Improving Ontology Matching Towards Achieving Semantic Interoperability

 – Improving Ontology Matching Towards Achieving Semantic Interoperability –

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ABSTRACT

This work proposes an algorithm for concept matching, applied in the ontology mapping domain. The basic idea is to seek the effective semantics embedded in the concept name by analyzing the contexts in which it appears.

Through simple interaction with the known lexicon WordNet, the right meaning associated with a concept is unequivocally elicited by exploring their local semantic contexts.

This approach reveals interesting results for the word sense disambiguation, when polysemy problems require a semantic interpretation.

The algorithm, though takes a longer time but yet produce a better matching because the concepts in the ontology trees are populated with much semantic information at the end of the first and second step of the matching process.

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TABLE OF CONTENT

DECLARATION……….. iii

CERTIFICATION…………. iv

DEDICATION…. v

ACKNOWLEDGEMENT…… vi

ABSTRACT…. vii

Table of Contents…… viii

LIST OF TABLES…….. xi

LIST OF FIGURES……………… xii

LIST OF APPENDICES…………… xiii

ABBREVIATIONS, DEFINITIONS, GLOSSARIES AND SYMBOLS……………………. xiv

CHAPTER ONE.…….

GENERAL INTRODUCTION………. 1

• Background of the study…………….. 1
• Research motivations and goals………………… 2

Motivating Example………….. 3

• Research questions………………. 9
• Research objectives…………… 10
• Contribution to Knowledge…………. 11

CHAPTER TWO

LITERATURE REVIEW……………………….. 12

• Designing and classifying …………….. 12
  • Why develop ontologies………………….. 13
  • Steps in developing ontologies……………. 13
  • Problems and solutions to ontology development…………………………. 14
  • Ontologies in the semantic web language……………………………….. 14

Example…….. 15

• Owlvisualizer (Owlviz)……………… 15

• Ontology …………….. 15
• Ontology matching towards semantic interoperability………. 16
  • What is ontology matching?…………………….. 18
  • Why is ontology matching needed/interesting?…………. 19

Language or meta-model level…… 19

Ontology or model level……… 19

• What is semantic interoperability, why is it needed?………… 20

• Review of ontology matching techniques………. 20

2.4.2 Coma++………….. 21

2.4.3 Automatch……….. 21

2.4.4. Duma…………. 22

2.4.5       Scalable knowledge composition….. 22

• Comparisons of ontology matching techniques………. 22
• Limitations of ontology matching techniques…….. 23

CHAPTER THREE

SYSTEM DEVELOPMENT……………… 25

• System Requirement…… 25
  • Protégé Ontology Software ……….. 26
  • S-match 31
• Introduction to graphs………… 32
  • Textual representation of graphs…………. 35
• Pattern in ……………………. 36
  • Pattern specification and …………. 36
  • Pattern visualization and …….. 37

CHAPTER FOUR

SYSTEM IMPLEMENTATION…………… 38

• WordNet Overview…………….. 38
• The semantic matching algorithm……… 39
• The tree matching algorithm: Step I Computing concepts at …. 42
• Dealing with ambiguity using concept sense discrimination algorithm……… 44
• The computation of the C_L matrix…………….. 46
• The computation of the C_N matrix………… 48
• An Architecture of our enhanced S-match implementation…………….. 52
• Conclusion…………….. 54

CHAPTER FIVE

CONCLUSION AND RECOMMENDATION………… 55

• Conclusion and future work………… 55

REFERENCE……….. 56

APPENDIX ONE          58 

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INTRODUCTION

This chapter discusses the introductory part of the thesis which includes the background of the study, research motivations and goals, the research questions for which the thesis should provide answers to, the methodology that is used to answer those questions and finally the summary of the thesis contribution to knowledge.

Background of the study

The world wide web is the greatest repository of information ever assembled by man. It contains documents and multimedia resources concerning almost every imaginable subject, and all of these data are instantaneously available to anyone with an Internet connection.

The web’s success is largely due to its decentralized design: web pages are hosted by numerous computer, where each document can point to other documents, either on the same or different computers.

As a result, individuals all over the world can provide content on the web, allowing it to grow exponentially as more and more people learn how to use it.

However, the web’s size has also become its limitation. Due to the sheer volume of available information, it is becoming increasingly difficult to locate useful information.

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REFERENCES

Bagiwa A.M and Junaidu S.B. (2011). A conceptual framework for adding textual annotations to hierarchical trees representing ontologies as a means of achieving semantic interoperability between different ontologies of the same domain. International journal of electrical, electronics and computer systems (IJEECS), pp. 1-6.
Bilke, A. and Naumann, F. (2005). Schema Matching Using Duplicates. Proceedings of the 21st International Conference on Data Engineering, ICDE. Tokyo, Japan, pp. 69–80.
Do, H. H. Melnik, S. and Rahm, E. (2002). Comparison of schema matching evaluations. Proceedings of the workshop on Web and Databases, pp 44-52.
Do, H.H. and Rahm, E. (2002). COMA – A System for Flexible Combination of Schema Matching Approaches. Proceedings of 28^th International Conference on Very Large Data Bases, August 20-23, 2002, Hong Kong, China. Morgan Kaufmann, 2002, pp. 610–621.
Doan, A. Madhavan, J. Domingos, P. and Halevy, A.Y. (2004) “Ontology Matching: A Machine Learning Approach. Handbook on Ontologies. Springer, 2004, pp. 385–404.