Ontology Development and Query Retrieval using Protégé Tool

Published Online August 2013 in MECS

World 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].

The 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 Web2.1    Importance

  This 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.

• 2.2    Definition

Semantic 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].

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. The

Semantic 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 shown

Table 1: “Comparison between Web and Semantic Web” [16]

Feature	WWW	Semantic Web
Fundamental Component	Unstructured Content	Structured Content
Primary Audience	Humans	Applications
Links	Indicate Location	Indicate Location and Meaning
Primary Vocabulary	Formatting Instructions	Semantics and Logics
Logic	Informal/Nonstandard	Descriptive Logic
Resources	Web Pages, Photos, Videos etc.	Web Pages, Photos, People
Search Engines	Google, Yahoo etc.	Hakia, Sindise etc.
Use of Ontology	Not Applicable	Applicable
Inference	WWW users cannot reach to conclusions.	SW users can reach.

The WWW consists primarily of content for human consumption. Content links to other content on the WWW via the universal Resource Locator (URL). The URL 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 does not 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.

• 2.3    Ontology

The 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]”

Advantages

There 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 required specification 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.

• 2.4    Ontology Languages and Editors

It 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”

Feature	Relational Database	Knowledgebase
Structure	Schema	Ontology Statements
Data	Rows	Instance Statements
Administration Language	DDL	Ontology Statements
Query Language	SQL	SPARQL
Relationships	Foreign Keys	Multidimensional
Logic	External of Database/Triggers	Formal Logic Statements
Uniqueness	Keys of Table	URI

• III.    Case Study

• 3.1    Ontology Development

The 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.

Tool: Protégé is an open-source tool for editing and managing Ontologies. It is the most widely used domain-independent, freely available, platformindependent technology for developing and managing terminologies, Ontologies, and knowledge bases in a broad range of application domains. There are various versions of protégé available out of which the frequently used ones are: protégé 2000, protégé 3.1, protégé 3.4 beta, protégé 3.4(released recently) and protégé 4.0 beta.

Computer Science Department Ontology Computer Science Person

Staff

Teaching (faculty)

Professor

Reader

Lecturer Non-Teaching Assistant Technician Student

Post Graduate

Graduate

Publication Books Journals

It provides a rich set of knowledge modeling structures. 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 Snippets

Following are different various Code snippet of Computer Science Department Ontology, developed in

Protégé 3.1

XML Code Snippet

xmlns:xsi=""

xsi:schemaLocation=" ">

:SYSTEM-CLASS

:STANDARD-CLASS

:ROLE

Abstract

:THING

Staff

:STANDARD-CLASS

:ROLE

Concrete

Person

ID

Sal

Teaching

:STANDARD-CLASS

:ROLE

Concrete

Staff

specialisation

Professor

:STANDARD-CLASS

:ROLE

Concrete

Teaching

Lecturer

:STANDARD-CLASS

:ROLE

Concrete

Teaching

TeachingAssistant

:STANDARD-CLASS

:ROLE

Concrete

Teaching

RDF Code Snippet

]>

xmlns:rdf_="&rdf_;"

xmlns:a="&a;"

xmlns:rdfs="&rdfs;">

rdfs:label="Academic">

OWL Code Snippet

xmlns:swrlb= xmlns:swrl=""

xmlns:protege=" wl/protege#"

xmlns:rdf=""

xmlns:xsd=""

In this paper, we have described the use of Semantic Web 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-Subclass

  

  Fig. 4: ” Ontology on Computer Science Department in Protégé 3.1 (Sub Class)”

  
  

  3.4 Query Retrieval

  

  Fig. 5: “Query retrieval “Staff Salary Greater than 25000”

  
  

Fig. 5, shows the result of query given to the Ontology based system.

• IV.    Conclusion

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 . We have developed ontology on Computer Science and Engineering Department using one of famous ontology editor named as Protégé 3.1. Protégé is an open-source tool for editing and managing Ontologies. It is the most widely used domainindependent, 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.

Acknowledgement

I ,Vishal Jain would like to give my sincere thanks to Prof. M. N. Hoda, Director, Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi for giving me opportunity to do P.hD from Lingaya’s University, Faridabad.