Matching Large Scale Ontology Effectively

Wang, Zongjiang; Wang, Yinglin; Zhang, Shensheng; Ge, Shen; Du, Tao

doi:10.1007/11836025_10

Cited by 14 publications

(2 citation statements)

References 6 publications

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“…For ontologies containing tens of thousands of subsumption relations (as AGROVOC and NALT) this type of constraint can lead to the creation of blocks with badly distributed sizes, unusable for alignment. However, this technique is used by the MOM system to align (theoretically) large ontologies, but the tests presented in [15] are only applied on ontologies of less than 700 concepts.…”

Section: State Of the Artmentioning

confidence: 99%

Alignment-Based Partitioning of Large-Scale Ontologies

Hamdi

Safar

Reynaud

et al. 2010

Advances in Knowledge Discovery and Management

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Ontology alignment is an important task for information integration systems that can make different resources, described by various and heterogeneous ontologies, interoperate. However very large ontologies have been built in some domains such as medicine or agronomy and the challenge now lays in scaling up alignment techniques that often perform complex tasks. In this paper, we propose two partitioning methods which have been designed to take the alignment objective into account in the partitioning process as soon as possible. These methods transform the two ontologies to be aligned into two sets of blocks of a limited size. Furthermore, the elements of the two ontologies that might be aligned are grouped in a minimal set of blocks and the comparison is then enacted upon these blocks. Results of experiments performed by the two methods on various pairs of ontologies are promising.

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Section: State Of the Artmentioning

confidence: 99%

Alignment-Based Partitioning of Large-Scale Ontologies

Hamdi

Safar

Reynaud

et al. 2010

Advances in Knowledge Discovery and Management

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“…The results of previous OAEI contests 1 show that more than half of the matching systems couldn't match large ontologies in less than one hour [12]. Consequently, several approaches have been proposed to address the problem of matching two large schemas, such as MOM [20], COMA++ [6] and Falcon [11]. As we will further discuss in Section 2, the current approaches to partition-based matching have several limitations and the design space for such solutions has not yet sufficiently been explored.…”

Section: Introductionmentioning

confidence: 99%

A Clustering-Based Approach for Large-Scale Ontology Matching

Algergawy

Maßmann

Rahm

2011

Advances in Databases and Information Systems

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Abstract. Schema and ontology matching have attracted a great deal of interest among researchers. Despite the advances achieved, the large matching problem still presents a real challenge, such as it is a timeconsuming and memory-intensive process. We therefore propose a scalable, clustering-based matching approach that breaks up the large matching problem into smaller matching problems. In particular, we first introduce a structure-based clustering approach to partition each schema graph into a set of disjoint subgraphs (clusters). Then, we propose a new measure that efficiently determines similar clusters between every two sets of clusters to obtain a set of small matching tasks. Finally, we adopt the matching prototype COMA++ to solve individual matching tasks and combine their results. The experimental analysis reveals that the proposed method permits encouraging and significant improvements.

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