Ontology-Based Data Cleaning

Kedad, Zoubida; Métais, Elisabeth

doi:10.1007/3-540-36271-1_12

Cited by 29 publications

(18 citation statements)

References 14 publications

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“…The resulting ontologies were incorporated into the global model, enhancing the information retrieval. Compared to other works, such as the ones presented in [12] and [15], our system provided a broader range of preprocessing suggestions. The generated preprocessing ontologies provide an open an intuitive approach for data sharing and management.…”

Section: Discussionmentioning

confidence: 99%

“…Integration at a schema level [10], D2RMAP, SEMEDA, KAON, ONTOFUSION [5] Preprocessing and Integration at an instance level [11], [12], ONTOCLEAN, ONTODATACLEAN [4] The majority of these systems follow an approach with two sequential steps: (i) Detection and (ii) Resolution. Although the resolution phase has been successfully automated in these systems, the detection phase is still carried out manually.…”

Section: Kdd Phase Systemsmentioning

confidence: 99%

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Automatic Generation of Integration and Preprocessing Ontologies for Biomedical Sources in a Distributed Scenario

Anguita

Pérez-Rey

Crespo

et al. 2008

2008 21st IEEE International Symposium on Computer-Based Medical Systems

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Section: Discussionmentioning

confidence: 99%

Section: Kdd Phase Systemsmentioning

confidence: 99%

Automatic Generation of Integration and Preprocessing Ontologies for Biomedical Sources in a Distributed Scenario

Anguita

Pérez-Rey

Crespo

et al. 2008

2008 21st IEEE International Symposium on Computer-Based Medical Systems

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“…as described in [21], but that is part of our ongoing research. Also, we did not yet investigate problems within the conceptual model of ontologies themselves.…”

Section: Overview Of Data Quality Problemsmentioning

confidence: 97%

Using SPARQL and SPIN for Data Quality Management on the Semantic Web

Fürber

Hepp

2010

Business Information Systems

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Abstract. The quality of data is a key factor that determines the performance of information systems, in particular with regard (1) to the amount of exceptions in the execution of business processes and (2) to the quality of decisions based on the output of the respective information system. Recently, the Semantic Web and Linked Data activities have started to provide substantial data resources that may be used for real business operations. Hence, it will soon be critical to manage the quality of such data. Unfortunately, we can observe a wide range of data quality problems in Semantic Web data. In this paper, we (1) evaluate how the state of the art in data quality research fits the characteristics of the Web of Data, (2) describe how the SPARQL query language and the SPARQL Inferencing Notation (SPIN) can be utilized to identify data quality problems in Semantic Web data automatically and this within the Semantic Web technology stack, and (3) evaluate our approach.

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“…Kedad et al [77] propose a method for dealing with semantic heterogeneity during the process of data cleaning when integrating data from multiple sources, which is differences in terminologies. The proposed solution is based on linguistic knowledge provided by a domain is-a ontology.…”

Section: Domain-specific Approachesmentioning

confidence: 99%

Semantic Web in data mining and knowledge discovery: A comprehensive survey

Ristoski

Paulheim

2016

Journal of Web Semantics

230

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a b s t r a c tData Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. This survey article gives a comprehensive overview of those approaches in different stages of the knowledge discovery process. As an example, we show how Linked Open Data can be used at various stages for building content-based recommender systems. The survey shows that, while there are numerous interesting research works performed, the full potential of the Semantic Web and Linked Open Data for data mining and KDD is still to be unlocked.

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Ontology-Based Data Cleaning

Cited by 29 publications

References 14 publications

Automatic Generation of Integration and Preprocessing Ontologies for Biomedical Sources in a Distributed Scenario

Automatic Generation of Integration and Preprocessing Ontologies for Biomedical Sources in a Distributed Scenario

Using SPARQL and SPIN for Data Quality Management on the Semantic Web

Semantic Web in data mining and knowledge discovery: A comprehensive survey

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