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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 3, ISSUE 11, NOVEMBER 2014 ISSN 2277-8616

307IJSTR2014www.ijstr.org

Mapping Between RDBMS And Ontology: AReview

Vishal Jain, Dr. S. V. A. V. Prasad

Abstract: Today Semantic web is playing a key role in the intelligent retrieval of information. It is the new-generation Web that tries to representinformation such that it can be used by machines not just for display purposes, but for automation, integration, and reuse across applications. It allowsthe representation and exchange of information in a meaningful way. Ontologies form the backbone of the Semantic Web; they allow machineunderstanding of information through the links between the information resources and the terms in the ontologies. Ontology describes basic concepts ina domain and defines relations among them. An ontology together with a set of individual instances of classes constitutes a knowledge base. An efforhas been made by the Semantic Web community to apply its semantic techniques in open, distributed and heterogeneous Web environments, and fosharing the knowledge in the semantic web. For sharing the knowledge ontologies were introduced, and have grown considerably in number. Buildingontology for a specific domain may be start from scratch or by modifying or using an existing ontology. The term Semantic Web (SW) given by TimBerners Lee is considered as vast concept within itself. Semantic Web (SW) is defined as collection of information linked in a way so that it can be easilyprocessed by machines. It is information in machine form. It contains Semantic Web Documents (SWDs) that are written in RD F or OWL languagesThey contain relevant information regarding users query. Crawlers play vital role in accessing information from SWDs..Index Terms: WWW, Semantic Web, Ontology, Ontology Mapping, OWL, RDBMS

1INTRODUCTION

Information Retrieval is the retrieval of information or data,either structured or unstructured. It retrieves in response toquery statement which may be unstructured or structured also.Unstructured Query is like sentence which is written incommon understandable language while structured query is inform of expression which is combination of equations andoperands. IR deals with fusion of streams of output documentsproduced from multiple retrieval methods. They combined toform single ranked stream which is shown to user. There aretwo methods for solving queries:

a) By submitting a given query to multiple documentcollections.

b) By submitting a given query through multiple IR methods.

Traditional text search engines fails for finding optimaldocuments because of following reasons:

Improper style of natural language: - These engines arenot capable of understanding complex way of writingdocuments.

High level unclear concepts: - Some concepts which areincluded in document but present search engines cantfind those words.

Semantic Relations: - We cant find relevant documents

for word specified in part of document. E.g. If we havesearched for Juice, then it will not find type or part ofJuice.

Information Retrieval mainly focuses on retrieval o

unstructured documents (natural text language documents)These documents may include videos, photos and audios etcIR addresses retrieval of documents from an organized weldefined huge collection of documents available on net whichmay be email, maps, news etc. Various goals of IR aredescribed below:

IR aims on retrieving unstructured documents. IR engine may produce collection of relevant documents

to user according to specified query entered by user. IR engine also arranges documents according to its rank

which involves Page Rank algorithm. If a document Ahas more effective results than document B, then A wilorganized first. It has been discussed in further sections.

2SEMANTIC WEBSemantic Web (SW) came into existence due to problem inconventional search engines that dissatisfies users byretrieving inadequate and inconsistent results. The documentsretrieved by conventional search engines are like horse odifferent colors. These engines work on predefined standardterms that work in centralized environment, thus accessingstandard Ontologies. With advent of SW and Ontology, usersare able to develop new facts and use their ownkeywords/terms in different environments. With use oontology, user can perform following tasks:

(a) Users can use Interface Description Languages (IDL

and services for different environments. IDL meansdefining new data objects and their relations.

(b) Users can communicate with different agents usingshared ontology like FOAF (Friend of a Friend).

Semantic Web (SW) is combination of SWDs that areexpressed in ontology languages (RDF, OWL). Ontology refersto categorization of concepts and relationships between termsin hierarchical fashion Although SWDs retrieves relevaninformation because they are characterized by semanticmethods and ideas, but it is tedious job to find URLs ofSWDs.

_______________________________

Vishal Jain, Research Scholar, Computer Science andEngineering Department, Lingayas University,

Faridabad, Haryana, INDIA.

E-mail:[email protected] Dr. S. V. A. V. Prasad, Professor, Electronics and

Communication Department, Lingayas University,

Faridabad, Haryana, INDIA.E-mail:[email protected]
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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3WEBONTOLOGYLANGUAGE(OWL)W3Cs Web Ontology Working Group defined OWL as threedifferent sublanguages:

1. OWL Lite2. OWL DL (includes OWL Lite)3. OWL Full (includes OWL DL)

The W3C-endorsed OWL specification includes the definitionof three variants of OWL, with different levels ofexpressiveness.

OWL Lite was originally intended to support those usersprimarily needing a classification hierarchy and simpleconstraints. For example, while it supports cardinalityconstraints, it only permits cardinality values of 0 or 1. It washoped that it would be simpler to provide tool support for OWLLite than its more expressive relatives, allowing quickmigration path for systems utilizing thesauri and othertaxonomies. In practice, however, most of the expressivenessconstraints placed on OWL Lite amount to little more thansyntactic inconveniences: most of the constructs available in

OWL DL can be built using complex combinations of OWL Litefeatures. Development of OWL Lite tools has thus provenalmost as difficult as development of tools for OWL DL, andOWL Lite is not widely used.

OWL DL was designed to provide the maximumexpressiveness possible while retaining computationalcompleteness (all conclusions are guaranteed to becomputed), decidability (all computations will finish in finitetime), and the availability of practical reasoning algorithms.OWL DL includes all OWL language constructs, but they canbe used only under certain restrictions (for example, numberrestrictions may not be placed upon properties which aredeclared to be transitive). OWL DL is so named due to its

correspondence with description logic, a field of research thathas studied the logics that form the formal foundation of OWL.

OWL Fullis based on a different semantics from OWL Lite orOWL DL, and was designed to preserve some compatibilitywith RDF Schema. For example, in OWL Full a class can betreated simultaneously as a collection of individuals and as anindividual in its own right; this is not permitted in OWL DL.OWL Full allows an ontology to augment the meaning of thepre-defined (RDF or OWL) vocabulary. It is unlikely that anyreasoning software will be able to support complete reasoningfor OWL Full.

Each of these sublanguages is an extension of its simpler

predecessor, both in what can be legally expressed and inwhat can be validly concluded. Usually, Ontologies are definedto consist of abstract concepts and relationships (orproperties) only. In some rare cases, Ontologies are defined toalso include instances of concepts and relationships. Thefollowing three types of Ontologies are common in literatureand are classified on the basis of their generality:

Domain: they are domain-specific and are used captureknowledge in a particular domain, e.g., engineering, medicine,e-commerce, etc.

Generic: they capture general, domain-independent

knowledge (e.g., space and time). They are shared by largenumber of users across distinct domains. Examples areWordNet and CYC.

Application: they capture the knowledge necessary for aparticularapplication, e.g., ontology representing the structureof a particular web site .

In enterprises, Google and Yahoo!, the major web searchservices, are using ontology-based approaches to find andorganize the contents on the Web.


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Table 1`: Current Web and Semantic Web

Table 2: RDBMS Vs Ontology

4ONTOLOGY MAPPINGMichael Wick, Khashayar Rohanimanesh, Andrew McCallum,AnHai Doan [11] presented a fully supervised statistical modelfor ontology mapping based on conditional random fields. Thismodel accounts for uncertainty in both the data and the data'sstructure. Results on two domains and showed that oursupervised model is able to generalize across them has beenevaluated. Yuan An, Alex Borgida and John Mylopoulos [12]discussed about the different mapping methods from databaseto ontologies. Here, author focused on semi-automatic tool,called MAPONTO, that assists users to discover plausiblesemantic relationships between a database schema (relationalor XML) and an ontology, expressing them as logical

formulas/rules. Raji Ghawi and Nadine Cullot [13], focus on acomponent of the architecture which is a tool, called DB2OWL,that automatically generates ontologies from databaseschemas as well as mappings that relate the ontologies to theinformation sources. The mapping process starts by detectingparticular cases for conceptual elements in the database andaccordingly converts database components to thecorresponding ontology components. A prototype of DB2OWLtool is implemented to create OWL ontology from relationaldatabase. Table 3 depicts the comparative study of variousapproaches to convert database management system toontology. Mostafa E. Saleh [15], presented an approach forsemantic query in traditional relational database by

establishing ontological layer. In this paper, author has beendescribed following rules for converting database to ontology:

a) If the primary key of more than one relation is the samethen they should be merged in one ontological classand their attributes should be merged.

b) If the primary key of one relation is unique for thatrelation, and not contain the primary key in anotherrelation, then that relation will be considered as oneontological class.

c) If the foreign key in a relation Ri is a primary key inanother relation Rj, then there is an object property(named by its name in Ri) from Ri to Rj, and the domain

is Ri, and range is Rj.d) If the relation primary key consists of two other primarykeys, then that relation is a property between twoclasses (resources), the classes are the two relationsdenoted by the two primary keys.

Wei Hu and Yuzhong Qu [16], propose a new approach todiscovering simple mappings between a relational databaseschema and ontology. It exploits simple mappings based onvirtual documents, and eliminates incorrect mappings viavalidating mapping consistency. Man Li, Xiao-Yong Du, ShanWang [17], described the learning rules from relationadatabase to OWL ontology. In this paper, an ontology learningapproach has been proposed to construct OWL ontology


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automatically based on data in relational database. Therelated learning rules are discussed in detail. It can be seenthat the approach is practical and helpful to the automation ofontology building. Guntars Bumans [18], demonstrated on asimple yet completely elaborated example how mappinginformation stored in relational tables can be processed usingSQL to generate RDF triples for OWL class and propertyinstances. Noreddine Gherabi, Khaoula Addakiri [19],

prototype has been implemented, which migrates a RDB intoOWL structure, for demonstrate the practical applicability ofapproach by showing how the results of reasoning of thistechnique can help improve the Web systems. Authors havepresented a new approach for mapping relational databaseinto Web ontology. It captures semantic information containedin the structures of RDB, and eliminates incorrect mappings byvalidating mapping consistency. Secondly, we have proposeda new algorithm for constructing contextual mappings,respecting the rules of passage, and integrity constraints.Fuad Mire Hassan, Imran Ghani, Muhammad Faheem,Abdirahman Ali Hajji [20], in this authors has been reviewedand presented a number of articles for Human ResourceOntology in eRecruitment domain. The papers described the

human resource ontology used within ontology matchingapproach, which provides means for semantic matchingapproach to match job seekers and job advertisements in arecruitment domain. Marc Ehrig and Steffen Staab [21],considered QOM, Quick Ontology Mapping, as a way to tradeoff between effectiveness (i.e. quality) and efficiency of themapping generation algorithms and demonstrated that QOMhas lower run-time complexity than existing prominentapproaches. Jess Barrasa, scar Corcho, Asuncin Gmez-Prez [22], in this paper authors has been illustrated R2O, anExtensible and Semantically Based Database to-ontologyMapping Language. Authors presented R2O, an extensibleand declarative language to describe mappings betweenrelational DB schemas and ontologies implemented in RDF(S)

or OWL. R2O provides an extensible set of primitives with welldefined semantics. Michal Laclavk [23], presented theapproach for creating semantic metadata from relationaldatabase data. When building ontology based informationsystems, it is often needed to convert or replicate data fromexisting information systems such as databases to theontology based information systems, if the ontology basedsystems want to work with real data. RDB2Onto convertsselected data from a relational database to a RDF/OWLontology document based on a defined template. CarlosEduardo Pires, Damires Souza, Thiago Pachco, Ana CarolinaSalgado [24], has been presented a tool SemMatcher, formatching ontology-based peer schemas, combining differentmatching strategies (e.g., linguistic, structural, and semantic).

SemMatcher allows the identification of semanticcorrespondences between two peer ontologies using domainontology as background knowledge. Also, the tool determinesa global similarity measure between the matching ontologiesthat can be used for peer clustering. Nikolaos Konstantinou,Dimitrios-Emmanuel Spanos, Michael Chalas, EmmanuelSolidakis and Nikolas Mitrou [25], presented a VisAVis, anapproach to mapping relational database contents toontologies. Authors has shown the key idea, instead of storinginstances along with the ontology terminology, keep themstored in a database and maintain a link to the dataset.

5NEWPROPOSEDFRAMEWORKWHICHCANBEDEVELOPED

In order to overcome the problems of the existing frameworksand tools, it is critical to design an appropriate framework. Thispaper proposes a framework similar to DB2OWL but hasadditional features that address the identified problems anddeficiencies. This implies that the proposed framework cansupport many databases as possible and the most used

programming languages. In addition, the new framework hasthe capacity to output information in different formats, whichnon-programmers can under re-use without the need for anexpert. However, the proposed framework will not depend onparticular table cases. It is a general framework that isapplicable to all tables, whatever the case. The proposedmapping process involves converting tables into classeswhich have several properties, as well as relationships. Theconversion process will start when the user uses a welldesigned user interface to send queries to the database. Theproposed visualization service must be able to present therequired queries in a suitable manner. In order to consider therequirements of different users, including those who do nothave programming skills, the visualization service should have

an interface that has select option for users to key-incommands in a desired language. This should consider all theavailable programming languages as well as human languagewhich diverse users can understand. Therefore, it is extremelycritical to include a module that translates the input text andinstruction into different programming languages. In additionthe new framework ought to incorporate a module that enablesusers to export information into different formats apart from thedefault format. Users must be able to output information in theform of text files, tables and datasets among others.


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Table 3: Features of different database-to-ontology mapping approaches[13]

Table 4: Ontologies based-on newly-built approaches and its associated matching algorithms [19, 20]


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6 COMPARISON OF ALREADY DEVELOPEDTOOLSANDFRAMEWORKS

List ofTools

Features

1. RDBTo Onto

(a) Design and implements ontology based onrelational database(b) User-oriented i.e. user can modify andaccess records.

2. AsioSemanticBridge

(a) Creates ontology and represent rows oftable as classes and columns as theirproperties.(b) Allows updating SPARQL queries to SQLand performs its execution.

3. DataGridSemanticWeb Kit

(a) Performs mapping as well as querying RDFtriples.(b) User defined tool that uses GUI (visualinterface) to define individual classes.(c) Generates SPARQL queries and translatesthem into SQL queries.

4.DB2OWL

(a) Automatically creates ontology byconverting each component of databases intoclasses, properties and relations.(b) Represents developed ontology in OWL-DL(Description Logic) language.(c) Supports only MySql, Oracle databases.

5. SOAP(SimpleobjectAccessprotocol)

(a) Predictive in Nature.(b) Uses classes to predict nature ofontologies.

6. R2O

(a) Uses XML for expressing elements ofdatabase and ontology.(b) Detects ambiguities between classes andtheir properties.

7. Triplify

(a) Represents data that is also present inother databases.(b) Generates SQL queries by linking andconverting requests from various databasesconnected on remote hosts.(c) Does not support SPARQL and is easy touse in various applications.

Table 5: Comparative Study of Tools

7CONCLUSIONThis paper emphasis on the concept of Ontology Mapping,discuss various approaches for converting relational databaseto ontology and vice-versa. It is evident the conversion of

relational databases to ontology is a diverse process and theframeworks and tools used are diverse. These frameworksand tools have their merits and demerits. Data presentationand output formats and languages are crucial concerns. Theproposed frameworks will ensure that there is maximum dataintegrity in after conversion. In addition, it offers users theability to customize queries depending on their literacy level.Automation is also a critical part of the proposed framework.

ACKNOWLEDGMENTI, Vishal Jain, would like to give my thanks to Mr. GagandeepSingh Narula for helping me in comparison of these tools andDr. M. N. Hoda, Director, Bharati Vidyapeeths Institute of

Computer Applications and Management (BVICAM), NewDelhi, for giving me opportunity to do Ph.D from LinagaysUniversity, Faridabad, Haryana.

REFERENCES[1]. Sakthi Murugan R, P. Shanthi Bala and Dr. G. Aghila

Ontology Based Information Retrieval -An AnalysisInternational Journal of Advanced Research in

Computer Science and Software EngineeringVolume 3, Issue 10, October 2013, page no. 486 to493.

[2]. Swathi Rajasurya , Tamizhamudhu Muralidharan Sandhiya Devi and Dr. S. Swamynathan, SemanticInformation Retrieval Using Ontology In UniversityDomain, International Journal of Web & SemanticTechnology (IJWesT), Taiwan, Vol.3, No.4, Octobe2012.

[3]. Zhanjun Li, Victor Raskin and Karthik Ramani, AMethodology Of Engineering Ontology DevelopmenFor Information Retrieval, International Conference

On Engineering Design, ICED07, 28 - 31 Augus2007, Cite Des Sciences Et De L'industrie, ParisFrance.

[4]. Dongpo Deng, Building Ontology of Place Name foSpatial Information Retrievalhttp://www.gisdevelopment.net/technology/gis/ma07259pf.html

[5]. Christopher S.G. Khoo, Jin-Cheon Na, Vivian WeWang, and Syin Chan, Developing an Ontology foEncoding Disease Treatment Information in MedicaAbstracts, DESIDOC Journal of Library & InformationTechnology, Vol. 31, No. 2, March 2011, pp. 103-115.

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[7]. Naveen Malviya, Nishchol Mishra, Santosh SahuDeveloping University Ontology using protg OWLTool: Process and Reasoning, International Journaof Scientific & Engineering Research Volume 2, Issue9, September-2011.

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[10]. Sanjay K. Dwivedi and Anand Kumar, Developmentof University Ontology for a SPOCMS, Journal OfEmerging Technologies In Web Intelligence, Vol. 5,No. 3, August 2013, Page no 213-221.

[11]. Michael Wick, Khashayar Rohanimanesh, AndrewMcCallum, AnHai Doan, A Discriminative Approachto Ontology MappingVLDB 08, August 2430, 2008,

Auckland, New Zealand.

[12]. Yuan An, Alex Borgida and John Mylopoulos,Building Semantic Mappings from Databases toOntologies, American Association for ArtificialIntelligence, Year 2006.

[13]. Raji Ghawi and Nadine Cullot, Database-to-OntologyMapping Generation for Semantic Interoperability,VLDB 07, September 23-28, 2007, Vienna, Austria.

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[15]. Mostafa E. Saleh , Semantic-Based Query inRelational Database Using Ontology, CanadianJournal on Data, Information and KnowledgeEngineering Vol. 2, No. 1, January 2011

[16]. Wei Hu and Yuzhong Qu, Discovering SimpleMappings Between Relational Database Schemasand Ontologies, School of Computer Science andEngineering, Southeast University, Nanjing 210096,P.R. China

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[21]. Marc Ehrig and Steffen Staab, QOM - QuickOntology Mapping, Institute AIFB, University ofKarlsruhe.

[22]. Jess Barrasa, scar Corcho, Asuncin GmezPrez, R2O, an Extensible and Semantically BasedDatabaseto-ontology Mapping Language, OntologyEngineering Group, Departamento de InteligenciaArtificial, Facultad de Informtica, UniversidadPolitcnica de Madrid, Spain

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Data to Ontology Individuals Mapping, Institute oInformatics, Slovak Academy of SciencesDubravska cesta 9, 845 07 Bratislava, Slovakia.

[24]. Carlos Eduardo Pires, Damires Souza, ThiagoPachco, Ana Carolina Salgado, SemMatcher: ATool for Matching Ontology-based Schemas.

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Authors

Vishal Jain has completed his M.Tech(CSE) from USIT, Guru Gobind SinghIndraprastha University, Delhi and doingP.hD from Computer Science andEngineering Department, LingayasUniversity, Faridabad. Presently he isworking as Assistant Professor in Bharat

Vidyapeeths Institute of Computer Applications andManagement, (BVICAM), New Delhi. His research areaincludes Web Technology, Semantic Web and InformationRetrieval. He is also associated with CSI, ISTE.

Dr. S. V. A. V. Prasad has completedM.Tech., Ph.D. Presently working asprofessor and Dean (Academic Affairs)Dean (R&D, IC), Dean School of ElectricaSciences. Sir has actively participated andorganized many refresher coursesseminars, workshops on ISO, ROHSComponent Technology, WEEEOrganizational Methods, Time Study

Productivity enhancement and product feasibility etc. Sir hasdeveloped various products like 15 MHz dual OscilloscopeHigh Voltage Tester, VHF Wattmeter, Standard SignalGenerator with AM/FM Modulator, Wireless Becom, High

power audio Amplifier, Wireless microphone and many morein the span of 25 years (1981 2007). Sir has awarded foexcellence in R&D in year 1999, 2004 and National QualityAward during the Year 1999, 2000, 2004 and 2006.Sir isFellow member of IEEE and life member of ISTE, IETE, andSociety of Audio & Video System. Sir has published more than90 research papers in various National & Internationaconferences and journals. Sir research area includes WirelessCommunication, Satellite Communication & AcousticAntenna, Neural Networks, and Artificial Intelligence.

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