D. P. Ballou and H. L. Pazer, Modeling completeness versus consistency tradeoffs in information decision contexts. Knowledge and Data Engineering, IEEE Transactions on, vol.15, issue.1, pp.240-243, 2003.

C. Batini, C. Cappiello, C. Francalanci, and A. Maurino, Methodologies for data quality assessment and improvement, ACM Computing Surveys (CSUR), vol.41, issue.3, p.16, 2009.

S. Bechhofer, I. Buchan, D. De-roure, P. Missier, J. Ainsworth et al., Why linked data is not enough for scientists, Future Generation Computer Systems, vol.29, issue.2, pp.599-611, 2013.

L. Berti-equille, I. Comyn-wattiau, M. Cosquer, Z. Kedad, S. Nugier et al., Assessment and analysis of information quality: a multidimensional model and case studies, vol.IJIQ, pp.300-323, 2011.
URL : https://hal.archives-ouvertes.fr/hal-01345434

P. Chen, W. Garcia, ;. F. Sun, Y. Wang, J. Lu et al., Hypothesis generation and data quality assessment through association mining, Proceedings of the 9th IEEE International Conference on Cognitive Informatics, ICCI 2010, pp.659-666, 2010.

E. F. Codd, Missing information (applicable and inapplicable) in relational databases, SIGMOD Record, vol.15, issue.4, pp.53-78, 1986.

F. Darari, W. Nutt, G. Pirrò, S. Razniewski-;-h.-alani, L. Kagal et al., Completeness statements about RDF data sources and their use for query answering, The Semantic Web -ISWC 2013 -12th International Semantic Web Conference, vol.8218, pp.66-83, 2013.

C. M. Eastman and B. J. Jansen, Coverage, relevance, and ranking: The impact of query operators on web search engine results, ACM Transactions on Information Systems (TOIS), vol.21, issue.4, pp.383-411, 2003.

C. Fürber and M. Hepp, Swiqa-a semantic web information quality assessment framework, ECIS, vol.15, p.19, 2011.

J. Golbeck, Combining provenance with trust in social networks for semantic web content filtering, Lecture Notes in Computer Science, vol.4145, pp.101-108, 2006.

K. Gouda and M. J. Zaki, Efficiently mining maximal frequent itemsets, Proceedings of the 2001 IEEE International Conference on Data Mining, ICDM '01, pp.163-170, 2001.

G. Grahne and J. Zhu, Efficiently using prefix-trees in mining frequent itemsets, FIMI '03, Frequent Itemset Mining Implementations, Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations, vol.90, 2003.

J. Han, J. Pei, and Y. Yin, Mining frequent patterns without candidate generation, Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp.1-12, 2000.

J. Han, J. Pei, Y. Yin, and R. Mao, Mining frequent patterns without candidate generation: A frequentpattern tree approach, Data Min. Knowl. Discov, vol.8, issue.1, pp.53-87, 2004.

O. Hartig, Trustworthiness of data on the web, Proceedings of the STI Berlin & CSW PhD Workshop. Citeseer, 2008.

O. Hartig and J. Zhao, Using web data provenance for quality assessment, Proceedings of the First International Workshop on the role of Semantic Web in Provenance Management (SWPM 2009), collocated with the 8th International Semantic Web Conference (ISWC-2009), vol.526, 2009.

D. M. Herzig and T. Tran, Heterogeneous web data search using relevance-based on the fly data integration, Proceedings of the 21st World Wide Web Conference, pp.141-150, 2012.

A. Hogan, A. Harth, A. Passant, S. Decker, and A. Polleres, Weaving the pedantic web, Proceedings of the WWW2010 Workshop on Linked Data on the Web, LDOW 2010, vol.628, 2010.

M. G. Institute, M. Chui, J. Manyika, J. Bughin, R. Dobbs et al., The social economy: Unlocking value and productivity through social technologies, 2012.

R. J. , Efficiently mining long patterns from databases, Proceedings ACM SIGMOD International Conference on Management of Data, pp.85-93, 1998.

Y. W. Lee, D. M. Strong, B. K. Kahn, and R. Y. Wang, Aimq: a methodology for information quality assessment, Information & management, vol.40, issue.2, pp.133-146, 2002.

M. Markovic, P. Edwards, D. Corsar, and J. Z. Pan, The crowd and the web of linked data: A provenance perspective, Wisdom of the Crowd, Papers from the 2012 AAAI Spring Symposium, 2012.

P. Mendes, C. Bizer, Y. Young, Z. Miklos, J. Calbimonte et al., Conceptual model and best practices for high-quality metadata

P. N. Mendes, H. Mühleisen, and C. Bizer, Sieve: linked data quality assessment and fusion, Proceedings of the 2012 Joint EDBT/ICDT Workshops, pp.116-123, 2012.

T. Omitola, N. Gibbins, and N. Shadbolt, Provenance in Linked Data Integration, Proc. of Linked Data in the Future Internet at the Future Internet Assembly, vol.16, 2010.

L. L. Pipino, Y. W. Lee, and R. Y. Wang, Data quality assessment, Communications of the ACM, vol.45, issue.4, pp.211-218, 2002.

M. Samwald, A. Jentzsch, C. Bouton, C. S. Kallesøe, E. Willighagen et al., Linked open drug data for pharmaceutical research and development, Journal of cheminformatics, vol.3, issue.1, p.19, 2011.

Y. Wand and R. Y. Wang, Anchoring data quality dimensions in ontological foundations, Communications of the ACM, vol.39, issue.11, pp.86-95, 1996.

R. Y. Wang and D. M. Strong, Beyond accuracy: What data quality means to data consumers, pp.5-33, 1996.

A. Zaveri, A. Rula, A. Maurino, R. Pietrobon, J. Lehmann et al., Quality assessment methodologies for linked open data, 2013.