Schema Matching Using Duplicates

Bilke, Alexander; Naumann, Felix

doi:10.1109/icde.2005.126

Cited by 143 publications

(141 citation statements)

References 24 publications

(25 reference statements)

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“…It is based on the cosine similarity that doesn't automatically discard words which are not strictly identical. This metric has two main advantages: 1) the token order is not important, 2) common uninformative words don't greatly affect similarity [15], [16]. The SoftTFIDF similarity measure can be computed between s and t as follows:…”

Section: B Similarity Function Assignmentmentioning

confidence: 99%

User Profile Matching in Social Networks

Raad

Chbeir

Dipandà

2010

2010 13th International Conference on Network-Based Information Systems

130

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Abstract-Inter-social networks operations and functionalities are required in several scenarios (data integration, data enrichment, information retrieval, etc.). To achieve this, matching user profiles is required. Current methods are so restrictive and do not consider all the related problems. Particularly, they assume that two profiles describe the same physical person only if the values of their Inverse Functional Property or IFP (e.g. the email address, homepage, etc.) are the same. However, the observed trend in social networks is not fully compatible with this assumption since users tend to create more than one social network account (for personal use, for work, etc.) while using same or different email addresses. In this work, we address the problem of matching user profiles in its globality by providing a suitable matching framework able to consider all the profile's attributes. Our framework allows users to give more importance to some attributes and assign each attribute a different similarity measure. The set of experiments conducted with our default/recommended attribute/similarity measures shows the superiority of our proposal in comparison with current ones.

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Section: B Similarity Function Assignmentmentioning

confidence: 99%

User Profile Matching in Social Networks

Raad

Chbeir

Dipandà

2010

2010 13th International Conference on Network-Based Information Systems

130

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“…Bilke and Naumann [Bilke and Naumann 2005] propose a semantic technique based on an analysis of duplicated instances. Leme et al ] introduced the notion of a contextualized vocabulary matching between a source ontology and a target ontology using a finite set of quadruples as the specification model; and also proposed a semantic schema matching technique based on similarity functions.…”

Section: Related Workmentioning

confidence: 99%

Towards Automatic Generation of Application Ontologies

Sacramento¹,

Vidal²,

Macêdo³

et al. 2010

Proceedings of the 12th International Conference on Enterprise Information Systems

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Abstract. In the context of the Semantic Web, a domain ontology may be used to provide the necessary support for linking together a large number of heterogeneous data sources pertaining to a same domain. In this paper, such data sources are first described as local ontologies. Then, each local ontology is rewritten as an application ontology, whose vocabulary is restricted to be a subset of the vocabulary of the domain ontology. Application ontologies enable the identification and the association of semantically corresponding concepts, and thereby help information discovery and retrieval, and also data integration. The main contribution of this paper is a strategy to automatically generate application ontologies, considering a set of local ontologies, a domain ontology and the result of the matching between each local ontology and the domain ontology. The proposed strategy also enables the automatic generation of mappings between the domain ontology and the application ontologies, and between each application ontology and its corresponding local ontology, which are used to query the data sources through the domain ontology.

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“…In this approach, no assumptions of common domains, global schema, underlying generative ontology, or other simplifications are made. Data are treated simply as opaque objects; the search process is purely syntactically and structurally driven [1,11]. The user-provided source and target instances provide the initial matches which drive the search process.…”

Section: Example 2 Consider the Basic Transformations Involved In Resmentioning

confidence: 99%

“…The approaches to data mapping most closely related to our data mapping solution are the works of Bilke and Naumann [1] and Kang and Naughton [11] on schema matching, and the Clio project [15] on schema mapping. To our knowledge, these works have not considered the full space of data-metadata transformations, with only the Clio [15] project considering any aspects of such mappings.…”

Section: Related Workmentioning

confidence: 99%