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2013 | Buch

Applications Kapitel lesen Erstes Kapitel lesen

Ontology Matching

verfasst von: Jérôme Euzenat, Pavel Shvaiko

Verlag: Springer Berlin Heidelberg

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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SUCHEN

Über dieses Buch

Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. 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 just raises heterogeneity problems to a higher level.

Euzenat and Shvaiko’s 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. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence.

The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed.

With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog 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 systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Inhaltsverzeichnis

Frontmatter

The Matching Problem

Frontmatter
Chapter 1. Applications
Abstract
This chapter overviews the major ontology matching applications. These applications are presented from a technological point of view, such as ontology integration or linked data, rather than from an economic sector or a domain of interest point of view, such as biomedicine or electronic government. Ontology matching is an enabling operation spreading all domains of interest (running in heterogeneous settings).
Jérôme Euzenat, Pavel Shvaiko
Chapter 2. The Matching Problem
Abstract
In a distributed and open system, such as the semantic web and many of the applications presented in the previous chapter, heterogeneity cannot be avoided. Different actors have different interests and habits, use different tools and knowledge, and most often, at different levels of detail. These various reasons for heterogeneity lead to diverse forms of heterogeneity, and, therefore, should be carefully taken into consideration.
Jérôme Euzenat, Pavel Shvaiko
Chapter 3. Methodology
Abstract
Ontology matching is an important operation in modern ontology engineering because of the heterogeneous environments in which ontologies are designed, developed and supposed to work. Methodologically, it is worthwhile to express relations between ontologies since this allows for: (i) working with small and self-sufficient modules, instead of monolithic ontologies, (ii) expressing the links between two versions of the same ontology, and thus, updating data from one ontology to another, or (iii) putting back an ontology in the context of an upper-level ontology, allowing it to become more consensual with other ontologies of that domain.
Jérôme Euzenat, Pavel Shvaiko

Ontology Matching Techniques

Frontmatter
Chapter 4. Classifications of Ontology Matching Techniques
Abstract
Having defined what the matching problem and the process for solving it are, and before scrutinising further the details of matching techniques, we classify them from different standpoints. This should help better understanding these systems.
Jérôme Euzenat, Pavel Shvaiko
Chapter 5. Basic Similarity Measures
Abstract
The goal of ontology matching is to find relations between entities expressed in different ontologies. Very often, these relations are equivalence relations that are discovered through the measure of similarity between these entities. However, more elaborate methods may directly find more precise relations.
Jérôme Euzenat, Pavel Shvaiko
Chapter 6. Global Matching Methods
Abstract
The basic similarities presented in Chap. 5 can be considered local because, in order to assess the similarity or dissimilarity between two entities, they only consider their proper characteristics (name, internal structure and extension). We consider here global methods, which consider the characteristics holding between the various entities in order to compare them.
Jérôme Euzenat, Pavel Shvaiko
Chapter 7. Matching Strategies
Abstract
The basic techniques presented in Chap. 5 and the global techniques provided in Chap. 6 are the building blocks on which a matching system is built. Once the similarity or dissimilarity between ontology entities is available, the alignment remains to be computed. This involves more comprehensive treatments. In particular, the following aspects of building a working matching system are considered in this chapter:
  • preparing, if necessary, to handle large scale ontologies (Sect. 7.1.1),
  • organising the combination of various similarities or matching algorithms (Sect. 7.2),
  • exploiting background knowledge sources (Sect. 7.3),
  • aggregating the results of the basic methods in order to compute the compound similarity between entities (Sect. 7.4),
  • learning matchers from data (Sect. 7.5) and tuning them (Sect. 7.6),
  • extracting alignments from the resulting (dis)similarity: indeed, different alignments with different characteristics may be extracted from the same (dis)similarity (Sect. 7.7),
  • improving alignments through disambiguation, debugging and repair (Sect. 7.8).
Jérôme Euzenat, Pavel Shvaiko

Systems and Evaluation

Frontmatter
Chapter 8. Overview of Matching Systems
Abstract
This chapter is an overview of matchers which have emerged during the last decades. There have already been some comparisons of matching systems, in particular in (Parent and Spaccapietra 2000; Rahm and Bernstein 2001; Do et al. 2002; Kalfoglou and Schorlemmer 2003b; Noy 2004a; Doan and Halevy 2005; Shvaiko and Euzenat 2005; Choi et al. 2006; Bellahsene et al. 2011). Our purpose here is not to compare them in full detail, though we give some comparisons, but rather to show their variety, in order to demonstrate in how many different ways the methods presented in the previous chapters have been practically exploited.
Jérôme Euzenat, Pavel Shvaiko
Chapter 9. Evaluation of Matching Systems
Abstract
The increasing number of methods available for ontology matching commands the evaluation of these methods.
Matching systems are difficult to compare, but the ontology matching field can only evolve if evaluation criteria are provided. These should help system designers assess the strengths and weaknesses of their systems as well as help application developers choose the most appropriate algorithm.
Jérôme Euzenat, Pavel Shvaiko

Representing, Explaining, and Processing Alignments

Frontmatter
Chapter 10. Frameworks and Formats: Representing Alignments
Abstract
Once matching has been performed, the resulting alignments are usually used in a wider context than a matching system itself. Several proposals have been made for representing the alignments and exchanging them among tools. This chapter presents some frameworks and formats for doing so. In particular, we address the following aspects:
Jérôme Euzenat, Pavel Shvaiko
Chapter 11. User Involvement
Abstract
This chapter considers how ontology matching techniques and human users interact from a technical perspective (rather than from organisational or social ones (von Hippel 2005)). This may occur because some functions of matching, such as finding anchors, are partially or completely carried out by individual users (Sect. 11.1) or sets of users (Sect. 11.2). The ability to explain alignments to users is also an important factor in the success of user involvement (Sect. 11.3). Finally, special attention is paid to tools for communicating with users and, more particularly, to alignment visualisers and editors (Sect. 11.4).
Jérôme Euzenat, Pavel Shvaiko
Chapter 12. Processing Alignments
Abstract
In this book, we have taken a two-step view on reducing semantic heterogeneity: (i) matching of entities to determine alignment and (ii) processing the alignment according to application needs. In the previous chapters, we have discussed various themes related to the first step. In this chapter, in turn, we present how the alignments can be specifically used by applications, thus focussing on the alignment processing step.
Jérôme Euzenat, Pavel Shvaiko

Conclusions

Frontmatter
Chapter 13. Conclusions
Abstract
In this book, we have attempted at covering ontology matching in its diversity. In particular, we have shown that many applications may need ontology matching (Chap. 1) and that there are different forms of ontologies that may need to be matched (Chap. 2). Based on reasonable methodological rules (Chap. 3), ontology matching can take advantage of innumerable basic (Chap. 5) and advanced (Chap. 6) techniques composed and supervised in diverse ways (Chap. 7). This has led to a profusion of available systems (Chap. 8). The output of matching can be provided according to different representations (Chap. 10) or executable forms (Chap. 12) which may need to be communicated to users (Chap. 11).
Jérôme Euzenat, Pavel Shvaiko
Backmatter
Metadaten
Titel
Ontology Matching
verfasst von
Jérôme Euzenat
Pavel Shvaiko
Copyright-Jahr
2013
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-642-38721-0
Print ISBN
978-3-642-38720-3
DOI
https://doi.org/10.1007/978-3-642-38721-0

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