R. Agrawal and R. Srikant, Fast algorithms for mining association rules, VLDB, vol.1215, 1994.

A. V. Aho, R. Michael, J. D. Garey, and . Ullman, The transitive reduction of a directed graph, SIAM Journal on Computing, vol.1, issue.2, 1972.

F. Baader, D. Calvanese, D. L. Mcguinness, and D. Nardi, The Description Logic Handbook, 2003.

M. Bienvenu, F. M. Suchanek, and D. Deutch, Provenance for web 2.0 data, SDM workshop, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00789146

C. Bizer, T. Heath, K. Idehen, and T. Berners-lee, Linked data on the Web, 2008.

K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor, Freebase: a collaboratively created graph database for structuring human knowledge, SIGMOD, 2008.

A. Bordes, X. Glorot, J. Weston, and Y. Bengio, A semantic matching energy function for learning with multi-relational data, Machine Learning, vol.94, issue.2, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00835282

A. Bordes, N. Usunier, A. Garcia-duran, J. Weston, and O. Yakhnenko, Translating Embeddings for Modeling Multi-relational Data, NIPS, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00920777

A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E. Hruschka et al., Toward an architecture for never-ending language learning, AAAI, 2010.

J. Jeremy, C. Carroll, P. Bizer, P. Hayes, and . Stickler, Named graphs, provenance and trust, 2005.

Y. Chen, D. Z. Wang, and S. Goldberg, Scalekb: Scalable learning and inference over large knowledge bases, VLDBJ, 2016.

K. Luc-de-raedt and . Kersting, Probabilistic inductive logic programming, Probabilistic Inductive Logic Programming, 2008.

L. Dehaspe and L. De-raedt, Mining association rules in multiple relations, International Conference on Inductive Logic Programming, 1997.

T. Dettmers, P. Minervini, P. Stenetorp, and S. Riedel, Convolutional 2d Knowledge Graph Embeddings, AAAI, 2018.

X. Dong, E. Gabrilovich, G. Heitz, W. Horn, N. Lao et al., Knowledge vault: A web-scale approach to probabilistic knowledge fusion, SIGKDD, 2014.

C. Minh-duc-tran, . Amato, T. Binh, and A. G. Nguyen, Tettamanzi. Comparing rule evaluation metrics for the evolutionary discovery of multirelational association rules in the semantic web, Genetic Programming, 2018.

O. Etzioni, M. Cafarella, D. Downey, S. Kok, A. Popescu et al., Web-scale information extraction in knowitall, 2004.

N. Fanizzi, C. Amato, F. Esposito, and P. Minervini, Numeric prediction on owl knowledge bases through terminological regression trees, International Journal of Semantic Computing, vol.6, issue.04, 2012.

, WordNet: An Electronic Lexical Database, 1998.

D. Ferrucci, E. Brown, J. Chu-carroll, J. Fan, D. Gondek et al., Building watson: An overview of the deepqa project, AI magazine, vol.31, issue.3, 2010.

M. D. Fisher, D. M. Gabbay, and L. Vila, Handbook of temporal reasoning in artificial intelligence, 2005.

H. Mohamed, D. Gad-elrab, J. Stepanova, G. Urbani, and . Weikum, Exception-enriched rule learning from knowledge graphs, ISWC, 2016.

L. Galárraga, S. Razniewski, A. Amarilli, and F. M. Suchanek, Predicting completeness in knowledge bases, WSDM, 2017.

L. Galárraga and F. M. Suchanek, Towards a numerical rule mining language, AKBC workshop, 2014.

L. Galárraga, C. Teflioudi, K. Hose, and F. M. Suchanek, Amie: Association rule mining under incomplete evidence in ontological knowledge bases, 2013.

L. Galárraga, C. Teflioudi, K. Hose, and F. M. Suchanek, Fast rule mining in ontological knowledge bases with amie+, VLDBJ, 2015.

L. Getoor, P. Christopher, and . Diehl, Link mining: a survey, ACM SIGKDD Explorations Newsletter, vol.7, issue.2, 2005.

C. Gutierrez, A. Carlos, A. Hurtado, and . Vaisman, Introducing time into rdf, IEEE Transactions on Knowledge and Data Engineering, vol.19, issue.2, 2007.

J. Hawthorne, Inductive logic, 2018.

S. Hellmann, J. Lehmann, and S. Auer, Learning of owl class descriptions on very large knowledge bases, J. on Semantic Web and Information Systems, vol.5, issue.2, 2009.

L. Henderson, The Stanford Encyclopedia of Philosophy, 2019.

D. Vinh-thinh-ho, M. H. Stepanova, E. Gad-elrab, G. Kharlamov, and . Weikum, Rule learning from knowledge graphs guided by embedding models, ISWC, 2018.

J. Hoffart, F. M. Suchanek, K. Berberich, and G. Weikum, Yago2: A spatially and temporally enhanced knowledge base from wikipedia, Artificial Intelligence, 2013.

K. Inoue, Induction as consequence finding, Machine Learning, vol.55, 2004.

G. Ji, S. He, L. Xu, K. Liu, and J. Zhao, Knowledge Graph Embedding via Dynamic Mapping Matrix, ACL, 2015.

T. Kimber, K. Broda, and A. Russo, Induction on failure: Learning connected horn theories, International Conference on Logic Programming and Nonmonotonic Reasoning, 2009.

M. Krötzsch, M. Marx, A. Ozaki, and V. Thost, Attributed description logics: Reasoning on knowledge graphs, IJCAI, 2018.

J. Lajus and F. M. Suchanek, Are all people married? determining obligatory attributes in knowledge bases, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01699857

N. Lao, T. Mitchell, and W. W. Cohen, Random walk inference and learning in a large scale knowledge base, EMNLP, 2011.

J. Lehmann, R. Isele, M. Jakob, A. Jentzsch, D. Kontokostas et al., DBpedia -a large-scale, multilingual knowledge base extracted from wikipedia, Semantic Web Journal, vol.6, issue.2, 2015.

B. Douglas, . Lenat, V. Ramanathan, and . Guha, Building large knowledge-based systems; representation and inference in the Cyc project, 1989.

A. Lerer, L. Wu, J. Shen, T. Lacroix, L. Wehrstedt et al., PyTorch-BigGraph: A Large-scale Graph Embedding System, Conference on Systems and Machine Learning, 2019.

Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu, Learning Entity and Relation Embeddings for Knowledge Graph Completion, AAAI, 2015.

H. Liu and P. Singh, Conceptnet. BT Technology Journal, vol.22, issue.4, 2004.

H. Liu, Y. Wu, and Y. Yang, Analogical inference for multirelational embeddings, ICML, 2017.

E. Margolis and S. Laurence, Concepts, The Stanford Encyclopedia of Philosophy, 2014.

M. Marx, M. Krötzsch, and V. Thost, Logic on mars: Ontologies for generalised property graphs, IJCAI, 2017.

A. Melo, M. Theobald, and J. Völker, Correlation-based refinement of rules with numerical attributes, FLAIRS, 2014.

S. Muggleton, Inverse entailment and progol, New generation computing, vol.13, issue.3-4, 1995.

S. Muggleton and L. De-raedt, Inductive logic programming: Theory and methods, The Journal of Logic Programming, vol.19, 1994.

S. Muggleton and C. Feng, Efficient induction of logic programs, 1990.

T. D. Dai-quoc-nguyen, D. Nguyen, D. Quoc-nguyen, and . Phung, A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network, NAACL, 2018.

M. Nickel, L. Rosasco, and T. Poggio, Holographic Embeddings of Knowledge Graphs, AAAI, 2016.

M. Nickel, H. Volker-tresp, and . Kriegel, A Three-way Model for Collective Learning on Multi-relational Data, ICML, 2011.

S. Ortona, P. Venkata-vamsikrishna-meduri, and . Papotti, Robust discovery of positive and negative rules in knowledge bases, ICDE, 2018.

D. Thomas-pellissier-tanon, S. Stepanova, P. Razniewski, G. Mirza, and . Weikum, Completeness-aware rule learning from knowledge graphs, 2017.

G. Plotkin, Automatic methods of inductive inference, 1972.

S. and P. R. Navigli, BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network, Artificial Intelligence, vol.193, 2012.

O. Ray, K. Broda, and A. Russo, Hybrid abductive inductive learning: A generalisation of progol, International Conference on Inductive Logic Programming, 2003.

S. Razniewski, F. M. Suchanek, and W. Nutt, But what do we actually know?, AKBC workshop, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01699864

B. Russell, The Problems of Philosophy, 1912.

S. Russell and P. Norvig, Artificial Intelligence: a Modern Approach, 2002.

Y. Ehud and . Shapiro, Inductive inference of theories from facts, 1981.

R. Socher, D. Chen, D. Christopher, A. Manning, and . Ng, Reasoning With Neural Tensor Networks for Knowledge Base Completion, NIPS, 2013.

A. Soulet, A. Giacometti, B. Markhoff, and F. M. Suchanek, Representativeness of Knowledge Bases with the Generalized Benford's Law, ISWC, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01824490

J. F. Sowa, Knowledge Representation: Logical, Philosophical, and Computational Foundations, 2000.

, Handbook on Ontologies. International Handbooks on Information Systems, 2004.

D. Stepanova, H. Mohamed, V. Gad-elrab, and . Ho, Rule induction and reasoning over knowledge graphs, Reasoning Web International Summer School, 2018.

F. M. Suchanek, S. Abiteboul, and P. Senellart, Probabilistic alignment of relations, instances, and schema, VLDB, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00745191

F. M. Suchanek, G. Kasneci, and G. Weikum, Yago -a core of semantic knowledge, 2007.
URL : https://hal.archives-ouvertes.fr/hal-01472497

M. Fabian, N. Suchanek, and . Preda, Semantic Culturomics, VLDB short paper track, 2014.

N. Tandon, A. Gerard-de-melo, G. De, and . Weikum, Knowlywood: Mining Activity Knowledge From Hollywood Narratives, CIKM, 2015.

N. Tandon, F. M. Gerard-de-melo, G. Suchanek, and . Weikum, WebChild: Harvesting and Organizing Commonsense Knowledge from the Web, WSDM, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01699891

M. Telgarsky, Representation Benefits of Deep Feedforward Networks, 2015.

T. Trouillon and M. Nickel, Complex and Holographic Embeddings of Knowledge Graphs: A Comparison, 2017.

O. Udrea, D. R. Recupero, and V. S. Subrahmanian, Annotated rdf, ACM Transactions on Computational Logic, vol.11, issue.2, 2010.

D. Vrande?i? and M. Krötzsch, Wikidata: a free collaborative knowledgebase, Communications of the ACM, vol.57, issue.10, 2014.

P. Wang, S. Li, and R. Pan, Incorporating GAN for Negative Sampling in Knowledge Representation Learning, AAAI, 2018.

Z. Wang, J. Zhang, J. Feng, and Z. Chen, Knowledge Graph Embedding by Translating on Hyperplanes, AAAI, 2014.

C. Welty, R. Fikes, and S. Makarios, A reusable ontology for fluents in owl, FOIS, 2006.

A. Whitehead and B. Russell, Principia mathematica, 1913.

, Word Wide Web Consortium. RDF Primer, 2004.

, Word Wide Web Consortium. RDF Vocabulary Description Language 1.0: RDF Schema, 2004.

, Word Wide Web Consortium. SKOS Simple Knowledge Organization System, 2009.

, Word Wide Web Consortium. OWL 2 Web Ontology Language, 2012.

, Word Wide Web Consortium. SPARQL 1.1 Query Language, 2013.

M. Yahya, D. Barbosa, K. Berberich, Q. Wang, and G. Weikum, Relationship Queries on Extended Knowledge Graphs, WSDM, 2016.

A. Yamamoto, Hypothesis finding based on upward refinement of residue hypotheses, Theoretical Computer Science, vol.298, issue.1, 2003.

B. Yang, W. Yih, X. He, J. Gao, and L. Deng, Embedding Entities and Relations for Learning and Inference in Knowledge Bases, ICLR, 2014.

K. Zupanc and J. Davis, Estimating rule quality for knowledge base completion with the relationship between coverage assumption, 2018.