JOURNAL OF COMPUTER SCIENCE AND ENGINEERING, VOLUME 17, ISSUE 2, FEBRUARY 2013

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Resolving Semantic Heterogeneity in Healthcare: An Ontology Matching Approach

Iroju O. Ganiyat, Soriyan, H. Abimbola and Gambo P. Ishaya

Abstract—The major goal of semantic interoperability in the healthcare domain is to ensure that healthcare systems exchange

information with a shared and unambiguous meaning. However, this objective is yet to be realized within the healthcare domain

due to the problem of semantic heterogeneity of information. However, diverse ontologies have been developed to resolve the

problem of semantic heterogeneity in the healthcare domain. Nevertheless, the use of these ontologies has proved abortive

towards the resolution of semantic heterogeneity. Consequently, semantic heterogeneity is a critical problem that is currently

being faced by healthcare organizations since it is often difficult for healthcare systems to interpret clinical terms appropriatel y

during communication process. This paper therefore outlines the causes and major challenges of semantic heterogeneity within

the healthcare domain and also proposes an ontology matching framework that intends to explicitly specify the semantics of

information in the healthcare domain in an unambiguous fashion.

Index Terms— healthcare domain, ontology matching, semantic heterogeneity, semantic interoperability

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1 INTRODUCTION

ne of the major barriers to the delivery of effective healthcare in the healthcare domain is the presence of semantic heterogeneity amongst healthcare

systems. This is because the healthcare system is characterized by heterogeneous terms which may refer to the same concept (e.g. heart and cardiac, heart attack and myocardial infarctions) [1]. This inherent polysemy of healthcare concepts makes data interpretation amongst communicating systems a formidable challenge because of the inability of computers and other ICT related facilities to capture the semantics of information [2].

Semantic heterogeneity in the context of healthcare can therefore be considered as a phenomenon which occurs whenever there is a discrepancy in the meaning and the interpretation of the same or related medical concepts. It is regarded as a major obstacle to the seamless exchange of health-related information as well as the correct interpretation of this information in an unambiguous way. In essence, semantic heterogeneity is the major hindrance to semantic interoperability in healthcare.

In recent times, ontologies have been deployed for resolving semantic heterogeneity by explicitly specifying the semantics of terms in a well defined and unambiguous manner [3], [4], and [5]. Unfortunately, the healthcare domain is composed of diverse ontologies with contradicting or overlapping parts [1]. Thus, the heterogeneous nature of the healthcare ontologies also introduces semantic heterogeneity to this domain [6]. Consequently, the healthcare domain is faced with major challenges such as increasing costs, undesirable error rates, dissatisfied patients and healthcare providers, as well as gross medical errors [7]. However, to alleviate this problem, the heterogeneous ontologies should be matched by finding the correspondences between the

semantically related entities of the ontologies in order to reduce heterogeneity between them [5]. This will ensure that the knowledge and data in the matched ontologies are semantically interoperable.

This paper therefore presents the concepts of interoperability, semantic heterogeneity and its attendant causes in healthcare. The paper also appraises the effects of semantic heterogeneity in healthcare and presents a proposed ontology matching framework for ameliorating the problems of semantic heterogeneity in healthcare.

2 THE CONCEPT OF INTEROPERABILITY

Interoperability, according to Trond and Jochen [8], is the ability of Information and Communication Technology (ICT) systems and of the business processes they support to exchange data and share information and knowledge. The Institute of Electrical and Electronics Engineers standard computer dictionary [9], also viewed interoperability as the ability of two or more systems or components to exchange information and to understand the meaning of the information that have been exchanged. Semantic Health Report [6], however, viewed interoperability in the context of the health system as the ability of Information and Communication Technology (ICT) applications and systems to exchange, understand and act on patients and other health-related information and knowledge, among linguistically and culturally disparate health professionals, patients and other actors and organizations within and across health system jurisdictions in a collaborative manner.

There are different levels of interoperability [10]. These include: 2.1 Level 0 or No Interoperability This is usually characterized by stand-alone systems

which have no interoperability.

O

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2.2 Level 1 or Technical Interoperability

This level of interoperability involves the use of a communication protocol for the exchange of data between systems. Technical interoperability establishes harmonization at the plug and play, signal and protocol level.

2.3 Level 2 or Syntactic interoperability This is the ability of two or more systems to exchange data and services using a common interoperability protocol such as the High Level Architecture (HLA). 2.4 Level 3 or Semantic Interoperability Semantic interoperability, according to the Institute of Electrical and Electronics Engineers Standard Computer Dictionary [6], refers to the ability of two or more systems to automatically interpret the information exchanged meaningfully and accurately in order to produce useful results as defined by the end users of the systems. Semantic interoperability is also used in a more general sense to refer to the ability of two or more systems to exchange information with an unambiguous and shared meaning [11]. In other words, semantic interoperability connotes that the precise meaning of the exchanged information is understood by the communicating systems. Hence, the systems are able to recognize and process semantically equivalent information homogeneously, even if their instances are heterogeneously represented, that is, if they are differently structured, and/or using different terminology or different natural language [6]. Semantic interoperability can thus be said to be distinct from the other levels of interoperability because it ensures that the receiving system understands the meaning of the exchange information, even when the algorithms used by the receiving system are unknown to the sending system.

2.5 Pragmatic Interoperability This level of interoperability is achieved when the interoperating systems are aware of the methods and procedures that each other are employing [10]. In other words, the use of the data or the context of its application

is understood by the participating systems. 2.6 Dynamic Interoperability A system is said to have attained dynamic Interoperability when they are able to comprehend the state changes that occur in the assumptions and constraints that each other is making over time, and are able to take advantage of those changes.

2.7 Conceptual Interoperability Conceptual interoperability is reached if the assumptions and constraints of the meaningful abstraction of reality are aligned.

3 HETEROGENEITY IN HEALTHCARE

The healthcare domain is a complex system that is made up of diverse independent sub-systems such as pharmacy, nursing, dentistry, medicine and radiology. These systems are composed of different specialities, physical locations and core principles [12]. The major goal of these autonomous systems is to manage patient information which is usually exchanged across these various systems. This information may be presented in diverse geographical locations and on diverse e-health systems. These systems are typically standalone systems developed by different people, with different methods and tools and are typically incompatible with one another. The heterogeneity of these systems represents a major problem in transferring data among healthcare systems. The heterogeneity in these systems can either be syntactic, schematic, or semantic. Syntactic heterogeneity is usually caused when different models or languages are used to develop the systems. Schematic heterogeneity typically occurs when different database structures are used in the development of the system while semantic heterogeneity is usually encountered whenever there is an inconsistency or a discrepancy in the meaning and the interpretation of the same or related medical concepts. Basically, semantic heterogeneity generally occurs when the same medical concept is represented using different denotations, thus the concepts are interpreted differently [13]. Semantic heterogeneity can occur as a result of the differences in data-definition constructs, differences in object representations, and system-level differences [14]. Thus, semantic heterogeneity is a complicated term for the phenomenon of disagreement about the meaning or interpretation of the same or related data. 3.1 Types of Semantic Heterogeneity Goh, [15], identified three main types of semantic heterogeneity. These include confounding conflicts, scaling conflict and naming conflict. Confounding conflict occurs when information items which seem to have the same meaning differ in reality, owing to different temporal contexts. Scaling conflict usually occurs when different reference systems are used to measure a value. Naming conflict occurs when the naming schemes of

information differ significantly. A frequent occurrence is the presence of homonyms and synonyms. This heterogeneity makes data interoperability a complex task.

4 CAUSES OF SEMANTIC HETEROGENEITY

IN HEALTHCARE

There are various factors inhibiting the meaningful exchange of healthcare information amongst healthcare systems. These factors include: 4.1 Lack of a Unified Terminological Set in Healthcare The healthcare system lacks a unified terminology set.

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Consequently, healthcare concepts are characterized by different terminologies which consist of multiple representations for the same clinical concept [16]. A typical example of this is the use of synonymous terms such as heart attack, MI, and myocardial infarction. These terms mean the same to a cardiologist but they are different to computers and other ICT related facilities. This is because of their inability to capture the semantics of information. This poses challenge to semantic interoperability in the healthcare domain. 4.2 Heterogeneous Structures There is no standardized structure for medical reports and clinical terminology [16]. For instance, the titles and codes of case notes, diseases, drugs, diagnostic tests, and examination differ in different healthcare organizations. The code for a particular disease in a clinical terminology or case note could refer to another disease in another terminology or case note. However, the basic precondition for the exchange of information in the healthcare domain requires that both the sender and receiver of the data use the same healthcare information standard [17].

4.3 Abbreviated Medical/Clinical Terms in Clinical Texts Clinical texts are generally characterized by numerous abbreviations which are highly ambiguous. For example, ―pe‖, may represent physical examination, pleural effusion, or pulmonary embolism [18]. This phenomenon presents a high degree of semantic heterogeneity to the healthcare domain. Consequently, this leads to gross and intolerable medical errors.

4.4 Exponential Increase in the Number of Incompatible Terminology Systems The exchange of patient records and other health related data as well as their meaningful analysis across diverse electronic health systems requires that the communicating systems have an understanding of the concepts stored in terminology systems such as nomenclatures, vocabularies, thesauri, or ontologies. The notion behind this theory is that, computer systems will comprehend one another perfectly if they deploy the same terminology or mutually compatible ones. However, the number of terminological systems with mutually incompatible definition is growing exponentially day by day [19].

4.5 Standardization Problems Most healthcare systems are proprietary and served a specific department within a healthcare institute at a time. This however makes it difficult to easily share information across diverse systems. However, there are several standards (such as Health Level 7 standards, Open EHR) that are aimed at providing standard interfaces to healthcare systems. These standards are

usually established by consensus and approved by a recognized body to provide rules, guidelines or characteristics foe activities [20]. One of the major goals of these standards is to improve patients’ care by resolving heterogeneity among disparate healthcare systems. However, healthcare institutes do not conform to a single standard. Hence, the seamless exchange of information within the healthcare domain remains a difficulty.

5 CHALLENGES OF SEMANTIC

HETEROGENEITY IN HEALTHCARE

Some of the major challenges of semantic heterogeneity in the healthcare domain are highlighted as follows:

5.1 Clinical Misinterpretation Medical information might be expressed in a way that is suitable for physicians but not necessarily for computation or even implementers of information systems [7]. Hence, the use of diverse terms for the same concept introduces semantic heterogeneity to the implementers thereby facilitating the misinterpretation of these terms.

5.2 High Rate of Error The delivery of safe and effective healthcare is a challenge, particularly as the degree of medical errors is becoming evident. The United States Institute of Medicine reported that 100,000 US citizens die each year through medical errors [21]. This is because the healthcare domain lacks a unified terminology as well as a unified ontology, which leads to semantic heterogeneity and hence the problem of semantic interoperability. Consequently, the healthcare system is characterized by high error rate.

5.3 Increased Cost of Healthcare One of the major challenges that the healthcare industry is facing is increasing costs. For instance, the costs of healthcare in the United States alone was about 14.9 % of the Gross Domestic Product specifically $1.6 trillion in 2002 [23], 1.9 trillion in 2005 [24] and projected to rise to 3.6 trillion by 2014 [23]. Increased cost of healthcare is usually due to the ineffective sharing and communication of data, information, and knowledge among various stakeholders in the healthcare network which is usually due to semantic heterogeneity. 5.4 Difficulty in Data Integration Patients records are usually stored on diverse hospital information systems with heterogeneous databases which usually require integration for effective and meaningful exchange of health related information. The integration of patients’ records, however, usually poses a problem within the healthcare domain. For instance, integrating patients records from two different databases with different formats might result in semantic heterogeneity. For example, one database schema might store the patient hospital number in a field named Hospital Number, but in another database schema, a field containing the same

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data could be called Patient _Number. Hence, the integration of these data in these databases might results in data corruption.

6 ONTOLOGY According to Shvaiko (2006) and Enuoyibofarhe et

al., [5], [24], an ontology provides a vocabulary that describes a domain of interest and a specification of the meaning of terms used in the vocabulary. They are generally used to provide a uniform conceptualization of terms [25]. An ontology according to Zaib [26] is a 5 tuple:

DRIACO ,,,, (1)

such that:

}....................,.........{ 1 kccC (2)

is the set of concepts.

)}(..............),........({ 1 kcAcAA (3)

with

}................,.........{)( 11 ini aacA (4)

being a set of attributes assigned to a concept cl.

}.....,..........,.........{ 1 mrrR (5)

with

ccrp being the set of relations; a relation

connects two concepts with each other, denotes the

natural alphabet in which the name/type of the relation is expressed.

}....................,.........{ 1 kIII (6)

with

}.......,..........,.........{ 1 on iiI (7)

being a set of instances assigned to a concept cn, and D is a set of description logic sentences. In the context of healthcare, ontologies are developed to facilitate the reuse and exchange of medical data [27]. Examples of ontologies in healthcare include disease ontology, the Systemized Nomenclature of Medical-Clinical Terms (SNOMED-CT), Unified Medical Language System (UMLS) and OpenGALEN. The basic advantage of using ontologies in the healthcare system is the ability to resolve semantic heterogeneity. Thus, medical ontologies are developed to enable the reuse and sharing of health related data and to resolve semantic

heterogeneity that are present within the data. In spite of the advantages of ontology in the healthcare domain, the use of diverse ontologies introduces heterogeneity problems to this domain [6]. 6.1 A Brief Description of Ontology Matching

According to Interop [28], ontology matching can be viewed as the process of setting up conjunction between heterogeneous ontologies without changing the original ontology, so that both sides can obtain a common understanding of the same object.

Formally, the ontology matching process according to Zaib [26] is defined as:

),(),(: 21 ccPOMatch (8)

where

Pc

Oc

2

1 (9)

tccsim ),( 21 (10)

where O and P are the ontologies,c1 and c2 are the concepts or entities of the ontologies, sim is the similarity function between the two entities and t is the similarity threshold. Ontology matching, according to Euzenat and Shvaiko [29], can be represented as a function which matches two input ontologies O and O’ by using a previous alignment A, a set of parameters and several other resources such as a knowledge or domain specific thesauri. This process generates an alignment A’ which represents the correspondences between the two input ontologies is produced.

),,,',( RPAOOfA (11)

The result of the ontology matching process is called an alignment. An alignment is defined as a set of correspondences which represent relations between different entities. A correspondence, c, according to Shvaiko, [5], is a 5-tuple:

nreeidc ,,',, (12)

Where id is a unique identifier of the correspondence, e is an entity of ontology O, e’ is an entity of O’, r denotes an alignment relation such as equivalence (=), more general, overlapping and disjointness between the two entities and n gives a confidence value such as a similarity value.

The ontology matching process is classified into three stages namely pre-matching stage, matching stage and post-matching stage [32]. The pre-matching stage involves feature engineering which involves the transformation of two ontologies into a common format suitable for similarity computation. Syntactic normalization is also involved in the feature engineering task. This involves the application of natural language processing techniques such as tokenization, lemmatization and elimination to the ontologies to be matched. Another task in the pre-matching stage is the determination of the next search step. This involves finding a matching candidate in the ontologies to be matched. The most common approach is to compare all entities of the first ontology with all entities of the second

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ontology. After the completion of the pre-matching stage, is the matching stage where the actual similarity computation is carried out to determine the similarity values between matching candidates. The post-matching stage requires the aggregation of different similarity values into a single value for one candidate pair. The final task of the post-matching stage is the interpretation of the similarity value in order to derive the best matching pair(s) among concepts in the first ontology and a set of concepts in the second ontology. These five tasks of ontology matching iterate until no new similarities are found.

7.0 THE PROPOSED FRAMEWORK

The proposed ontology matching framework for resolving semantic heterogeneity in the healthcare domain is depicted in Fig. 1. The goal of this framework is to match medical terms/concepts extracted from heterogeneous medical ontologies such as SNOMED-CT and disease ontology. These terms are disambiguated by looking up the Unified Medical Language System which is a form of medical ontology for the meaning of the concepts.The concepts are then matched in a pair-wise manner. This is achieved by querying the Unified Medical Language System (UMLS) for the meaning of the terms and their set of synonyms. This is done based on the assumption that two terms are similar if they have at least one common word in their sets of synonyms [30]. If there is a common word in the set of synsets, it indicates that the two terms are semantically similar. The semantic similarity of the concepts is computed with the use of the Wu and Palmers algorithm and Jiang Coranth semantic similarity measures. The Wu and Palmer algorithm determines how semantically similar two word senses are in the UMLS, based on the depth of the two senses in the resource and their lower common subsumer.

Fig. 1: Proposed Ontology Matching Framework In addition, the Jiang Conrath semantic similarity measure measures the semantic similarity of the two terms by using the difference in the information content of the two concepts to indicate their similarity [31]. This measure returns a score denoting how similar two word senses are, based on the information content of the lowest common subsumer in the UMLS and that of the input synsets. These semantic similarity measures result in a semantic similarity matrix between the concepts compared. Afterwards, the results of the semantic similarity measures are aggregated using the ordered weighted average of the two semantic similarity measures as depicted in equation 6.3.

x

ii

x

i

ijcwupagg wesimwesimsimsim1

1

.),( (13)

Where wei is the weight assigned to the similarity values simi. The aggregated values are pruned as values that are below the threshold of 0.6 are eliminated. If the ratio is exactly 1.0, it indicates that the terms are equivalent. As a rule of thumb, if the ratio is over 0.6, it means the terms are close matches. Consequently, terms that are within this range can be said to be semantically equivalent.

The proposed ontology matching framework is aimed at solving semantic heterogeneity in healthcare by determining concepts that are characterized by similar terminologies. This will help in reducing medical diagnostic errors.

8.0 Conclusion The use of ontologies within the healthcare domain is the primary means of resolving semantic heterogeneity. However, the diversity of ontologies used within this domain raises semantic heterogeneity to a higher level. However, a successful ontology matching between two systems reconciles the semantic heterogeneity between

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them. This paper therefore appraised semantic heterogeneity, its causes within the healthcare domain and also the challenges of semantic heterogeneity within the healthcare domain. The paper also examines the concepts of ontology and ontology matching, and also presents an ontology matching framework for resolving semantic heterogeneity in healthcare.

REFERENCES [1] O. Iroju, A. Soriyan, and I. Gambo, ―Ontology Matching: An

Ultimate Solution for Semantic Interoperability in Healthcare‖, International Journal of Computer Applications., vol. 51, no.21, pp.7-14, August, 2012.

[2] T. Bittner, M. Donnelly, and S. Winter, ―Ontology and Semantic

Interoperability‖, Institute for Formal Ontology and Medical Information Science (IFOMIS) Saarland University, 2006.

[3] T. Gruber, ―Toward Principles for the Design of Ontologies Used For Knowledge Sharing‖, Stanford University. 1993.

[4] N. Guarino. ―The Role of Ontologies for the Semantic Web (And

Beyond)‖, Technical Report, Laboratory for Applied Ontology, Institute for Cognitive Sciences And Technology (ISTC-CNR), 2004.

[5] P. Shvaiko, ―Iterative Schema-Based Semantic Matching‖, PhD Dissertation. International Doctorate School in Information and

Communication Technology, University Of Trento, 2006.

[6] Semantic Health Report, ―Semantic Interoperability for Better Health and Safer Healthcare‖, European Commission, Information

Society and Media, 2009. [7 ] C. Bock, L. Carnahan, S. Fenves, M .Gruninger, V. Kashyap, B.

Lide, J. Nell, R. Raman and R. Sriram, ―Healthcare Strategic Focus Area: Clinical Informatics‖. National Institute of Standards and

Technology, Technology Administration, Department of Commerce, United States of America, 2005.

[8] A. U. Trond and F. Jochen. ―The Momentum of Open Standards - a Pragmatic Approach to Software Interoperability‖. European

Journal of e-practice, pp. 1-13, 2008. [9] Institute of Electrical and Electronics Engineers Standard

Computer Dictionary, A Compilation of IEEE Standard Computer Glossaries, Institute of Electrical and Electronics Engineers, 1990.

[10] A. Tolk, Y. Saikou., and D. T Charles, ―Support of Integratability, Interoperability, and Composability for System-of-

Systems Engineering‖. Journal of Systemic, Cybernetics and Informatics, vol. 5, no 5, pp. 65-74, 2006.

[11] A.S Gregory, R. B. Kushel, and Y. William, ―Supporting

Interoperability Using the Discrete-Event Modeling Ontology

(Demo). Proceedings of the 2009 Winter Simulation, 2009.

[12] A. Ryan, ―Towards Semantic Interoperability in Healthcare: Ontology Mapping from SNOMED-CT to HL7 version 3‖.

Conferences in Research and Practice in Information Technology (CRPIT), Vol. 72, 2009.

[13] N.Y Ayadi, M. Ahmed, Y. Pollet,‖ Ontology-based meta-model for semantically interoperable system‖, Proceedings of iiWAS2006, pp.

413-422. 2006.

[14] A. Belokosztolszki, ―Semantic Heterogeneity Spells Trouble‖,

http://www/semantic heterogeneity/Semantic Heterogeneity Spells

Trouble.com, 2004.

[15] C.H. Goh, ―Representing and Reasoning about Semantic

Conflicts in Heterogeneous Information Systems‖. Massachusetts Institute of Technology, 1997.

[16] P. Hyeoun-Ae, and H. Nick. ―Clinical Terminologies: A Solution for Semantic Interoperability‖. Journal of Korean Society of Medical

Informatics, vol. 15, no 1, pp 1-11. 2009.

[17] A. N. Peter, ―On Solving the Problem of Semantic Heterogeneity

in Clinical Information Systems‖. Computer Science Department,

University of Texas-Pan American, 2008. [18] C.Friedman, ―Semantic Text Parsing for Patient Records‖

Department of Biomedical Informatics, Columbia University, New York, New York, 2005.

[19] R. Lenz, M. Beyer, and A. Kuhn, ―Semantic Integration in Healthcare Networks‖, Connecting Medical Informatics and Bio-

Informatics, pp. 385-390, 2005. [20] B. Veli, B. L. Gokce, D. Asuman and K. Yildiray,‖ Artemis

Message Exchange Framework: Semantic Interoperability of Exchanged Messages in the Healthcare Domain‖. Software Research

and Development Center, Middle East Technical University (METU), Ankara Turkiye, 2006.

[21] Semantic Health Report. “Semantic Interoperability Deployment and Research Roadmap‖. European Commission, Information Society

and Media, 2007.

[22]National Center for Health Statistics.‖ With Chart book on Trends in the Health of Americans‖.

http://www.cdc.gov/nchs/data/hus/hus04trend.pdf#116 , 2004.

[23]National Healthcare Expenditures Projections, Office of the Actuary. Centers for Medicare and Medicaid Services.USA, 2004.

[24] O.J. Emuoyibofarhe, O.A Lasisi., and J.B Oladosu, ―Toward the Semantic Health Grid‖. Proceeding of AICTRA, Nigeria. pp 14-22,

2007.

[25] A. Costa, S. S Renata, G. Guizzardi, and C. Jos´e,‖COReS:

Context-Aware, Ontology-Based Recommender System for Service

Recommendation‖. Department of Computer Science, UFES,

Vitoria-ES, Brazil, ITC-irst, Trento-Povo, Italy and Laboratory of

Applied Ontologies (ISTC-CNR), Trento, Italy, 2004.

[26] S. Z. Zaib, ―Instance-Based Ontology Matching and the Evaluation of Matching Systems‖. Inaugural-Dissertation.

Department of Computer Science, Heinrich Heine University of Dusseldorf, Germany, 2010.

[27] P, Waraporn, P. Meesad, and G. Clayton, ―Proposed Ontology Based Knowledge and Integration Framework‖, International

Journal of Computer Science and Network Security, vol.10 no.3, pp. 30-36, 2004.

[28] Interop, ―State of the Art Report. Ontology Interoperability‖. Information Society Technologies, 2008.

[29] J. Euzenat, and P. Shvaiko, ―Ontology Matching‖, Springer-

Verlag, Heidelberg, 2007.

[30] A. Islam, and D. Inkpen,‖ Semantic Text Similarity Using

34

Corpus-Based Word Similarity and String Similarity‖, ACM Transaction Knowledge Discovery Data, vol. 2, no 2, pp.1024, 2008.

[31] S. Torres, and S. Gelbukh, ―Comparing Similarity Measures for Original WSD Lesk Algorithm‖, Advances in Computer Science and

Applications, vol. 43, pp. 155-166, 2009. [32] M. Ehrig, and S. Staab, ―QOM - Quick Ontology Mapping‖,

International Semantic Web Conference. Vol. 3298, pp. 683–697 2004.

Iroju O. Ganiyat has a B.Sc. in Computer Technology at Babcock University, Nigeria. She also has M.Sc and PhD in Computer

Science at Obafemi Awolowo University, Nigeria. She is a lecturer at the Department of Computer Science, Adeyemi College of Education, Ondo, Nigeria. Her research interest is on interoperability

and ontology matching. Soriyan H. Abimbola is an Associate Prof of Computer Science at

the Obafemi Awolowo University Ile-Ife, Nigeria. Her research interest is in Information Systems, Health Informatics, and Software Engineering.

Gambo P. Ishaya is a lecturer of Computer Science at the Obafemi Awolowo University Ile-Ife, Nigeria. He is pursuing a PhD degree in Computer Science. He has got a good number of publications in

reputable journals and learned conferences. His research interest is in Information systems design and methodology, software engineering with emphasis on Software Architecture and software

quality issues.