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  4. RDF Doctor: A Holistic Approach for Syntax Error Detection and Correction of RDF Data
 
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2019
Conference Paper
Title

RDF Doctor: A Holistic Approach for Syntax Error Detection and Correction of RDF Data

Abstract
Over the years, the demand for interoperability support between diverse applications has significantly in- creased. The Resource Definition Framework (RDF), among other solutions, is utilized as a data modeling language which allows for encoding the knowledge from various domains in a unified representation. More- over, a vast amount of data from heterogeneous data sources are continuously published in documents using the RDF format. Therefore, these RDF documents should be syntactically correct in order to enable software agents performing further processing. Albeit, a number of approaches have been proposed for ensuring error-free RDF documents, commonly they are not able to identify all syntax errors at once by failing on the first encountered error. In this paper, we tackle the problem of simultaneous error identification, and propose RDF-Doctor, a holistic approach for detecting and resolving syntactic errors in a semi-automatic fashion. First, we define a comprehensive list of errors that can be detected along with customized error messages to allow users for a better understanding of the actual errors. Next, a subset of syntactic errors is corrected automatically based on matching them with predefined error messages. Finally, for a particular number of errors, customized and meaningful messages are delivered to users to facilitate the manual corrections process. The results from empirical evaluations provide evidence that the presented approach is able to effectively detect a wide range of syntax errors and automatically correct a large subset of them.
Author(s)
Hemid, Ahmad  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Halilaj, Lavdim  
Robert Bosch GmbH
Khiat, Abderrahmane  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Lohmann, Steffen  
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Mainwork
IC3K 2019, 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. Proceedings. Vol.2: KEOD  
Project(s)
LUCID
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)  
Fraunhofer-Gesellschaft FhG
Bundesministerium für Bildung und Forschung BMBF (Deutschland)  
Conference
International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) 2019  
International Conference on Knowledge Engineering and Ontology Development (KEOD) 2019  
Open Access
DOI
10.5220/0008493205080516
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
Keyword(s)
  • RDF

  • error detection

  • error correction

  • Syntax Validation

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