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

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

Proceedings Article•

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

Fausto Giunchiglia, Pavel Shvaiko, Mikalai Yatskevich

01 Jan 2005-pp 0

TL;DR: An algorithm implementing semantic matching is presented, and its implementation within the S-Match system is discussed, and the results, though preliminary, look promising, in particular for what concerns precision and recall.

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Abstract: 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.

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Book•

Ontology Matching

[...]

Jérôme Euzenat, Pavel Shvaiko

05 Jun 2007

TL;DR: 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.

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Abstract: 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.

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2,579 citations

Cites background or methods from "S-Match: an algorithm and an implem..."

...re-implementation of CtxMatch with a few added functionalities (Giunchiglia et al. 2004)....
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...A matcher based on WordNet can be designed by translating the (lexical) relations provided by WordNet to logical relations according to the following rules (Giunchiglia et al. 2004):...
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..., equivalence, subsumption (≤), and incompatibility (⊥) (Giunchiglia et al. 2004; Bouquet et al. 2003b; Hamdi et al. 2010b; Spiliopoulos et al. 2010)....
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Book Chapter•DOI•

A survey of schema-based matching approaches

[...]

Pavel Shvaiko¹, Jérôme Euzenat²•Institutions (2)

University of Trento¹, French Institute for Research in Computer Science and Automation²

01 Jan 2005-Journal on Data Semantics

TL;DR: This paper presents a new classification of schema-based matching techniques that builds on the top of state of the art in both schema and ontology matching and distinguishes between approximate and exact techniques at schema-level; and syntactic, semantic, and external techniques at element- and structure-level.

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Abstract: 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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1,285 citations

Cites background or methods from "S-Match: an algorithm and an implem..."

...However, it is sufficiently autonomous for being singled out, see, for example [32, 33]....
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...S-Match....
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...As from [12, 32, 33], the approach is to decompose the graph (tree) matching problem into the set of node matching problems....
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...Semantic techniques have been exploited only by S-Match [33]....
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...string-based (5); WordNet: S-Match [33, 34] language-based (3); sense-based (2), propositional SAT (2) gloss-based (6)...
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Book Chapter•DOI•

LogMap: logic-based and scalable ontology matching

[...]

Ernesto Jiménez-Ruiz¹, Bernardo Cuenca Grau¹•Institutions (1)

University of Oxford¹

23 Oct 2011

TL;DR: This paper presents LogMap--a highly scalable ontology matching system with 'built-in' reasoning and diagnosis capabilities, and is the only matching system that can deal with semantically rich ontologies containing tens (and even hundreds of thousands of classes).

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Abstract: In this paper, we present LogMap--a highly scalable ontology matching system with 'built-in' reasoning and diagnosis capabilities. To the best of our knowledge, LogMap is the only matching system that can deal with semantically rich ontologies containing tens (and even hundreds) of thousands of classes. In contrast to most existing tools, LogMap also implements algorithms for 'on the fly' unsatisfiability detection and repair. Our experiments with the ontologies NCI, FMA and SNOMED CT confirm that our system can efficiently match even the largest existing bio-medical ontologies. Furthermore, LogMap is able to produce a 'clean' set of output mappings in many cases, in the sense that the ontology obtained by integrating LogMap's output mappings with the input ontologies is consistent and does not contain unsatisfiable classes.

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473 citations

Cites background from "S-Match: an algorithm and an implem..."

..., S-Match [10]), in practice reasoning is known to aggravate the scalability problem (e....
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...Although the first reasoning-based techniques for ontology matching were proposed relatively early on (e.g., S-Match [10]), in practice reasoning is known to aggravate the scalability problem (e.g., no reasoner known to us can classify the integration NCI-SNOMED via UMLS)....
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Journal Article•DOI•

Ontology matching with semantic verification

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Yves R. Jean-Mary, E. Patrick Shironoshita, Mansur R. Kabuka¹•Institutions (1)

University of Miami¹

01 Sep 2009-Journal of Web Semantics

TL;DR: Experimental results show the increased accuracy obtained by combining lexical, structural and extensional matchers with semantic verification, and demonstrate the advantage of using a domain-specific thesaurus for the alignment of specialized ontologies.

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415 citations

Journal Article•DOI•

Ontology matching

[...]

Lorena Otero-Cerdeira¹, Francisco Javier Rodríguez-Martínez¹, Alma Gómez-Rodríguez¹•Institutions (1)

University of Vigo¹

01 Feb 2015-Expert Systems With Applications

TL;DR: A literature review regarding articles on ontology matching published in the last decade serves the purpose of offering an up-to-date review of the field and showing its evolution trends.

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Abstract: We present a literature review regarding articles on ontology matching published in the last decade.It serves the purpose of offering an up-to-date review of the field and showing its evolution trends.Over 1600 papers have been sorted according to a classification framework that we have defined.This framework helps in identifying the distribution of the load work in the last decade.Practitioners have been consulted to contrast and validate the results of the review. The amount of research papers published nowadays related to ontology matching is remarkable and we believe that reflects the growing interest of the research community. However, for new practitioners that approach the field, this amount of information might seem overwhelming. Therefore, the purpose of this work is to help in guiding new practitioners get a general idea on the state of the field and to determine possible research lines.To do so, we first perform a literature review of the field in the last decade by means of an online search. The articles retrieved are sorted using a classification framework that we propose, and the different categories are revised and analyzed. The information in this review is extended and supported by the results obtained by a survey that we have designed and conducted among the practitioners.

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352 citations

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References

PDF

Open Access

More filters

Journal Article•DOI•

WordNet: a lexical database for English

[...]

George A. Miller¹•Institutions (1)

Princeton University¹

01 Nov 1995-Communications of The ACM

TL;DR: WordNet1 provides a more effective combination of traditional lexicographic information and modern computing, and is an online lexical database designed for use under program control.

...read moreread less

Abstract: Because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines. WordNet1 provides a more effective combination of traditional lexicographic information and modern computing. WordNet is an online lexical database designed for use under program control. English nouns, verbs, adjectives, and adverbs are organized into sets of synonyms, each representing a lexicalized concept. Semantic relations link the synonym sets [4].

...read moreread less

15,068 citations

"S-Match: an algorithm and an implem..." refers background in this paper

...The core idea is to compute atomic concepts, as they are denoted by atomic labels (namely, labels of single words), as the senses provided by WordNet [16]....
[...]

Journal Article•DOI•

A survey of approaches to automatic schema matching

[...]

Erhard Rahm¹, Philip A. Bernstein²•Institutions (2)

Leipzig University¹, Microsoft²

01 Dec 2001

TL;DR: A taxonomy is presented that distinguishes between schema-level and instance-level, element- level and structure- level, and language-based and constraint-based matchers and is intended to be useful when comparing different approaches to schema matching, when developing a new match algorithm, and when implementing a schema matching component.

...read moreread less

Abstract: Schema matching is a basic problem in many database application domains, such as data integration, E-business, data warehousing, and semantic query processing. In current implementations, schema matching is typically performed manually, which has significant limitations. On the other hand, previous research papers have proposed many techniques to achieve a partial automation of the match operation for specific application domains. We present a taxonomy that covers many of these existing approaches, and we describe the approaches in some detail. In particular, we distinguish between schema-level and instance-level, element-level and structure-level, and language-based and constraint-based matchers. Based on our classification we review some previous match implementations thereby indicating which part of the solution space they cover. We intend our taxonomy and review of past work to be useful when comparing different approaches to schema matching, when developing a new match algorithm, and when implementing a schema matching component.

...read moreread less

3,693 citations

Proceedings Article•DOI•

[...]

Sergey Melnik¹, Hector Garcia-Molina¹, Erhard Rahm²•Institutions (2)

Stanford University¹, Leipzig University²

26 Feb 2002

TL;DR: This paper presents a matching algorithm based on a fixpoint computation that is usable across different scenarios and conducts a user study, in which the accuracy metric was used to estimate the labor savings that the users could obtain by utilizing the algorithm to obtain an initial matching.

...read moreread less

Abstract: Matching elements of two data schemas or two data instances plays a key role in data warehousing, e-business, or even biochemical applications. In this paper we present a matching algorithm based on a fixpoint computation that is usable across different scenarios. The algorithm takes two graphs (schemas, catalogs, or other data structures) as input, and produces as output a mapping between corresponding nodes of the graphs. Depending on the matching goal, a subset of the mapping is chosen using filters. After our algorithm runs, we expect a human to check and if necessary adjust the results. As a matter of fact, we evaluate the 'accuracy' of the algorithm by counting the number of needed adjustments. We conducted a user study, in which our accuracy metric was used to estimate the labor savings that the users could obtain by utilizing our algorithm to obtain an initial matching. Finally, we illustrate how our matching algorithm is deployed as one of several high-level operators in an implemented testbed for managing information models and mappings.

...read moreread less

1,613 citations

"S-Match: an algorithm and an implem..." refers background or methods in this paper

...Some examples of previous solutions are [12], [1], [15], [18], [5], [10]; see [6] for an in depth discussion about syntactic and semantic matching....
[...]
...We have compared S-Match with three state of the art schema-based matching systems, namely Cupid [12], COMA [4], and Similarity Flooding (SF) [15] as implemented within the Rondo system [14]....
[...]
...We have done some preliminary comparison between S-Match and three state of the art matching systems, namely Cupid [12], COMA [4], and SF [15] as implemented within the Rondo system [14]....
[...]

Proceedings Article•

Generic Schema Matching with Cupid

[...]

Jayant Madhavan¹, Philip A. Bernstein², Erhard Rahm³•Institutions (3)

University of Washington¹, Microsoft², Leipzig University³

11 Sep 2001

TL;DR: This paper proposes a new algorithm, Cupid, that discovers mappings between schema elements based on their names, data types, constraints, and schema structure, using a broader set of techniques than past approaches.

...read moreread less

Abstract: Schema matching is a critical step in many applications, such as XML message mapping, data warehouse loading, and schema integration. In this paper, we investigate algorithms for generic schema matching, outside of any particular data model or application. We first present a taxonomy for past solutions, showing that a rich range of techniques is available. We then propose a new algorithm, Cupid, that discovers mappings between schema elements based on their names, data types, constraints, and schema structure, using a broader set of techniques than past approaches. Some of our innovations are the integrated use of linguistic and structural matching, context-dependent matching of shared types, and a bias toward leaf structure where much of the schema content resides. After describing our algorithm, we present experimental results that compare Cupid to two other schema matching systems.

...read moreread less

1,533 citations

"S-Match: an algorithm and an implem..." refers background or methods in this paper

...We have compared S-Match with three state of the art, schema based, matching systems, namely Cupid [11], COMA [1], and Similarity Flooding (SF) [14] as implemented within the Rondo system [13]....
[...]
...We have compared S-Match with three state of the art schema-based matching systems, namely Cupid [12], COMA [4], and Similarity Flooding (SF) [15] as implemented within the Rondo system [14]....
[...]
...We have done some preliminary comparison between S-Match and three state of the art matching systems, namely Cupid [12], COMA [4], and SF [15] as implemented within the Rondo system [14]....
[...]
...Element level weak semantics matchers have been vastly used in previous syntactic matchers, for instance in [12] and [4]....
[...]
...Some examples of previous solutions are [12], [1], [15], [18], [5], [10]; see [6] for an in depth discussion about syntactic and semantic matching....
[...]

Book Chapter•DOI•

COMA: a system for flexible combination of schema matching approaches

[...]

Hong-Hai Do¹, Erhard Rahm¹•Institutions (1)

Leipzig University¹

20 Aug 2002

TL;DR: This work develops the COMA schema matching system as a platform to combine multiple matchers in a flexible way and uses COMA as a framework to comprehensively evaluate the effectiveness of different matchers and their combinations for real-world schemas.

...read moreread less

Abstract: Schema matching is the task of finding semantic correspondences between elements of two schemas. It is needed in many database applications, such as integration of web data sources, data warehouse loading and XML message mapping. To reduce the amount of user effort as much as possible, automatic approaches combining several match techniques are required. While such match approaches have found considerable interest recently, the problem of how to best combine different match algorithms still requires further work. We have thus developed the COMA schema matching system as a platform to combine multiple matchers in a flexible way. We provide a large spectrum of individual matchers, in particular a novel approach aiming at reusing results from previous match operations, and several mechanisms to combine the results of matcher executions. We use COMA as a framework to comprehensively evaluate the effectiveness of different matchers and their combinations for real-world schemas. The results obtained so far show the superiority of combined match approaches and indicate the high value of reuse-oriented strategies.

...read moreread less

1,199 citations

"S-Match: an algorithm and an implem..." refers background or methods in this paper

...We have compared S-Match with three state of the art, schema based, matching systems, namely Cupid [11], COMA [1], and Similarity Flooding (SF) [14] as implemented within the Rondo system [13]....
[...]
...We have compared S-Match with three state of the art schema-based matching systems, namely Cupid [12], COMA [4], and Similarity Flooding (SF) [15] as implemented within the Rondo system [14]....
[...]
...We have done some preliminary comparison between S-Match and three state of the art matching systems, namely Cupid [12], COMA [4], and SF [15] as implemented within the Rondo system [14]....
[...]
...Element level weak semantics matchers have been vastly used in previous syntactic matchers, for instance in [12] and [4]....
[...]