P. Bernstein, J. Madhavan, and E. Rahm, Generic Schema Matching, Ten Years Later, 2011.

P. Shvaiko and J. Euzenat, Tutorial on schema and ontology matching, pp.5-29, 2005.

E. Rahm and P. A. Bernstein, A Survey of Approaches to Automatic Schema Matching, JVLDB, pp.334-350, 2001.

M. G. De-carvalho, A. H. Laender, M. A. Gonçalves, A. S. Da, and . Silva, An evolutionary approach to complex schema matching, Information Systems, vol.38, issue.3, pp.302-316, 2013.

N. F. Noy and M. A. Musen, Algorithm and tool for automated ontology merging and alignment, 2000.

Z. Dragisic, K. Eckert, J. Euzenat, A. Ferrara, R. Granada et al., Results of the ontology alignment evaluation initiative, 2014.
URL : https://hal.archives-ouvertes.fr/hal-02089264

S. R. Jeffery, M. J. Franklin, and A. Y. Halevy, Pay-as-you-go user feedback for dataspace systems, pp.847-860, 2008.

M. Sayyadian, H. Lekhac, A. Doan, and L. Gravano, Efficient keyword search across heterogeneous relational databases, pp.346-355, 2007.

Z. Yan, N. Zheng, Z. G. Ives, P. P. Talukdar, and C. Yu, Actively soliciting feedback for query answers in keyword search-based data integration, pp.205-216, 2013.

K. Belhajjame, N. W. Paton, A. A. Fernandes, C. Hedeler, and S. M. Embury, User feedback as a first class citizen in information integration systems, pp.175-183, 2011.

Q. V. Nguyen, T. T. Nguyen, Z. Miklós, K. Aberer, A. Gal et al., Pay-as-you-go reconciliation in schema matching networks, pp.220-231, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00875448

P. Cudré-mauroux, K. Aberer, and A. Feher, Probabilistic Message Passing in Peer Data Management Systems, pp.41-52, 2006.

Q. V. Nguyen, T. K. Wijaya, Z. Miklos, K. Aberer, E. Levy et al., Minimizing Human Effort in Reconciling Match Networks, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00838259

T. Sagi and A. , Non-binary evaluation measures for big data integration, VLDBJ, pp.105-126, 2018.
DOI : 10.1007/s00778-017-0489-y

B. Gu, Z. Li, X. Zhang, A. Liu, G. Liu et al., The interaction between schema matching and record matching in data integration, TKDE, pp.186-199, 2017.

Y. Wang and Y. He, Synthesizing mapping relationships using table corpus, pp.1117-1132, 2017.
DOI : 10.1145/3035918.3064010
URL : http://arxiv.org/pdf/1705.09276

P. Vassiliadis, A. V. Zarras, and I. Skoulis, Gravitating to rigidity: Patterns of schema evolution-and its absence-in the lives of tables, Information Systems, vol.63, pp.24-46, 2017.

H. Gonzalez, A. Y. Halevy, C. S. Jensen, A. Langen, J. Madhavan et al., Google fusion tables: web-centered data management and collaboration, pp.1061-1066, 2010.

W. Litwin, L. Mark, and N. Roussopoulos, CSUR, pp.267-293, 1990.

K. Smith, M. Morse, P. Mork, M. Li, A. Rosenthal et al., The role of schema matching in large enterprises, 2009.

K. Aberer, P. Cudre-mauroux, A. M. Ouksel, T. Catarci, M. Hacid et al., Emergent semantics principles and issues, pp.25-38, 2004.
DOI : 10.1007/978-3-540-24571-1_2
URL : https://hal.archives-ouvertes.fr/hal-01585949

L. Ardissono, A. Goy, G. Petrone, and M. Segnan, From service clouds to user-centric personal clouds, pp.1-8, 2009.
DOI : 10.1109/cloud.2009.61
URL : http://www.di.unito.it/%7Egoy/papers/cloudII09.pdf

S. R. Yerva, Z. Miklós, and K. Aberer, Quality-aware similarity assessment for entity matching in web data, Information Systems, vol.37, issue.4, pp.336-351, 2012.
DOI : 10.1016/j.is.2011.09.007
URL : https://infoscience.epfl.ch/record/169237/files/yerva_IS_Elseiver_Final.pdf

D. Rinser, D. Lange, and F. Naumann, Cross-lingual entity matching and infobox alignment in wikipedia, Information Systems, vol.38, issue.6, pp.887-907, 2013.
DOI : 10.1016/j.is.2012.10.003

D. Ritter, N. May, and S. Rinderle-ma, Patterns for emerging application integration scenarios: A survey, Information Systems, vol.67, pp.36-57, 2017.
DOI : 10.1016/j.is.2017.03.003

D. Fensel, H. Lausen, A. Polleres, J. De-bruijn, M. Stollberg et al., Enabling semantic web services: the web service modeling ontology, 2006.

M. Paolucci, T. Kawamura, T. R. Payne, and K. Sycara, Semantic matching of web services capabilities, pp.333-347, 2002.

D. Aumueller, H. Do, S. Massmann, and E. Rahm, Schema and ontology matching with coma++, pp.906-908, 2005.
DOI : 10.1145/1066157.1066283
URL : http://se-pubs.dbs.uni-leipzig.de/files/sigmod2005-coma++.pdf

D. H. Ngo, YAM++ : (not) Yet Another Matcher for Ontology Matching Task, Z. Bellahsene, 2012.
DOI : 10.1016/j.websem.2016.09.002
URL : https://hal.archives-ouvertes.fr/lirmm-00720648

L. Seligman, P. Mork, A. Halevy, K. Smith, M. J. Carey et al., Openii: an open source information integration toolkit, pp.1057-1060, 2010.

A. Doan, P. Domingos, and A. Y. Halevy, Reconciling schemas of disparate data sources: a machine-learning approach, pp.509-520, 2001.

A. , Uncertain Schema Matching, 2011.

A. Gal, T. Sagi, M. Weidlich, E. Levy, V. Shafran et al., Making sense of top-k matchings: A unified match graph for schema matching, 2012.

F. R. Kschischang, B. J. Frey, and H. Loeliger, Factor graphs and the sumproduct algorithm, IEEE Trans. Inf. Theory, pp.498-519, 2001.

Q. V. Nguyen, H. X. Luong, Z. Miklós, and K. Aberer, Collaborative schema matching reconciliation, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00842697

Q. V. Nguyen, T. T. Nguyen, Z. Miklós, and K. Aberer, On leveraging crowdsourcing techniques for schema matching networks, pp.139-154, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00812037

C. Zhang and C. Ré, Towards high-throughput gibbs sampling: A study across storage managers, pp.397-408, 2013.

A. Mccallum, K. Bellare, and F. Pereira, A conditional random field for discriminatively-trained finite-state string edit distance, pp.388-395, 2005.

G. Casella and E. I. George, Explaining the gibbs sampler, The American Statistician, vol.46, issue.3, pp.167-174, 1992.

C. Kim and C. R. Nelson, State-space models with regime switching: classical and gibbs-sampling approaches with applications

B. Marthi, H. Pasula, S. Russell, and Y. Peres, Decayed mcmc iltering, pp.319-326, 2002.

J. Hopcroft and R. Tarjan, Algorithm 447: efficient algorithms for graph manipulation, CACM, pp.372-378, 1973.

Q. V. Nguyen, Reconciling schema matching networks, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01103662

M. Franklin, A. Halevy, and D. Maier, From databases to dataspaces: a new abstraction for information management, pp.27-33, 2005.

H. Roitman and A. , Ontobuilder: fully automatic extraction and consolidation of ontologies from web sources using sequence semantics, pp.573-576, 2006.

E. Peukert, J. Eberius, and E. Rahm, AMC -A framework for modelling and comparing matching systems as matching processes, pp.1304-1307, 2011.

D. Faria, C. Pesquita, E. Santos, I. F. Cruz, and F. M. Couto, Agreementmakerlight: a scalable automated ontology matching system, p.29, 2014.

Z. Bellahsene, A. Bonifati, and E. Rahm, Schema Matching and Mapping, 2011.
URL : https://hal.archives-ouvertes.fr/lirmm-00581346

A. Gal, H. Roitman, and T. Sagi, From diversity-based prediction to better ontology & schema matching, pp.1145-1155, 2016.
DOI : 10.1145/2872427.2882999

C. J. Zhang, L. Chen, H. Jagadish, and C. C. Cao, Reducing uncertainty of schema matching via crowdsourcing, pp.757-768, 2013.

T. T. Nguyen, Q. V. Nguyen, M. Weidlich, and K. Aberer, Result selection and summarization for web table search, pp.231-242, 2015.
DOI : 10.1109/icde.2015.7113287

F. Duchateau, R. Coletta, Z. Bellahsene, and R. J. Miller, (not) yet another matcher, pp.1537-1540, 2009.
DOI : 10.1145/1645953.1646165

A. Gal and T. Sagi, Tuning the ensemble selection process of schema matchers, JIS, pp.845-859, 2010.

Y. Lee, M. Sayyadian, A. Doan, and A. S. Rosenthal, eTuner: tuning schema matching software using synthetic scenarios, pp.97-122, 2007.

M. Vargas-vera and M. Nagy, State of the art on ontology alignment, pp.17-42, 2015.

L. Otero-cerdeira, F. J. Rodríguez-martínez, and A. Gómez-rodríguez, Ontology matching: A literature review, pp.949-971, 2015.

N. T. Tam, M. Weidlich, D. C. Thang, H. Yin, and N. Q. Hung, Retaining data from streams of social platforms with minimal regret, pp.2850-2856, 2017.

X. Dong, A. Y. Halevy, and C. Yu, Data integration with uncertainty, pp.687-698, 2007.

A. D. Sarma, X. Dong, and A. Y. Halevy, Bootstrapping pay-as-you-go data integration systems, pp.861-874, 2008.

J. Gong, R. Cheng, and D. W. Cheung, Efficient management of uncertainty in XML schema matching, JVLDB, pp.385-409, 2012.

N. B. Amor, D. Dubois, H. Gouider, and H. Prade, Possibilistic preference networks, ISCI, vol.460, pp.401-415, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01303854

M. Haddad, P. Leray, and N. B. Amor, Learning possibilistic networks from data: a survey, pp.IFSA-EUSFLAT, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01150815

C. Borgelt and R. Kruse, Operations and evaluation measures for learning possibilistic graphical models, AI, vol.148, issue.1-2, pp.385-418, 2003.

C. Borgelt and R. Kruse, Learning possibilistic graphical models from data, TFS, vol.11, issue.2, pp.159-172, 2003.

W. Su, J. Wang, and F. Lochovsky, Holistic schema matching for web query interfaces, pp.77-94, 2006.

J. Madhavan, P. A. Bernstein, A. Doan, and A. Halevy, Corpus-based schema matching, pp.57-68, 2005.

B. Saha, I. Stanoi, and K. L. Clarkson, Schema covering: a step towards enabling reuse in information integration, pp.285-296, 2010.

A. Gal, M. Katz, T. Sagi, M. Weidlich, K. Aberer et al., Completeness and ambiguity of schema cover, pp.241-258, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00842699

K. Aberer, P. Cudré-mauroux, and M. Hauswirth, Start making sense: The Chatty Web approach for global semantic agreements, JWS, pp.89-114, 2003.

T. T. Nguyen, T. C. Phan, Q. V. Nguyen, K. Aberer, and B. Stantic, Maximal fusion of facts on the web with credibility guarantee, Information Fusion, vol.48, pp.55-66, 2019.

S. Duan, A. Fokoue, and K. Srinivas, One size does not fit all: Customizing ontology alignment using user feedback, pp.177-192, 2010.

I. F. Cruz, C. Stroe, and M. Palmonari, Interactive user feedback in ontology matching using signature vectors, pp.1321-1324, 2012.

N. Q. Hung, D. C. Thang, M. Weidlich, and K. Aberer, Minimizing efforts in validating crowd answers, pp.999-1014, 2015.

A. V. Zhdanova and P. Shvaiko, Community-Driven Ontology Matching, pp.34-49, 2006.

R. Mccann, W. Shen, and A. Doan, Matching Schemas in Online Communities: A Web 2.0 Approach, pp.110-119, 2008.

I. F. Cruz, F. Loprete, M. Palmonari, C. Stroe, and A. Taheri, Pay-as-you-go multi-user feedback model for ontology matching, pp.80-96, 2014.

N. Q. Hung, N. T. Tam, L. N. Tran, and K. Aberer, An evaluation of aggregation techniques in crowdsourcing, pp.1-15, 2013.

N. Q. Hung, C. T. Duong, N. T. Tam, M. Weidlich, K. Aberer et al., Argument discovery via crowdsourcing, J, vol.26, issue.4, pp.511-535, 2017.