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Knowledge Graphs

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Hogan, Aidan, Blomqvist, Eva, Cochez, Michael, D’amato, Claudia, Melo, Gerard De, Gutierrez, Claudio, Kirrane, Sabrina, Gayo, José Emilio Labra, Navigli, Roberto, Neumaier, Sebastian, Ngomo, Axel-Cyrille Ngonga, Polleres, Axel, Rashid, Sabbir M., Rula, Anisa, Schmelzeisen, Lukas, Sequeda, Juan, Staab, Steffen, Zimmermann, Antoine·2022·ACM Computing Surveys
DOI 10.1145/3447772
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Abstract

In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.

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  1. 10.1109/ACCESS.2020.2973928 · DOI
  2. R. Angles . 2018 . The property graph database model . In Proc. of AMW. R. Angles. 2018. The property graph database model. In Proc. of AMW.
  3. 10.1145/3104031 · DOI
  4. R. Angles , P. Arenas , M. Barceló , P. A. Boncz , G. H. L. Fletcher , C. Gutierrez , T. Lindaaker , M. Paradies , S. Plantikow , J. F. Sequeda , O. van Rest , and H. Voigt . 2018. G-CORE: A core for future graph query languages . In Proc. of SIGMOD. R. Angles, P. Arenas, M. Barceló, P. A. Boncz, G. H. L. Fletcher, C. Gutierrez, T. Lindaaker, M. Paradies, S. Plantikow, J. F. Sequeda, O. van Rest, and H. Voigt. 2018. G-CORE: A core for future graph query languages. In Proc. of SIGMOD.
  5. 10.1145/1322432.1322433 · DOI
  6. F. Baader I. Horrocks C. Lutz and U. Sattler. 2017. An Introduction to Description Logic. Cambridge University Press. F. Baader I. Horrocks C. Lutz and U. Sattler. 2017. An Introduction to Description Logic. Cambridge University Press. · DOI
  7. I. Balazevic , C. Allen , and M. Hospedales , T. 2019. Hypernetwork knowledge graph embeddings . In Proc. of ICANN Workshops. I. Balazevic, C. Allen, and M. Hospedales, T.2019. Hypernetwork knowledge graph embeddings. In Proc. of ICANN Workshops.
  8. I. Balazevic , C. Allen , and T. M. Hospedales . 2019. Multi-relational Poincaré graph embeddings . In Proc. of NeurIPs. I. Balazevic, C. Allen, and T. M. Hospedales. 2019. Multi-relational Poincaré graph embeddings. In Proc. of NeurIPs.
  9. I. Balazevic , C. Allen , and T. M. Hospedales . 2019. TuckER: Tensor factorization for knowledge graph completion . In Proc. of EMNLP. I. Balazevic, C. Allen, and T. M. Hospedales. 2019. TuckER: Tensor factorization for knowledge graph completion. In Proc. of EMNLP.
  10. P. Barceló , E. V. Kostylev , M. Monet , J. Peréz , J. Reutter , and J. P. Silva . 2020. The logical expressiveness of graph neural networks . In Proc. of ICLR. P. Barceló, E. V. Kostylev, M. Monet, J. Peréz, J. Reutter, and J. P. Silva. 2020. The logical expressiveness of graph neural networks. In Proc. of ICLR.
  11. L. Bellomarini , E. Sallinger , and G. Gottlob . 2018. The Vadalog system: Datalog-based reasoning for knowledge graphs . Proc. oVLDB Endow. 11 , 9 ( 2018 ). L. Bellomarini, E. Sallinger, and G. Gottlob. 2018. The Vadalog system: Datalog-based reasoning for knowledge graphs. Proc. oVLDB Endow. 11, 9 (2018).
  12. K. Bollacker , P. Tufts , T. Pierce , and R. Cook . 2007. A platform for scalable, collaborative, structured information integration . In Proceedings of the International Workshop on Information Integration on the Web (IIWeb’07) , Ullas Nambiar and Zaiqing Nie (Eds.). K. Bollacker, P. Tufts, T. Pierce, and R. Cook. 2007. A platform for scalable, collaborative, structured information integration. In Proceedings of the International Workshop on Information Integration on the Web (IIWeb’07), Ullas Nambiar and Zaiqing Nie (Eds.).
  13. P. A. Bonatti , S. Decker , A. Polleres , and V. Presutti . 2018. Knowledge graphs: New directions for knowledge representation on the semantic web (Dagstuhl Seminar 18371) . Dagstuhl Rep. 8 , 9 ( 2018 ). P. A. Bonatti, S. Decker, A. Polleres, and V. Presutti. 2018. Knowledge graphs: New directions for knowledge representation on the semantic web (Dagstuhl Seminar 18371). Dagstuhl Rep. 8, 9 (2018).
  14. 10.1016/j.websem.2011.06.003 · DOI
  15. A. Bordes , N. Usunier , A. García-Durán , J. Weston , and O. Yakhnenko . 2013. Translating embeddings for modeling multi-relational data . In Proc. of NIPS. A. Bordes, N. Usunier, A. García-Durán, J. Weston, and O. Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Proc. of NIPS.
  16. J. Bruna , W. Zaremba , A. Szlam , and Y. LeCun . 2014. Spectral networks and locally connected networks on graphs . In Proc. of ICLR. J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun. 2014. Spectral networks and locally connected networks on graphs. In Proc. of ICLR.
  17. L. Bühmann , J. Lehmann , and P. Westphal . 2016. DL-learner—A framework for inductive learning on the Semantic Web . J. Web Seman. 39 ( 2016 ). L. Bühmann, J. Lehmann, and P. Westphal. 2016. DL-learner—A framework for inductive learning on the Semantic Web. J. Web Seman. 39 (2016).
  18. Š. Čebirić, F. Goasdoué , H. Kondylakis , D. Kotzinos , I. Manolescu , G. Troullinou , and M. Zneika . 2019. Summarizing semantic graphs: A survey . VLDB J. 28 , 3 ( 2019 ). Š. Čebirić, F. Goasdoué, H. Kondylakis, D. Kotzinos, I. Manolescu, G. Troullinou, and M. Zneika. 2019. Summarizing semantic graphs: A survey. VLDB J. 28, 3 (2019).
  19. M. Cochez , P. Ristoski , S. P. Ponzetto , and H. Paulheim . 2017. Global RDF vector space embeddings . In Proc. of ISWC. M. Cochez, P. Ristoski, S. P. Ponzetto, and H. Paulheim. 2017. Global RDF vector space embeddings. In Proc. of ISWC.
  20. C. d’ Amato , S. Staab , A. G. B. Tettamanzi , D. M. Tran , and F. L. Gandon . 2016. Ontology enrichment by discovering multi-relational association rules from ontological knowledge bases . In Proc. of SAC. C. d’Amato, S. Staab, A. G. B. Tettamanzi, D. M. Tran, and F. L. Gandon. 2016. Ontology enrichment by discovering multi-relational association rules from ontological knowledge bases. In Proc. of SAC.
  21. A. Dave , A. Jindal , L. E. Li , R. Xin , J. Gonzalez , and M. Zaharia . 2016. GraphFrames: An integrated API for mixing graph and relational queries . In Proc. of GRADES. A. Dave, A. Jindal, L. E. Li, R. Xin, J. Gonzalez, and M. Zaharia. 2016. GraphFrames: An integrated API for mixing graph and relational queries. In Proc. of GRADES.
  22. L. De Raedt (Ed.). 2008. Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies). Springer. L. De Raedt (Ed.). 2008. Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies). Springer. · DOI · Paper page
  23. T. Demeester , T. Rocktäschel , and S. Riedel . 2016. Lifted rule injection for relation embeddings . In Proc. of EMNLP. T. Demeester, T. Rocktäschel, and S. Riedel. 2016. Lifted rule injection for relation embeddings. In Proc. of EMNLP.
  24. T. Dettmers , P. Minervini , P. Stenetorp , and S. Riedel . 2018. Convolutional 2D knowledge graph embeddings . In Proc. of AAAI. T. Dettmers, P. Minervini, P. Stenetorp, and S. Riedel. 2018. Convolutional 2D knowledge graph embeddings. In Proc. of AAAI.
  25. R. Q. Dividino , S. Sizov , S. Staab , and B. Schueler . 2009. Querying for provenance, trust, uncertainty and other meta knowledge in RDF. J. Web Seman. 7 , 3 ( 2009 ). R. Q. Dividino, S. Sizov, S. Staab, and B. Schueler. 2009. Querying for provenance, trust, uncertainty and other meta knowledge in RDF. J. Web Seman. 7, 3 (2009).
  26. X. Dong , E. Gabrilovich , G. Heitz , W. Horn , N. Lao , K. Murphy , T. Strohmann , S. Sun , and W. Zhang . 2014. Knowledge vault: A web-scale approach to probabilistic knowledge fusion . In Proc. of KDD. X. Dong, E. Gabrilovich, G. Heitz, W. Horn, N. Lao, K. Murphy, T. Strohmann, S. Sun, and W. Zhang. 2014. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. In Proc. of KDD.
  27. L. Ehrlinger and W. Wöß . 2016. Towards a definition of knowledge graphs . In Proc. of SEMANTiCS Posters & Demos. L. Ehrlinger and W. Wöß. 2016. Towards a definition of knowledge graphs. In Proc. of SEMANTiCS Posters & Demos.
  28. D. Fensel U. Simsek K. Angele E. Huaman E. Kärle O. Panasiuk I. Toma J. Umbrich and A. Wahler. 2020. Knowledge Graphs—Methodology Tools and Selected Use Cases. Springer. D. Fensel U. Simsek K. Angele E. Huaman E. Kärle O. Panasiuk I. Toma J. Umbrich and A. Wahler. 2020. Knowledge Graphs—Methodology Tools and Selected Use Cases. Springer. · DOI · Paper page
  29. N. Francis , A. Green , P. Guagliardo , L. Libkin , T. Lindaaker , V. Marsault , S. Plantikow , M. Rydberg , P. Selmer , and A. Taylor . 2018. Cypher: An evolving query language for property graphs . In Proc. of SIGMOD. N. Francis, A. Green, P. Guagliardo, L. Libkin, T. Lindaaker, V. Marsault, S. Plantikow, M. Rydberg, P. Selmer, and A. Taylor. 2018. Cypher: An evolving query language for property graphs. In Proc. of SIGMOD.
  30. M. H. Gad-Elrab , D. Stepanova , J. Urbani , and G. Weikum . 2016. Exception-enriched rule learning from knowledge graphs . In Proc. of ISWC. M. H. Gad-Elrab, D. Stepanova, J. Urbani, and G. Weikum. 2016. Exception-enriched rule learning from knowledge graphs. In Proc. of ISWC.
  31. L. A. Galárraga , C. Teflioudi , K. Hose , and F. Suchanek . 2013. AMIE: Association rule mining under incomplete evidence in ontological knowledge bases . In Proc. of WWW. L. A. Galárraga, C. Teflioudi, K. Hose, and F. Suchanek. 2013. AMIE: Association rule mining under incomplete evidence in ontological knowledge bases. In Proc. of WWW.
  32. 10.1007/s00778-015-0394-1 · DOI · Paper page
  33. G. A. Gesese , R. Biswas , and Sack H . 2019. A comprehensive survey of knowledge graph embeddings with literals: Techniques and applications . In Proc. of DL4KG. G. A. Gesese, R. Biswas, and Sack H.2019. A comprehensive survey of knowledge graph embeddings with literals: Techniques and applications. In Proc. of DL4KG.
  34. J. M. Giménez-García , A. Zimmermann , and P. Maret . 2017. NdFluents: An ontology for annotated statements with inference preservation . In Proc. of ESWC. J. M. Giménez-García, A. Zimmermann, and P. Maret. 2017. NdFluents: An ontology for annotated statements with inference preservation. In Proc. of ESWC.
  35. X. Glorot , A. Bordes , J. Weston , and Y. Bengio . 2013. A semantic matching energy function for learning with multi-relational data . In Proc. of ICLR Workshops. X. Glorot, A. Bordes, J. Weston, and Y. Bengio. 2013. A semantic matching energy function for learning with multi-relational data. In Proc. of ICLR Workshops.
  36. R. V. Guha , R. McCool , and R. Fikes . 2004. Contexts for the semantic web . In Proc. of ISWC. R. V. Guha, R. McCool, and R. Fikes. 2004. Contexts for the semantic web. In Proc. of ISWC.
  37. S. Guo , Q. Wang , L. Wang , B. Wang , and L. Guo . 2016. Jointly embedding knowledge graphs and logical rules . In Proc. of EMNLP. S. Guo, Q. Wang, L. Wang, B. Wang, and L. Guo. 2016. Jointly embedding knowledge graphs and logical rules. In Proc. of EMNLP.
  38. 10.1109/TKDE.2007.34 · DOI · Paper page
  39. O. Hartig . 2017 . Foundations of RDF* and SPARQL*—An alternative approach to statement-level metadata in RDF . In Proc. of AMW. O. Hartig. 2017. Foundations of RDF* and SPARQL*—An alternative approach to statement-level metadata in RDF. In Proc. of AMW.
  40. 10.1007/978-3-031-79432-2 · DOI
  41. N. Heist S. Hertling R. Ringler and H. Paulheim. 2020. Knowledge graphs on the web—An overview. CoRR abs/2003.00719 (2020). N. Heist S. Hertling R. Ringler and H. Paulheim. 2020. Knowledge graphs on the web—An overview. CoRR abs/2003.00719 (2020). · arXiv
  42. D. Hernández , A. Hogan , and M. Krötzsch . 2015. Reifying RDF: What works well with Wikidata? In Proc. of SSWS. D. Hernández, A. Hogan, and M. Krötzsch. 2015. Reifying RDF: What works well with Wikidata? In Proc. of SSWS.
  43. P. Hitzler M. Krötzsch and S. Rudolph. 2010. Foundations of Semantic Web Technologies. Chapman and Hall/CRC Press. P. Hitzler M. Krötzsch and S. Rudolph. 2010. Foundations of Semantic Web Technologies. Chapman and Hall/CRC Press. · DOI · Paper page
  44. V. T. Ho , D. Stepanova , M. H. Gad-Elrab , E. Kharlamov , and G. Weikum . 2018. Rule learning from knowledge graphs guided by embedding models . In Proc. of ISWC. V. T. Ho, D. Stepanova, M. H. Gad-Elrab, E. Kharlamov, and G. Weikum. 2018. Rule learning from knowledge graphs guided by embedding models. In Proc. of ISWC.
  45. J. Hoffart , F. M. Suchanek , K. Berberich , E. Lewis-Kelham , G. de Melo , and G. Weikum . 2011. YAGO2: Exploring and querying world knowledge in time, space, context, and many languages . In Proc. of WWW. J. Hoffart, F. M. Suchanek, K. Berberich, E. Lewis-Kelham, G. de Melo, and G. Weikum. 2011. YAGO2: Exploring and querying world knowledge in time, space, context, and many languages. In Proc. of WWW.
  46. A. Hogan . 2020. Knowledge graphs: Research directions . In Proc. of Reasoning Web . Springer . A. Hogan. 2020. Knowledge graphs: Research directions. In Proc. of Reasoning Web. Springer.
  47. A. Hogan , E. Blomqvist , M. Cochez , C. d’ Amato , G. de Melo , C. Gutierrez , J. E. Labra Gayo , S. Kirrane , S. Neumaier , A. Polleres , R. Navigli , A. C. Ngonga Ngomo , S. M. Rashid , A. Rula , L. Schmelzeisen , J. F. Sequeda , S. Staab , and A. Zimmermann . 2020. Knowledge graphs . CoRRarxiv : 2003 . 02320 (2020). A. Hogan, E. Blomqvist, M. Cochez, C. d’Amato, G. de Melo, C. Gutierrez, J. E. Labra Gayo, S. Kirrane, S. Neumaier, A. Polleres, R. Navigli, A. C. Ngonga Ngomo, S. M. Rashid, A. Rula, L. Schmelzeisen, J. F. Sequeda, S. Staab, and A. Zimmermann. 2020. Knowledge graphs. CoRRarxiv:2003.02320 (2020).
  48. M. Homola and L. Serafini . 2012. Contextualized knowledge repositories for the semantic web . J. Web Seman. 12 ( 2012 ). M. Homola and L. Serafini. 2012. Contextualized knowledge repositories for the semantic web. J. Web Seman. 12 (2012).
  49. X. Huang , J. Zhang , D. Li , and P. Li . 2019. Knowledge graph embedding based question answering . In Proc. of WSDM. X. Huang, J. Zhang, D. Li, and P. Li. 2019. Knowledge graph embedding based question answering. In Proc. of WSDM.
  50. R. Hussein , D. Yang , and P. Cudré-Mauroux . 2018. Are meta-paths necessary? Revisiting heterogeneous graph embeddings . In Proc. of CIKM. R. Hussein, D. Yang, and P. Cudré-Mauroux. 2018. Are meta-paths necessary? Revisiting heterogeneous graph embeddings. In Proc. of CIKM.
  51. 10.14778/3007263.3007270 · DOI
  52. D. Janke and S. Staab . 2018. Storing and querying semantic data in the cloud . In Proc. of RW. D. Janke and S. Staab. 2018. Storing and querying semantic data in the cloud. In Proc. of RW.
  53. G. Ji , S. He , L. Xu , K. Liu , and J. Zhao . 2015. Knowledge graph embedding via dynamic mapping matrix . In Proc. of ACL. G. Ji, S. He, L. Xu, K. Liu, and J. Zhao. 2015. Knowledge graph embedding via dynamic mapping matrix. In Proc. of ACL.
  54. E. Kärle U. Simsek O. Panasiuk and D. Fensel. 2018. Building an ecosystem for the Tyrolean tourism knowledge graph. CoRR abs/1805.05744 (2018). E. Kärle U. Simsek O. Panasiuk and D. Fensel. 2018. Building an ecosystem for the Tyrolean tourism knowledge graph. CoRR abs/1805.05744 (2018). · arXiv
  55. M. Kejriwal . 2019. Domain-specific Knowledge Graph Construction . Springer . M. Kejriwal. 2019. Domain-specific Knowledge Graph Construction. Springer.
  56. T. N. Kipf and M. Welling . 2017. Semi-supervised classification with graph convolutional networks . In Proc. of ICLR. T. N. Kipf and M. Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proc. of ICLR.
  57. M. Krötzsch , M. Marx , A. Ozaki , and V. Thost . 2018. Attributed description logics: Reasoning on knowledge graphs . In Proc. of IJCAI. M. Krötzsch, M. Marx, A. Ozaki, and V. Thost. 2018. Attributed description logics: Reasoning on knowledge graphs. In Proc. of IJCAI.
  58. J. E. Labra Gayo E. Prud’hommeaux I. Boneva and D. Kontokostas. 2017. Validating RDF Data. Vol. 7. Morgan & Claypool. J. E. Labra Gayo E. Prud’hommeaux I. Boneva and D. Kontokostas. 2017. Validating RDF Data. Vol. 7. Morgan & Claypool. · DOI
  59. 10.3233/SW-140134 · DOI · Paper page
  60. Y. Lin , Z. Liu , M. Sun , Y. Liu , and X. Zhu . 2015. Learning entity and relation embeddings for knowledge graph completion . In Proc. of AAAI. Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In Proc. of AAAI.
  61. 10.14778/2212351.2212354 · DOI
  62. C. Lu , P. Laublet , and M. Stankovic . 2016. Travel attractions recommendation with knowledge graphs . In Proc. of EKAW. C. Lu, P. Laublet, and M. Stankovic. 2016. Travel attractions recommendation with knowledge graphs. In Proc. of EKAW.
  63. G. Malewicz , M. H. Austern , A. J. C. Bik , J. C. Dehnert , I. Horn , N. Leiser , and G. Czajkowski . 2010. Pregel: A system for large-scale graph processing . In Proc. of SIGMOD. G. Malewicz, M. H. Austern, A. J. C. Bik, J. C. Dehnert, I. Horn, N. Leiser, and G. Czajkowski. 2010. Pregel: A system for large-scale graph processing. In Proc. of SIGMOD.
  64. J. McCarthy . 1993 . Notes on formalizing context . In Proc. of IJCAI. J. McCarthy. 1993. Notes on formalizing context. In Proc. of IJCAI.
  65. N. Mihindukulasooriya , M. Rashid , G. Rizzo , R. García-Castro , Ó. Corcho, and M. Torchiano . 2018. RDF shape induction using knowledge base profiling . In Proc. of SAC. N. Mihindukulasooriya, M. Rashid, G. Rizzo, R. García-Castro, Ó. Corcho, and M. Torchiano. 2018. RDF shape induction using knowledge base profiling. In Proc. of SAC.
  66. J . J. Miller. 2013 . Graph database applications and concepts with Neo4j. In Proc. of SAIC. J. J. Miller. 2013. Graph database applications and concepts with Neo4j. In Proc. of SAIC.
  67. F. Monti , D. Boscaini , J. Masci , E. Rodolà , J. Svoboda , and M. M. Bronstein . 2017. Geometric deep learning on graphs and manifolds using mixture model CNNs . In Proc. of CVPR. F. Monti, D. Boscaini, J. Masci, E. Rodolà, J. Svoboda, and M. M. Bronstein. 2017. Geometric deep learning on graphs and manifolds using mixture model CNNs. In Proc. of CVPR.
  68. B. Motik , R. Shearer , and I. Horrocks . 2009. Hypertableau reasoning for description logics . J. Artif. Intell. Res. 36 ( 2009 ). B. Motik, R. Shearer, and I. Horrocks. 2009. Hypertableau reasoning for description logics. J. Artif. Intell. Res. 36 (2009).
  69. R. Navigli and S. P. Ponzetto . 2012. BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network . AI J. 193 ( 2012 ). R. Navigli and S. P. Ponzetto. 2012. BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. AI J. 193 (2012).
  70. V. Nguyen , O. Bodenreider , and A. Sheth . 2014. Don’t like RDF reification?: Making statements about statements using singleton property . In Proc. of WWW. V. Nguyen, O. Bodenreider, and A. Sheth. 2014. Don’t like RDF reification?: Making statements about statements using singleton property. In Proc. of WWW.
  71. M. Nickel , L. Rosasco , and T. A. Poggio . 2016. Holographic embeddings of knowledge graphs . In Proc. of AAAI. M. Nickel, L. Rosasco, and T. A. Poggio. 2016. Holographic embeddings of knowledge graphs. In Proc. of AAAI.
  72. M. Nickel and V. Tresp . 2013. Tensor factorization for multi-relational learning . In Proc. of ECML-PKDD. M. Nickel and V. Tresp. 2013. Tensor factorization for multi-relational learning. In Proc. of ECML-PKDD.
  73. I. Nonaka and H. Takeuchi. 1995. The Knowledge-Creating Company. Oxford University. I. Nonaka and H. Takeuchi. 1995. The Knowledge-Creating Company. Oxford University. · DOI · Paper page
  74. 10.1145/3329781.3332266 · DOI · Paper page
  75. L. Page , S. Brin , R. Motwani , and T. Winograd . 1999 . The PageRank Citation Ranking: Bringing Order to the Web . Technical Report 1999-66. Stanford InfoLab. L. Page, S. Brin, R. Motwani, and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report 1999-66. Stanford InfoLab.
  76. J. Z. Pan G. Vetere J. M. Gómez-Pérez and H. Wu (Eds.). 2017. Exploiting Linked Data and Knowledge Graphs in Large Organisations. Springer. J. Z. Pan G. Vetere J. M. Gómez-Pérez and H. Wu (Eds.). 2017. Exploiting Linked Data and Knowledge Graphs in Large Organisations. Springer. · DOI
  77. N. Park , A. Kan , X. L. Dong , T. Zhao , and C. Faloutsos . 2019. Estimating node importance in knowledge graphs using graph neural networks . In Proc. of SIGKDD. N. Park, A. Kan, X. L. Dong, T. Zhao, and C. Faloutsos. 2019. Estimating node importance in knowledge graphs using graph neural networks. In Proc. of SIGKDD.
  78. N. Park , A. Kan , X. L. Dong , T. Zhao , and C. Faloutsos . 2020. MultiImport: Inferring node importance in a knowledge graph from multiple input signals . In Proc. of SIGKDD. N. Park, A. Kan, X. L. Dong, T. Zhao, and C. Faloutsos. 2020. MultiImport: Inferring node importance in a knowledge graph from multiple input signals. In Proc. of SIGKDD.
  79. H. Paulheim . 2017. Knowledge graph refinement: A survey of approaches and evaluation methods. Seman. Web J. 8, 3 ( 2017 ). H. Paulheim. 2017. Knowledge graph refinement: A survey of approaches and evaluation methods. Seman. Web J. 8, 3 (2017).
  80. T. Pellissier Tanon , D. Stepanova , S. Razniewski , P. Mirza , and G. Weikum . 2017. Completeness-aware rule learning from knowledge graphs . In Proc. of ISWC. T. Pellissier Tanon, D. Stepanova, S. Razniewski, P. Mirza, and G. Weikum. 2017. Completeness-aware rule learning from knowledge graphs. In Proc. of ISWC.
  81. J. Pennington , R. Socher , and C. Manning . 2014. GloVe: Global vectors for word representation . In Proc. of EMNLP. J. Pennington, R. Socher, and C. Manning. 2014. GloVe: Global vectors for word representation. In Proc. of EMNLP.
  82. A. Piscopo , L. Kaffee , C. Phethean , and E. Simperl . 2017. Provenance information in a collaborative knowledge graph: An evaluation of Wikidata external references . In Proc. of ISWC. A. Piscopo, L. Kaffee, C. Phethean, and E. Simperl. 2017. Provenance information in a collaborative knowledge graph: An evaluation of Wikidata external references. In Proc. of ISWC.
  83. E. Prud’hommeaux , J. E. Labra Gayo , and H. Solbrig . 2014. Shape expressions: An RDF validation and transformation language . In Proc. of SEMANTICS. E. Prud’hommeaux, J. E. Labra Gayo, and H. Solbrig. 2014. Shape expressions: An RDF validation and transformation language. In Proc. of SEMANTICS.
  84. J. Pujara , H. Miao , L. Getoor , and W. W. Cohen . 2013. Knowledge graph identification . In Proc. of ISWC. J. Pujara, H. Miao, L. Getoor, and W. W. Cohen. 2013. Knowledge graph identification. In Proc. of ISWC.
  85. S. Rabanser O. Shchur and S. Günnemann. 2017. Introduction to tensor decompositions and their applications in machine learning. CoRR abs/1711.10781 (2017). S. Rabanser O. Shchur and S. Günnemann. 2017. Introduction to tensor decompositions and their applications in machine learning. CoRR abs/1711.10781 (2017). · arXiv
  86. P. Ristoski and H. Paulheim . 2016. RDF2Vec: RDF graph embeddings for data mining . In Proc. of ISWC. P. Ristoski and H. Paulheim. 2016. RDF2Vec: RDF graph embeddings for data mining. In Proc. of ISWC.
  87. G. Rizzo , C. d’ Amato , N. Fanizzi , and F. Esposito . 2017. Terminological cluster trees for disjointness axiom discovery . In Proc. of ESWC. G. Rizzo, C. d’Amato, N. Fanizzi, and F. Esposito. 2017. Terminological cluster trees for disjointness axiom discovery. In Proc. of ESWC.
  88. T. Rocktäschel and S. Riedel . 2017. End-to-end differentiable proving . In Proc. of NIPS. T. Rocktäschel and S. Riedel. 2017. End-to-end differentiable proving. In Proc. of NIPS.
  89. M. A. Rodriguez . 2015 . The Gremlin graph traversal machine and language . In Proc. of DBPL. M. A. Rodriguez. 2015. The Gremlin graph traversal machine and language. In Proc. of DBPL.
  90. S. Rudolph , M. Krötzsch , and P. Hitzler . 2008. Description logic reasoning with decision diagrams: Compiling SHIQ to disjunctive datalog . In Proc. of ISWC. S. Rudolph, M. Krötzsch, and P. Hitzler. 2008. Description logic reasoning with decision diagrams: Compiling SHIQ to disjunctive datalog. In Proc. of ISWC.
  91. A. Rula , M. Palmonari , A. Harth , S. Stadtmüller , and A. Maurino . 2012. On the diversity and availability of temporal information in linked open data . In Proc. of ISWC. A. Rula, M. Palmonari, A. Harth, S. Stadtmüller, and A. Maurino. 2012. On the diversity and availability of temporal information in linked open data. In Proc. of ISWC.
  92. A. Rula , M. Palmonari , S. Rubinacci , A. Ngonga Ngomo , J. Lehmann , A. Maurino , and D. Esteves . 2019. TISCO: Temporal scoping of facts . J. Web Seman. 54 ( 2019 ). A. Rula, M. Palmonari, S. Rubinacci, A. Ngonga Ngomo, J. Lehmann, A. Maurino, and D. Esteves. 2019. TISCO: Temporal scoping of facts. J. Web Seman. 54 (2019).
  93. A. Sadeghian , M. Armandpour , P. Ding , and P. Wang . 2019. DRUM: End-to-end differentiable rule mining on knowledge graphs . In Proc. of NIPS. A. Sadeghian, M. Armandpour, P. Ding, and P. Wang. 2019. DRUM: End-to-end differentiable rule mining on knowledge graphs. In Proc. of NIPS.
  94. 10.1109/TNN.2008.2005605 · DOI · Paper page
  95. 10.1037/e436252004-001 · DOI
  96. M. Schneider and G. Sutcliffe . 2011. Reasoning in the OWL 2 full ontology language using first-order automated theorem proving . In Proc. of CADE. M. Schneider and G. Sutcliffe. 2011. Reasoning in the OWL 2 full ontology language using first-order automated theorem proving. In Proc. of CADE.
  97. C. Schuetz , L. Bozzato , B. Neumayr , M. Schrefl , and L. Serafini . 2021 . Knowledge graph OLAP: A multidimensional model and query operations for contextualized knowledge graphs. Seman. Web J. (2021). (Accepted; In Press) . C. Schuetz, L. Bozzato, B. Neumayr, M. Schrefl, and L. Serafini. 2021. Knowledge graph OLAP: A multidimensional model and query operations for contextualized knowledge graphs. Seman. Web J. (2021). (Accepted; In Press).
  98. P. Seifer , J. Härtel , M. Leinberger , R. Lämmel , and S. Staab . 2019. Empirical study on the usage of graph query languages in open source Java projects . In Proc. of SLE. P. Seifer, J. Härtel, M. Leinberger, R. Lämmel, and S. Staab. 2019. Empirical study on the usage of graph query languages in open source Java projects. In Proc. of SLE.
  99. R. Socher , D. Chen , C. D. Manning , and A. Ng . 2013. Reasoning with neural tensor networks for knowledge base completion . In Proc. of NIPS. R. Socher, D. Chen, C. D. Manning, and A. Ng. 2013. Reasoning with neural tensor networks for knowledge base completion. In Proc. of NIPS.
  100. 10.1109/72.572108 · DOI · Paper page
  101. 10.1007/978-3-642-05082-4_12 · DOI
  102. P. Stutz , D. Strebel , and A. Bernstein . 2016. Signal/Collect12 . Seman. Web J. 7 , 2 ( 2016 ). P. Stutz, D. Strebel, and A. Bernstein. 2016. Signal/Collect12. Seman. Web J. 7, 2 (2016).
  103. F. M. Suchanek , J. Lajus , A. Boschin , and G. Weikum . 2019. Knowledge representation and rule mining in entity-centric knowledge bases . In Proc. of RWeb. F. M. Suchanek, J. Lajus, A. Boschin, and G. Weikum. 2019. Knowledge representation and rule mining in entity-centric knowledge bases. In Proc. of RWeb.
  104. Yizhou Sun and Jiawei Han . 2012. Mining Heterogeneous Information Networks: Principles and Methodologies . Morgan & Claypool Publishers . Yizhou Sun and Jiawei Han. 2012. Mining Heterogeneous Information Networks: Principles and Methodologies. Morgan & Claypool Publishers.
  105. 10.14778/3402707.3402736 · DOI · Paper page
  106. G. Töpper , M. Knuth , and H. Sack . 2012. DBpedia ontology enrichment for inconsistency detection . In Proc. of I-SEMANTICS. G. Töpper, M. Knuth, and H. Sack. 2012. DBpedia ontology enrichment for inconsistency detection. In Proc. of I-SEMANTICS.
  107. O. Udrea , D. Reforgiato Recupero , and V. S. Subrahmanian . 2010 . Annotated RDF. ACM Trans. Comput. Log. 11 , 2 ( 2010 ). O. Udrea, D. Reforgiato Recupero, and V. S. Subrahmanian. 2010. Annotated RDF. ACM Trans. Comput. Log. 11, 2 (2010).
  108. S. Vashishth , P. Jain , and P. Talukdar . 2018. CESI: Canonicalizing open knowledge bases using embeddings and side information . In Proc. of WWW. S. Vashishth, P. Jain, and P. Talukdar. 2018. CESI: Canonicalizing open knowledge bases using embeddings and side information. In Proc. of WWW.
  109. 10.1016/j.websem.2015.07.001 · DOI
  110. 10.1145/2629489 · DOI · Paper page
  111. M. Wang , R. Wang , J. Liu , Y. Chen , L. Zhang , and G. Qi . 2018 . Towards empty answers in SPARQL: Approximating querying with RDF embedding. In Proc. of ISWC. M. Wang, R. Wang, J. Liu, Y. Chen, L. Zhang, and G. Qi. 2018. Towards empty answers in SPARQL: Approximating querying with RDF embedding. In Proc. of ISWC.
  112. Q. Wang , B. Wang , and L. Guo . 2015. Knowledge base completion using embeddings and rules . In Proc. of IJCAI. Q. Wang, B. Wang, and L. Guo. 2015. Knowledge base completion using embeddings and rules. In Proc. of IJCAI.
  113. X. Wang , H. Ji , C. Shi , B. Wang , Y. Ye , P. Cui , and P. S. Yu . 2019. Heterogeneous graph attention network . In Proc. of WWW. X. Wang, H. Ji, C. Shi, B. Wang, Y. Ye, P. Cui, and P. S. Yu. 2019. Heterogeneous graph attention network. In Proc. of WWW.
  114. X. Wang and S. Yang . 2019. A tutorial and survey on fault knowledge graph . In Proc. of CyberDI/CyberLife. 256–271 . X. Wang and S. Yang. 2019. A tutorial and survey on fault knowledge graph. In Proc. of CyberDI/CyberLife. 256–271.
  115. Marcin Wylot , Manfred Hauswirth , Philippe Cudré-Mauroux , and Sherif Sakr . 2018. RDF data storage and query processing schemes: A survey. ACM Comput. Surv. 51, 4 ( 2018 ). Marcin Wylot, Manfred Hauswirth, Philippe Cudré-Mauroux, and Sherif Sakr. 2018. RDF data storage and query processing schemes: A survey. ACM Comput. Surv. 51, 4 (2018).
  116. G. Xiao , L. Ding , G. Cogrel , and D. Calvanese . 2019. Virtual knowledge graphs: An overview of systems and use cases . Data Int. 1 , 3 ( 2019 ), 201–223. G. Xiao, L. Ding, G. Cogrel, and D. Calvanese. 2019. Virtual knowledge graphs: An overview of systems and use cases. Data Int. 1, 3 (2019), 201–223.
  117. R. Xin , J. Gonzalez , M. J. Franklin , and I. Stoica . 2013. GraphX: A resilient distributed graph system on Spark . In Proc. of GRADES. R. Xin, J. Gonzalez, M. J. Franklin, and I. Stoica. 2013. GraphX: A resilient distributed graph system on Spark. In Proc. of GRADES.
  118. R. Xin , J. Rosen , M. Zaharia , M. J. Franklin , S. Shenker , and I. Stoica . 2013. Shark: SQL and rich analytics at scale . In Proc. of SIGMOD. R. Xin, J. Rosen, M. Zaharia, M. J. Franklin, S. Shenker, and I. Stoica. 2013. Shark: SQL and rich analytics at scale. In Proc. of SIGMOD.
  119. K. Xu , W. Hu , J. Leskovec , and S. Jegelka . 2019. How powerful are graph neural networks? In Proc. of ICLR. K. Xu, W. Hu, J. Leskovec, and S. Jegelka. 2019. How powerful are graph neural networks? In Proc. of ICLR.
  120. 10.1007/s11704-016-5228-9 · DOI
  121. F. Yang , Z. Yang , and W. W. Cohen . 2017. Differentiable learning of logical rules for knowledge base reasoning . In Proc. of NIPS. F. Yang, Z. Yang, and W. W. Cohen. 2017. Differentiable learning of logical rules for knowledge base reasoning. In Proc. of NIPS.
  122. L. Yang , Z. Xiao , W. Jiang , Y. Wei , Y. Hu , and H. Wang . 2020. Dynamic heterogeneous graph embedding using hierarchical attentions . In Proc. of ECIR. L. Yang, Z. Xiao, W. Jiang, Y. Wei, Y. Hu, and H. Wang. 2020. Dynamic heterogeneous graph embedding using hierarchical attentions. In Proc. of ECIR.
  123. F. Zhang , N. J. Yuan , D. Lian , X. Xie , and W. Ma . 2016. Collaborative knowledge base embedding for recommender systems . In Proc. of SIGKDD. F. Zhang, N. J. Yuan, D. Lian, X. Xie, and W. Ma. 2016. Collaborative knowledge base embedding for recommender systems. In Proc. of SIGKDD.
  124. A. Zimmermann , N. Lopes , A. Polleres , and U. Straccia . 2012. A general framework for representing, reasoning and querying with annotated semantic web data . J. Web Seman. 12 ( Mar. 2012 ). A. Zimmermann, N. Lopes, A. Polleres, and U. Straccia. 2012. A general framework for representing, reasoning and querying with annotated semantic web data. J. Web Seman. 12 (Mar. 2012).

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Metadata

Authors
Hogan, A., Blomqvist, E., Cochez, M., D’amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A.-C. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A.
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2021-07-02
Volume
54
Issue
4
Pages
1-37
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3447772
OpenAlex
W3010336026
Cited by
1,904
References
22
FWCI
71.81
Type
journal article
Language
en
Open access
bronze
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Funders
  • Marie Skłodowska-Curie (860801)
  • ANID – Millennium Science Initiative Program (ICN17_002)
  • MOUSSE ERC (726487)
  • Fondecyt (1181896)
  • German Research Foundation (STA 572/18-1)
  • European Union’s Horizon 2020 research and innovation programme (731601)
  • Spanish Ministry of Economy and Competitiveness (TIN2017-88877-R)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Knowledge Graphs. https://app.cochat.ai/papers/10.1145/3447772
The paper
Hogan, A., Blomqvist, E., Cochez, M., D’amato, C., Melo, G. D., Gutierrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngomo, A.-C. N., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2022). Knowledge Graphs. ACM Computing Surveys. https://doi.org/10.1145/3447772

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.