Similarity flooding: a versatile graph matching algorithm and its application to schema matching

Melnik, Sergey; García-Molina, Héctor; Rahm, Erhard

doi:10.1109/icde.2002.994702

Cited by 873 publications

(840 citation statements)

References 12 publications

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“…Similarity Flooding (SF) [17] is a matching algorithm based on a fixpoint computation that is usable across different scenarios. SF takes two graphs as input, and produces as output a mapping between corresponding nodes.…”

Section: Related Work In Ontology Matchingmentioning

confidence: 99%

Knowledge Sharing and Reuse in Digital Forensics

Huang

Yasinsac

Hayes

2010

2010 Fifth IEEE International Workshop on Systematic Approaches to Digital Forensic Engineering

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Section: Related Work In Ontology Matchingmentioning

confidence: 99%

Knowledge Sharing and Reuse in Digital Forensics

Huang

Yasinsac

Hayes

2010

2010 Fifth IEEE International Workshop on Systematic Approaches to Digital Forensic Engineering

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“…Techniques for computing the mapping are discussed in [9]. The second alignment in this study uses schema matching algorithm and, more specifically, relies on the Cupid algorithm.…”

Section: Approaches To Aligning Ontologiesmentioning

confidence: 99%

Comparing two approaches for aligning representations of anatomy

Zhang

Mork

Bodenreider

et al. 2007

Artificial Intelligence in Medicine

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Objective-To analyze the comparison, through their results, of two distinct approaches applied to aligning two representations of anatomy.Materials-Both approaches use a combination of lexical and structural techniques. In addition, the first approach takes advantage of domain knowledge, while the second approach treats alignment as a special case of schema matching. The same versions of FMA and GALEN were aligned by each approach. 2199 concept matches were obtained by both approaches.Methods and results-For matches identified by one approach only (337 and 336 respectively), we analyzed the reasons that caused the other approach to fail. Conclusions-The first approach could be improved by addressing partial lexical matches and identifying matches based solely on structural similarity. The second approach may be improved by taking into account synonyms in FMA and identifying semantic mismatches. However, only 33% of the possible one-to-one matches among anatomical concepts were identified by the two approaches together. New directions need to be explored in order to handle more complex matches.

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“…Finally, Schema Matching [4,12,13] identifies semantic relations between schema elements based on their names, data types, constraints, and structures. The primary goal is to find the one-one simple correspondences which are part of the input for our mapping discovery algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…In short, the main contributions of this work are as follows: (i) we propose a mapping formalism to capture the semantics of XML schemas based on treepattern formulas [3]; (ii) we propose a heuristic algorithm for finding semantic mappings, which are akin to a tree connection embedded in the ontology; (iii) we enhance the algorithm by taking into account information about (a) XML Schema features such as occurrence constraints, key and keyref definitions, (b) cardinality constraints in the ontology, and (c) XML document design guidelines under the hypothesis that an explicit or implicit ontology existed during the process of XML document design; (iv) we adopt the accuracy metric of schema matching [13] and evaluate the tool with a number of experiments.…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by the success of the Clio tool [14,15], our tool takes three inputs: an ontology, an XML schema (actually, its unfolding into tree structures that we will call element trees), and simple correspondences between XML attributes and ontology datatype properties, of the kind possibly generated by already existing tools (e.g., [4,12,13]). The output is a ranked list of complex formulas representing semantic mappings of the kind described earlier.…”

Section: Introductionmentioning

confidence: 99%

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Constructing Complex Semantic Mappings Between XML Data and Ontologies

Borgida

Mylopoulos

2005

The Semantic Web – ISWC 2005

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Abstract. Much data is published on the Web in XML format satisfying schemas, and to make the Semantic Web a reality, such data needs to be interpreted with respect to ontologies. Interpretation is achieved through a semantic mapping between the XML schema and the ontology. We present work on the heuristic construction of complex such semantic mappings, when given an initial set of simple correspondences from XML schema attributes to datatype properties in the ontology. To accomplish this, we first offer a mapping formalism to capture the semantics of XML schemas. Second, we present our heuristic mapping construction algorithm. Finally, we show through an empirical study that considerable effort can be saved when constructing complex mappings by using our prototype tool.

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Similarity flooding: a versatile graph matching algorithm and its application to schema matching

Abstract: Abstract

Cited by 873 publications

References 12 publications

Knowledge Sharing and Reuse in Digital Forensics

Knowledge Sharing and Reuse in Digital Forensics

Comparing two approaches for aligning representations of anatomy

Constructing Complex Semantic Mappings Between XML Data and Ontologies

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