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 Shvaikos 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.

Ontology Matching

[서평]「The Unified Modeling Language User Guide」

Schema and ontology matching is a critical problem in many application domains, such as semantic web, schema/ontology integration, data warehouses, e-commerce, etc. Many different matching solutions have been proposed so far. In this paper we present a new classification of schema-based matching techniques that builds on the top of state of the art in both schema and ontology matching. Some innovations are in introducing new criteria which are based on (i) general properties of matching techniques, (ii) interpretation of input information, and (iii) the kind of input information. In particular, we distinguish between approximate and exact techniques at schema-level; and syntactic, semantic, and external techniques at element- and structure-level. Based on the classification proposed we overview some of the recent schema/ontology matching systems pointing which part of the solution space they cover. The proposed classification provides a common conceptual basis, and, hence, can be used for comparing different existing schema/ontology matching techniques and systems as well as for designing new ones, taking advantages of state of the art solutions.

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A survey of schema-based matching approaches

After years of research on ontology matching, it is reasonable to consider several questions: is the field of ontology matching still making progress? Is this progress significant enough to pursue further research? If so, what are the particularly promising directions? To answer these questions, we review the state of the art of ontology matching and analyze the results of recent ontology matching evaluations. These results show a measurable improvement in the field, the speed of which is albeit slowing down. We conjecture that significant improvements can be obtained only by addressing important challenges for ontology matching. We present such challenges with insights on how to approach them, thereby aiming to direct research into the most promising tracks and to facilitate the progress of the field.

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Ontology Matching: State of the Art and Future Challenges

In the recent years, different Web knowledge graphs, both free and commercial, have been created. While Google coined the term "Knowledge Graph" in 2012, there are also a few openly available knowledge graphs, with DBpedia, YAGO, and Freebase being among the most prominent ones. Those graphs are often constructed from semi-structured knowledge, such as Wikipedia, or harvested from the web with a combination of statistical and linguistic methods. The result are large-scale knowledge graphs that try to make a good trade-off between completeness and correctness. In order to further increase the utility of such knowledge graphs, various refinement methods have been proposed, which try to infer and add missing knowledge to the graph, or identify erroneous pieces of information. In this article, we provide a survey of such knowledge graph refinement approaches, with a dual look at both the methods being proposed as well as the evaluation methodologies used.

Knowledge graph refinement: A survey of approaches and evaluation methods

Results of the Ontology Alignment Evaluation Initiative

We think of Match as an operator which takes two graph-like structures and produces a mapping between those nodes of the two graphs that correspond semantically to each other. Semantic matching is a novel approach where semantic correspondences are discovered by computing and returning as a result, the semantic information implicitly or explicitly codified in the labels of nodes and arcs. In this paper we present an algorithm implementing semantic matching, and we discuss its implementation within the S-Match system. We also test S-Match against three state of the art matching systems. The results, though preliminary, look promising, in particular for what concerns precision
and recall.

https://drops.dagstuhl.de/opus/volltexte/2005/37/pdf/04391.GiunchigliaFausto1.Paper.37.pdf/

S-Match: an algorithm and an implementation of semantic matching

We think of Match as an operator which takes two graph-like structures (e.g., conceptual hierarchies or ontologies) and produces a mapping between those nodes of the two graphs that correspond semantically to each other. Semantic matching is a novel approach where semantic correspondences are discovered by computing, and returning as a result, the semantic information implicitly or explicitly codified in the labels of nodes and arcs. In this paper we present an algorithm implementing semantic matching, and we discuss its implementation within the S-Match system. We also test S-Match against three state of the art matching systems. The results, though preliminary, look promising, in particular for what concerns precision and recall.

/pdf/s-match-an-algorithm-and-an-implementation-of-semantic-1cg1bg1z1w.pdf

S-Match: An algorithm and an implementation of semantic matching

We view match as an operator that takes two graph-like structures (e.g., classifications, XML schemas) and produces a mapping between the nodes of these graphs that correspond semantically to each other. Semantic matching is based on two ideas: (i) we discover mappings by computing semantic relations (e.g., equivalence, more general); (ii) we determine semantic relations by analyzing the meaning (concepts, not labels) which is codified in the elements and the structures of schemas. In this paper we present basic and optimized algorithms for semantic matching, and we discuss their implementation within the S-Match system. We evaluate S-Match against three state of the art matching systems, thereby justifying empirically the strength of our approach.

https://groups.inf.ed.ac.uk/OK/Publications/Algorithms%20and%20Implementation.pdf

Semantic matching: algorithms and implementation

Typical ontology matching applications, such as ontology integration, focus on the computation of correspondences holding between the nodes of two graph-like structures, e.g., between concepts in two ontologies. However, for applications such as web service integration, we need to establish whether full graph structures correspond to one another globally, preserving certain structural properties of the graphs being considered. The goal of this paper is to provide a new matching operation, called structure-preserving semantic matching . This operation takes two graph-like structures and produces a set of correspondences, (i ) still preserving a set of structural properties of the graphs being matched, (ii ) only in the case if the graphs are globally similar to one another. Our approach is based on a formal theory of abstraction and on a tree edit distance measure. We have evaluated our solution in various settings. Empirical results show the efficiency and effectiveness of our approach.

/pdf/approximate-structure-preserving-semantic-matching-2idegshl5p.pdf

Mikalai Yatskevich

Papers

Results of the Ontology Alignment Evaluation Initiative

S-Match: an algorithm and an implementation of semantic matching

S-Match: An algorithm and an implementation of semantic matching

Semantic matching: algorithms and implementation

Approximate Structure-Preserving Semantic Matching