An ontology typically provides a vocabulary describing a domain of interest and a specification of the meaning of terms in that vocabulary. Depending on the precision of this specification, the notion of ontology encompasses several data or conceptual models, e.g., classifications, database schemas, fully axiomatised theories. Ontologies tend to be everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, social networks. They are, indeed, a practical means to conceptualise what is expressed in a computer format. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it raises heterogeneity problems to a higher level.
For instance, imagine two organisations dealing with books: one is a cultural product electronic commerce site (which sells books, music, movies, etc.) and the other is a university library. The activities of both organisations deal with some related products, the books, but are concerned with different aspects of these: the seller is concerned by the margin, the publisher or the type of binding. The library, in turn, pays more attention to the topic, the size and the year of publication. Both are concerned by the price and the author. Yet they may consider these differently, because the price can include tax and shipping fees or not and being expressed in different currencies or because the authors can be denoted by individual objects or by the character string of their names. Moreover, the seller may organise the books according to their commercial types and the library according to their literary types. In summary, these two organisations will obviously have different and heterogeneous ontologies.
These two institutions may have to interact, for example, because the second one wants to order books to the first one or because the first one wants to digitise the collections of the second one. In order to do so seamlessly, they need to find the correspondences between the entities in their respective ontologies. The correspondences may express that what is called a book in the ontology of the seller stands for what is called a volume in that of the library. Furthermore, the price in the seller ontology should be multiplied by a tax rate for obtaining the corresponding price in the library ontology. The process of finding these correspondences is called "ontology matching".
This book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Ontology entities, in turn, usually denote the named entities of ontologies, such as classes, properties or individuals. However, these entities can also be more complex expressions, such as formulas, concept definitions, queries or term building expressions. Ontology matching results, called alignments, can thus express with various degrees of precision the relations between the ontologies under consideration.
Alignments can be used for various tasks, such as ontology merging, query answering, data translation or for browsing the semantic web. In the above mentioned example, the library can take advantage of alignments for automatically ordering a book and the seller can use them for checking the availability of a reference by the library. Matching ontologies enables the knowledge and data expressed in the matched ontologies to interoperate. It is thus of utmost importance for the above mentioned applications whose interoperability is jeopardised by heterogeneous ontologies.
Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, artificial intelligence. They take advantage of various properties of ontologies, e.g., structures, data instances, semantics, or labels, and use techniques from different fields, e.g., statistics and data analysis, machine learning, automated reasoning, and linguistics. These solutions share some techniques and tackle similar problems, but differ in the way they combine and exploit their results. As a consequence, they are quite difficult to compare and describe, lacking a uniform framework.
Ontology Matching aims at being a reference book that presents currently available work in the topic in a uniform framework. In particular, though we use the word ontology, the work and the techniques considered in this book can equally be applied to database schema matching, catalogue integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a detailed account of matching techniques and matching systems in a systematic way from theoretical, practical and application perspectives. The main emphasis of this book is thus on technical solutions for matching.
We have aimed at a sufficiently comprehensive and documented book so that readers can find and learn about almost any subject related to ontology matching and be referred to further reading. Several topics are not covered in full depth but presented only in some salient details for completeness purpose.
It is not the goal of this book to advocate one approach to ontology matching against the others, but rather to show the variety of approaches and their adequacy in different contexts. We are convinced that there is not one unique approach to ontology matching. We concentrate, however, on automatic solutions for matching. Many applications require submitting matching results to user scrutiny and control before using them, but the better the automated part of the task, the easier the control.
This book provides a comprehensive coverage of ontology matching for the researcher and the practitioner. In particular, it reconsiders the former frameworks and classifications, broadening their scope and accounting for more solutions. It goes as far as describing in detail basic techniques used in matching systems, reviewing available systems, providing a framework for their evaluation and discussing their applications. This unified view of ontology matching techniques and solutions aims at being the starting point to implementing matching solutions dedicated to a particular application context or developing new techniques. So readers should find in this book a starting point for implementing and understanding matching, they should not expect the ultimate matching solution to be unveiled.
Ontology Matching is not meant to be a textbook, though it features exercises for a selected number of chapters. These exercises can help readers in evaluating their understanding of some technical concepts. This book is also complemented by a web site (http://book.ontologymatching.org) which features additional information and resources.
This book is organised in five parts.
Part I is dedicated to the motivation and the definition of the ontology matching problem. The motivation is given in Chapter 1 through various applications that can take advantage of matching ontologies and the presentation of how matching contributes to these applications. In Chapter 2, the ontology matching problem is technically defined in various instances of ontology matching occurring in different contexts, such as folksonomies, classifications, databases, XML and entity–relationship schemas and finally formal ontologies. It justifies the emphasis of this book on ontology matching and provides definitions for the vocabulary used. Finally, it technically defines the ontology expression languages, the ontology matching process and its result: the alignment.
Part II provides a comprehensive coverage of the techniques currently used for ontology matching. It is the main part of the book. Chapter 3 defines a classification of matching approaches which will be used in the subsequent chapters. Chapter 4 presents the basic methods that can be used for assessing the similarity or dissimilarity of ontology entities. These techniques are the basis of most, if not all, current ontology matchers. The composition of an ontology matching system from these basic techniques is considered in Chapter 5, which presents the high-level tools for designing ontology matching systems.
Part III is devoted to packaged matching systems that can be used in applications. Chapter 6 presents a large panel of state of the art matching systems. The reader will find that the basic techniques presented before can lead to a large diversity of systems. Chapter 7 is dedicated to the evaluation of matching solutions. It presents techniques for discriminating empirically among these systems and evaluating their suitability to a particular application.
Part IV is devoted to the use of the ontology matching results in applications once they have been obtained. Chapter 8 considers how alignments can be expressed either for being stored or for being communicated between systems. This chapter also presents frameworks in which alignments can be both obtained and used in various ways. Chapter 9 deals with the explanation of matching results to users. This manipulation is important when matching is not expected to be automatic. Finally, Chapter 10 addresses the ultimate use of ontology matching results through their implementation as an effective procedure, e.g., rules, articulation axioms, mediators that can be used within applications.
Part V concludes the book, summarising the current state of ontology matching and emphasising remaining problems that will have to be addressed by further research.
A graphical representation of this organisation is presented below. The arrows offer different independent reading paths through the book.
This book is intended for researchers and practitioners of information and ontology engineering.
The book outline provides a progressive presentation of the ontology matching field and can be read in its entirety. However, each chapter considers ontology matching under a different perspective and can be read in isolation (though it is advised to read the first part before any other). Those who are only interested in getting acquainted with ontology matching can start by reading Chapters 1, 2 and 11.
For researchers and students dealing with the problem of semantic heterogeneity, we provide not only a comprehensive overview of the state of the art in ontology matching, but also present in detail recent research developments. They show how ontology matching technologies are going to evolve, indicating which research topics are in the academic agenda and which of them represent the scientific challenges. A course on ontology matching should take some motivations from Chapter 1, explain the concepts introduced in Chapter 2, use the classification of Chapter 3 for exposing Chapters 4 and 5 and certainly provide some insights from Chapter 7.
For information technology practitioners, both from industry and academia, who want to implement an ontology matching component, this book will help take advantage of state of the art solutions. These readers will take more profit in Chapters 4, 5, 6, 8, 9 and 10.
For professionals in the areas of e-commerce and knowledge management, the book provides decision support on the use of ontology matching technologies, information about potential problems, and guidelines for the successful application of existing approaches. These readers will take more profit in Chapters 1, 2, 3, 6, 7, 8 and 10.
We only expect from readers a basic knowledge about data and conceptual modelling and graph theory. Knowledge about logics can also be helpful, though not strictly necessary.
The work presented in this book has been partly supported by the Knowledge Web network of excellence (IST-2004-507482) of the European Commission 6th Framework Programme for Research and Technological Development. We emphasise the crucial role played by the European networks of excellence in providing support for cooperative research on important and emerging topics such as this one. This book testifies to the rich working atmosphere these networks contributed to create.
We thank all the participants of the Heterogeneity work package of Knowledge Web and, in particular, Than-Le Bach, Jesus Barrasa, Paolo Bouquet, Jan De Bo, Jos De Bruijn, Rose Dieng-Kuntz, Enrico Franconi, Raúl García Castro, Manfred Hauswirth, Pascal Hitzler, Mustafa Jarrar, Markus Krötzsch, Ruben Lara, Malgorzata Mochol, Amedeo Napoli, Luciano Serafini, François Sharffe, Giorgos Stamou, Heiner Stuckenschmidt, York Sure, Vojtěch Svátek, Valentina Tamma, Sergio Tessaris, Paolo Traverso, Raphaël Troncy, Sven van Acker, Frank van Harmelen, and Ilya Zaihrayeu.
Some people had a particular impact on the book through many fruitful discussions, detailed technical feedback on various ontology matching themes, joint work and continuous support during the time we have been elaborating on it. We are very grateful for this to Marc Ehrig, Fausto Giunchiglia, Loredana Laera, Diana Maynard, Deborah McGuinness, Petko Valchev, Mikalai Yatskevich, and Antoine Zimmermann.
We also thank Amedeo Napoli for his careful reading. We are indebted to Fiona McNeill for the time she kindly spent on a first complete draft of this book and her insightful suggestions.
Finally, we are grateful to our Springer Verlag editor, Ralf Gerstner, for his belief that we had material for such a book and for his kind patience during its production.
Reproduced from J. Euzenat, P. Shvaiko, Ontology matching,
Copyright © Springer-Verlag, 2007.
Reproduction forbidden without authorisation.