Semantic Matching: Algorithms and Implementation

Giunchiglia, Fausto; Yatskevich, Mikalai; Shvaiko, Pavel

doi:10.1007/978-3-540-74987-5_1

Cited by 144 publications

(165 citation statements)

References 38 publications

Supporting

Mentioning

165

Contrasting

Order By: Relevance

“…Execution time indicator shows scalability properties of the matchers and their potential to become industrial-strength systems. Also, referring to [41], the fact that some systems run out of memory on some test cases, although being fast on the other test cases, suggests that their performance time is achieved by using a large amount of main memory. Therefore, usage of main memory should also be taken into account.…”

Section: Performance Of Ontology-matching Techniquesmentioning

confidence: 99%

“…Optimizations are worth been done only once the underlying basic techniques are stable. For example, in the case of S-Match [35,38,41], when dealing with lightweight ontologies [32,42], the matching problem was reduced to the validity problem for the propositional calculus. The basic version of S-Match was using a standard DPLLbased satisfiability procedure of SAT4J 11 .…”

Section: Performance Of Ontology-matching Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Ten Challenges for Ontology Matching

Shvaiko¹,

Euzenat

2008

Lecture Notes in Computer Science

Self Cite

210

135

View full text Add to dashboard Cite

Abstract. This paper aims at analyzing the key trends and challenges of the ontology matching field. The main motivation behind this work is the fact that despite many component matching solutions that have been developed so far, there is no integrated solution that is a clear success, which is robust enough to be the basis for future development, and which is usable by non expert users. In this paper we first provide the basics of ontology matching with the help of examples. Then, we present general trends of the field and discuss ten challenges for ontology matching, thereby aiming to direct research into the critical path and to facilitate progress of the field.

show abstract

Section: Performance Of Ontology-matching Techniquesmentioning

confidence: 99%

Section: Performance Of Ontology-matching Techniquesmentioning

confidence: 99%

Ten Challenges for Ontology Matching

Shvaiko¹,

Euzenat

2008

Lecture Notes in Computer Science

Self Cite

210

135

View full text Add to dashboard Cite

show abstract

“…Identifying semantic correspondences is a computational expensive task. In fact, tools leveraging on semantics, including MinSMatch, typically require logical reasoning support that can amount to exponential computation in the worst case [22]. It is therefore fundamental to develop techniques that limit as much as possible the calls to logical reasoners.…”

Section: Limitations Of Current Matching Toolsmentioning

confidence: 99%

“…8. ComputeMappingElement is structurally very similar to the NodeMatch function described in [27], modulo the key difference that no calls to SAT are needed. ComputeMappingElement always returns the strongest mapping element.…”

Section: Step 3: Computing the Mapping Of Maximum Sizementioning

confidence: 99%

Save Up to 99% of Your Time in Mapping Validation

Maltese

Giunchiglia

Autayeu

2010

On the Move to Meaningful Internet Systems, OTM 2010

Self Cite

View full text Add to dashboard Cite

Abstract. Identifying semantic correspondences between different vocabularies has been recognized as a fundamental step towards achieving interoperability. Several manual and automatic techniques have been recently proposed. Fully manual approaches are very precise, but extremely costly. Conversely, automatic approaches tend to fail when domain specific background knowledge is needed. Consequently, they typically require a manual validation step. Yet, when the number of computed correspondences is very large, the validation phase can be very expensive. In order to reduce the problems above, we propose to compute the minimal set of correspondences, that we call the minimal mapping, which are sufficient to compute all the other ones. We show that by concentrating on such correspondences we can save up to 99% of the manual checks required for validation.

show abstract

“…Our approach has some commonalities with approaches for semantic schema matching [12,42], which take as input two graph-like structures and produce a mapping between the nodes of these graphs that correspond semantically to each other. First, in a pre-processing phase, the labels at the graph nodes, which are initially written in natural language, are translated into propositional formulas to explicitly and formally codify the label's intended meaning.…”

Section: Related Workmentioning

confidence: 99%

Ontology-Driven Conceptual Design of ETL Processes Using Graph Transformations

Skoutas

Simitsis

Sellis³

2009

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. One of the main tasks during the early steps of a data warehouse project is the identification of the appropriate transformations and the specification of inter-schema mappings from the source to the target data stores. This is a challenging task, requiring firstly the semantic and secondly the structural reconciliation of the information provided by the available sources. This task is a part of the Extract-Transform-Load (ETL) process, which is responsible for the population of the data warehouse. In this paper, we propose a customizable and extensible ontologydriven approach for the conceptual design of ETL processes. A graphbased representation is used as a conceptual model for the source and target data stores. We then present a method for devising flows of ETL operations by means of graph transformations. In particular, the operations comprising the ETL process are derived through graph transformation rules, the choice and applicability of which are determined by the semantics of the data with respect to an attached domain ontology. Finally, we present our experimental findings that demonstrate the applicability of our approach.

show abstract

Semantic Matching: Algorithms and Implementation

Cited by 144 publications

References 38 publications

Ten Challenges for Ontology Matching

Ten Challenges for Ontology Matching

Save Up to 99% of Your Time in Mapping Validation

Ontology-Driven Conceptual Design of ETL Processes Using Graph Transformations

Contact Info

Product

Resources

About