></class></knowledge_base>RDF Code Snippet<?xml version='1.0' encoding='UTF-8'?><!DOCTYPE rdf:RDF [<!ENTITY rdf '/1999/02/22-rdf-syntax-ns#'><!ENTITY a '/system#'><!ENTITY rdf_ '/rdf'><!ENTITY rdfs '/2000/01/rdf-schema#'> ]><rdf:RDF xmlns:rdf="&rdf;"xmlns:rdf_="&rdf_;"xmlns:a="&a;"xmlns:rdfs="&rdfs;"><rdfs:Class rdf:about="&rdf_;Academic"rdfs:label="Academic"><rdfs:subClassOfrdf:resource="&rdf_;Nonteaching"/></rdfs:Class>OWL Code Snippet<?xml version="1.0"?><rdf:RDFxmlns:xsp="http://www.owl-/2005/08/07/xsp.owl#"xmlns:swrlb=/2003/11/swrlb# xmlns:swrl="/2003/11/swrl#"xmlns:protege="/plugins/o wl/protege#"xmlns:rdf="/1999/02/22-rdf-syntax-ns#"xmlns:xsd="/2001/XMLSchema#"<owl:Ontology rdf:about=""/><owl:Class rdf:ID="UndergraduateStudent"><rdfs:subClassOf><owl:Class rdf:ID="Student"/></rdfs:subClassOf>In this paper, we have described the use of SemanticWeb in Information Retrieval with the help of Ontology. Information Retrieval over collection of those documents offers new challenges and opportunities. The paper shows that Semantic Web (SW) is better than current World Wide Web (www) by defining various differences between them. It gives brief overview on Ontology and its role in Semantic Web (SW).3.3 Class-SubclassFig. 4: ” Ontology on Computer Science Department in Protégé 3.1 (Sub Class)”3.4Query RetrievalFig. 5: “Query retrieval “Staff Salary Greater than 25000”Fig. 5, shows the result of query given to the Ontology based system.IV.ConclusionOntology represents information in a manner so that this information can also be used by machines not only for displaying, but also for automating, integrating, and reusing the same information across various applications. We have developed ontology on Computer Science and Engineering Department using one of famous ontology editor named as Protégé3.1. Protégéis an open-source tool for editing and managing Ontologies. It is the most widely used domain-independent, freely available, platform-independent technology for developing and managing ontologies. This paper will help upcoming researchers to develop an ontology using the protégé 3.1 in the semantic web. This ontology can also be used by any university system to make relevant search on the web. The developed ontology can be extended further to improve the performance of the Internet Technology. AcknowledgementI ,Vishal Jain would like to give my sincere thanks to Prof. M. N. Hoda, Director, Bharati V idyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi for giving me opportunity to do P.hD from Lingaya’s University, Faridabad. 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IEEE Intelligent Systems, Jan/Feb 1999.[24]Time Berners-Lee, The Semantic Web Revisited,IEEE Intelligent Systems, 2006[25]Lina Tankelevičienė, Ontology and OntologyEngineering: Analysis of Concepts, Classifications and Potential Use in E-Learning Context, Technical Report MII-SED-08-01, February 2008.[26]Daniel L. Rubin, Natalya F. Noy and Mark A.Musen, “Protégé: A Tool for Managing and Using Terminology in Radiology Applications”, Journal of Digital Imaging. 2007 Nov; 20(Suppl 1)34-46 Authors’ ProfilesVishal Jain has completed hisM.Tech (CSE) from USIT, GuruGobind Singh IndraprasthaUniversity, Delhi and doing PhDfrom Computer Science andEngineering Department,Lingaya’s University, Faridabad. Presently he is working as Assistant Professor in Bharati Vidyapeeth’s Institute of Computer Applications and Management, (BVICAM), New Delhi. His research area includes Web Technology, Semantic Web and Information Retrieval. He is also associated with CSI, ISTE.Dr. Mayank Singh has completedhis M. E in software engineeringfrom Thapar University and PhDfrom Uttarakhand TechnicalUniversity. His Research areaincludes Software Engineering,Software Testing, Wireless SensorNetworks and Data Mining. Presently He is working as Associate Professor in Krishna Engineering College, Ghaziabad. He is associated with CSI, IE (I), IEEE Computer Society India and ACM.。