W. Beek, S. Schlobach, and F. Van-harmelen, A contextualised semantics for owl: sameas, International Semantic Web Conference, pp.405-419, 2016.
DOI : 10.1007/978-3-319-34129-3_25

W. Beek and J. Raad, Wielemaker, and Frank van Harmelen. sameas. cc: The closure of 500m owl: sameas statements, European Semantic Web Conference, pp.65-80, 2018.

W. Beek, L. Rietveld, and S. Schlobach, , 2016.

V. Blondel, J. Guillaume, R. Lambiotte, and E. Lefebvre, Fast unfolding of communities in large networks, J. of statistical mechanics, issue.10, p.10008, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01146070

. Ph, P. Cudré-mauroux, M. Haghani, K. Jost, H. Aberer et al., idMesh: graph-based disambiguation of linked data, WWW Conf, pp.591-600, 2009.

J. Cuzzola, E. Bagheri, and J. Jovanovic, Filtering inaccurate entity co-references on the linked open data, International DEXA Conference, pp.128-143, 2015.

G. De-melo, Not quite the same: Identity constraints for the web of linked data, 2013.

S. De-rooij, W. Beek, P. Bloem, F. Van-harmelen, and S. Schlobach, Are names meaningful? quantifying social meaning on the semantic web, ISWC, pp.184-199, 2016.

M. Dean, G. Schreiber, S. Bechhofer, F. Van-harmelen, J. Hendler et al., Owl web ontology language reference. W3C Recommendation February, vol.10, 2004.

L. Ding, J. Shinavier, T. Finin, and D. L. Mcguinness, owl:sameas and linked data: An empirical study, Proceedings of the Second Web Science Conference, 2010.

J. D. Fernández, W. Beek, M. A. Martínez-prieto, and M. Arias, LOD-a-lot-A Queryable Dump of the LOD Cloud, 2017.

C. Guéret, P. Groth, C. Stadler, and J. Lehmann, Assessing linked data mappings using network measures, Extended Semantic Web Conference, pp.87-102, 2012.

H. Halpin, J. Patrick, J. P. Hayes, D. L. Mccusker, H. Mcguinness et al., When owl: sameas isn't the same: An analysis of identity in linked data, ISWC, pp.305-320, 2010.

A. Hogan, A. Zimmermann, J. Umbrich, A. Polleres, and S. Decker, Scalable and distributed methods for entity matching, consolidation and disambiguation over linked data corpora, Web Semantics: Science, Services and Agents on the World Wide Web, vol.10, pp.76-110, 2012.

A. Jaffri, H. Glaser, and I. Millard, URI disambiguation in the context of Linked Data, Linked Data on the Web Workshop (LDOW), 2008.

A. Lancichinetti and S. Fortunato, Community detection algorithms: a comparative analysis, Physical review E, vol.80, issue.5, p.56117, 2009.

A. Lancichinetti, S. Fortunato, and F. Radicchi, Benchmark graphs for testing community detection algorithms, Physical review E, vol.78, issue.4, p.46110, 2008.

W. Liu, M. Pellegrini, and X. Wang, Detecting communities based on network topology, Scientific reports, vol.4, p.5739, 2014.

E. J. Mark and . Newman, Modularity and community structure in networks, Proceedings of the national academy of sciences, vol.103, pp.8577-8582, 2006.

L. Papaleo, N. Pernelle, F. Sa¨?ssa¨?s, and C. Dumont, Logical detection of invalid sameas statements in rdf data, International Conference EKAW, pp.373-384, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01275943

H. Paulheim, Identifying wrong links between datasets by multi-dimensional outlier detection, WoDOOM, pp.27-38, 2014.

J. Raad, N. Pernelle, and F. Sa¨?ssa¨?s, Detection of contextual identity links in a knowledge base, Proceedings of the Knowledge Capture Conference, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01665062

A. Valdestilhas, T. Soru, and A. Ngomo, Cedal: timeefficient detection of erroneous links in large-scale link repositories, International Conference on Web Intelligence, pp.106-113, 2017.

Z. Yang, R. Algesheimer, and C. Tessone, A comparative analysis of community detection algorithms on artificial networks, Scientific reports, vol.6, p.30750, 2016.
DOI : 10.2139/ssrn.2937843
URL : http://europepmc.org/articles/pmc4967864?pdf=render