Document Open Access Logo

Reasoning in Knowledge Graphs (Invited Paper)

Authors Ricardo Guimarães , Ana Ozaki

PDF
Thumbnail PDF

File

OASIcs.AIB.2022.2.pdf
  • Filesize: 0.98 MB
  • 31 pages

Document Identifiers

Author Details

Ricardo Guimarães
  • University of Bergen, Norway
Ana Ozaki
  • University of Bergen, Norway

Funding

  • Guimarães, Ricardo: The first author is supported by the ERC project "Lossy Preprocessing" (LOPRE), grant number 819416, led by Prof. Saket Saurabh.
  • Ozaki, Ana: The second author is supported by the NFR project "Learning Description Logic Ontologies", grant number 316022.

Acknowledgements

Part of this work has been done in the context of CEDAS (Center for Data Science, University of Bergen, Norway).

Cite AsGet BibTex

Ricardo Guimarães and Ana Ozaki. Reasoning in Knowledge Graphs (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 2:1-2:31, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/OASIcs.AIB.2022.2

Abstract

Knowledge Graphs (KGs) are becoming increasingly popular in the industry and academia. They can be represented as labelled graphs conveying structured knowledge in a domain of interest, where nodes and edges are enriched with metaknowledge such as time validity, provenance, language, among others. Once the data is structured as a labelled graph one can apply reasoning techniques to extract relevant and insightful information. We provide an overview of deductive and inductive reasoning approaches for reasoning in KGs.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Knowledge representation and reasoning
Keywords
  • Knowledge Graphs
  • Description Logics
  • Knowledge Graph Embeddings

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. Ralph Abboud, İsmail İlkan Ceylan, Thomas Lukasiewicz, and Tommaso Salvatori. Boxe: A box embedding model for knowledge base completion. In Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020. URL: https://proceedings.neurips.cc/paper/2020/hash/6dbbe6abe5f14af882ff977fc3f35501-Abstract.html.
  2. Serge Abiteboul, Richard Hull, and Victor Vianu. Foundations of Databases. Addison Wesley, 1994. Google Scholar
  3. Serge Abiteboul and Victor Vianu. Datalog extensions for database queries and updates. Journal of Computer and System Sciences, 43:62-124, 1991. Google Scholar
  4. Rakesh Agrawal, Tomasz Imieliński, and Arun Swami. Mining association rules between sets of items in large databases. Special Interest Group on Management Of Data SIGMOD, 22(2):207-216, June 1993. Google Scholar
  5. Shqiponja Ahmetaj, Robert David, Magdalena Ortiz, Axel Polleres, Bojken Shehu, and Mantas Šimkus. Reasoning about explanations for non-validation in SHACL. In Proceedings of the Eighteenth International Conference on Principles of Knowledge Representation and Reasoning. International Joint Conferences on Artificial Intelligence Organization, September 2021. URL: https://doi.org/10.24963/kr.2021/2.
  6. Medina Andresel, Julien Corman, Magdalena Ortiz, Juan L. Reutter, Ognjen Savkovic, and Mantas Simkus. Stable Model Semantics for Recursive SHACL. In Proceedings of The Web Conference 2020. ACM, April 2020. URL: https://doi.org/10.1145/3366423.3380229.
  7. Franz Baader, Ian Horrocks, Carsten Lutz, and Uli Sattler. An Introduction to Description Logic. Cambridge University Press, 2017. Google Scholar
  8. Franz Baader, Francesco Kriegel, Adrian Nuradiansyah, and Rafael Peñaloza. Making repairs in description logics more gentle. In Michael Thielscher, Francesca Toni, and Frank Wolter, editors, Principles of Knowledge Representation and Reasoning: Proceedings of the Sixteenth International Conference, KR 2018, Tempe, Arizona, 30 October - 2 November 2018, pages 319-328. AAAI Press, 2018. URL: https://aaai.org/ocs/index.php/KR/KR18/paper/view/18056.
  9. Franz Baader and Rafael Peñaloza. Axiom pinpointing in general tableaux. J. Log. Comput., 20(1):5-34, 2010. URL: https://doi.org/10.1093/logcom/exn058.
  10. Luigi Bellomarini, Eleonora Laurenza, Emanuel Sallinger, and Evgeny Sherkhonov. Reasoning under uncertainty in knowledge graphs. In Rules and Reasoning, pages 131-139. Springer International Publishing, 2020. URL: https://doi.org/10.1007/978-3-030-57977-7_9.
  11. Luigi Bellomarini, Markus Nissl, and Emanuel Sallinger. Monotonic aggregation for temporal datalog. In Ahmet Soylu, Alireza Tamaddoni-Nezhad, Nikolay Nikolov, Ioan Toma, Anna Fensel, and Joost Vennekens, editors, Proceedings of the 15th International Rule Challenge, 7th Industry Track, and 5th Doctoral Consortium @ RuleML+RR 2021 co-located with 17th Reasoning Web Summer School (RW 2021) and 13th DecisionCAMP 2021 as part of Declarative AI 2021, Leuven, Belgium (virtual due to Covid-19 pandemic), 8 - 15 September, 2021, volume 2956 of CEUR Workshop Proceedings. CEUR-WS.org, 2021. URL: http://ceur-ws.org/Vol-2956/paper30.pdf.
  12. Luigi Bellomarini, Emanuel Sallinger, and Georg Gottlob. The vadalog system. Proceedings of the VLDB Endowment, 11(9):975-987, May 2018. URL: https://doi.org/10.14778/3213880.3213888.
  13. Meghyn Bienvenu. Complexity of abduction in the EL family of lightweight description logics. In Gerhard Brewka and Jérôme Lang, editors, Principles of Knowledge Representation and Reasoning: Proceedings of the Eleventh International Conference, KR 2008, Sydney, Australia, September 16-19, 2008, pages 220-230. AAAI Press, 2008. URL: http://www.aaai.org/Library/KR/2008/kr08-022.php.
  14. Meghyn Bienvenu, Balder ten Cate, Carsten Lutz, and Frank Wolter. Ontology-based data access: A study through disjunctive datalog, CSP, and MMSNP. In Richard Hull and Wenfei Fan, editors, Proceedings of the 32nd Symposium on Principles of Database Systems (PODS'13), pages 213-224. ACM, 2013. Google Scholar
  15. Bart Bogaerts and Maxime Jakubowski. Fixpoint Semantics for Recursive SHACL. Electronic Proceedings in Theoretical Computer Science, 345:41-47, September 2021. URL: https://doi.org/10.4204/eptcs.345.14.
  16. Bart Bogaerts, Maxime Jakubowski, and Jan Van den Bussche. SHACL: A Description Logic in Disguise. In Proceedings of the 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning (BNAIC/BENELEARN 2021), August 2021. URL: http://arxiv.org/abs/2108.06096.
  17. Iovka Boneva, José Emilio Labra Gayo, and Eric G. Prud'hommeaux. Semantics and validation of shapes schemas for RDF. In Claudia d'Amato, Miriam Fernández, Valentina A. M. Tamma, Freddy Lécué, Philippe Cudré-Mauroux, Juan F. Sequeda, Christoph Lange, and Jeff Heflin, editors, The Semantic Web - ISWC 2017 - 16th International Semantic Web Conference, Vienna, Austria, October 21-25, 2017, Proceedings, Part I, volume 10587 of Lecture Notes in Computer Science, pages 104-120. Springer, 2017. URL: https://doi.org/10.1007/978-3-319-68288-4_7.
  18. Daniel Borchmann. Learning terminological knowledge with high confidence from erroneous data. PhD thesis, Higher School of Economics, 2014. Google Scholar
  19. Daniel Borchmann and Felix Distel. Mining of EL-GCIs. In The 11th IEEE International Conference on Data Mining Workshops, Vancouver, Canada, 2011. Google Scholar
  20. Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi-relational data. In Advances in neural information processing systems, pages 2787-2795, 2013. Google Scholar
  21. Camille Bourgaux and Ana Ozaki. Querying attributed dl-lite ontologies using provenance semirings. In AAAI, pages 2719-2726. AAAI Press, 2019. Google Scholar
  22. Camille Bourgaux, Ana Ozaki, and Jeff Z. Pan. Geometric models for (temporally) attributed description logics. In Martin Homola, Vladislav Ryzhikov, and Renate A. Schmidt, editors, Proceedings of the 34th International Workshop on Description Logics (DL 2021) part of Bratislava Knowledge September (BAKS 2021), Bratislava, Slovakia, September 19th to 22nd, 2021, volume 2954 of CEUR Workshop Proceedings. CEUR-WS.org, 2021. URL: http://ceur-ws.org/Vol-2954/paper-7.pdf.
  23. Sebastian Brandt, Elem Güzel Kalayci, Roman Kontchakov, Vladislav Ryzhikov, Guohui Xiao, and Michael Zakharyaschev. Ontology-based data access with a horn fragment of metric temporal logic. In Satinder P. Singh and Shaul Markovitch, editors, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA, pages 1070-1076. AAAI Press, 2017. URL: http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14881.
  24. F. Buccafurri, N. Leone, and P. Rullo. Enhancing Disjunctive Datalog by constraints. IEEE Transactions on Knowledge and Data Engineering, 12(5):845-860, 2000. URL: https://doi.org/10.1109/69.877512.
  25. Ling Cai, Krzysztof Janowicz, Bo Yan, Rui Zhu, and Gengchen Mai. Time in a Box. In Proceedings of the 11th on Knowledge Capture Conference. ACM, December 2021. URL: https://doi.org/10.1145/3460210.3493566.
  26. Andrea Calì, Georg Gottlob, and Thomas Lukasiewicz. Datalog±. In Proceedings of the 12th International Conference on Database Theory - ICDT quotesingle09. ACM Press, 2009. URL: https://doi.org/10.1145/1514894.1514897.
  27. Andrea Calì, Georg Gottlob, and Thomas Lukasiewicz. A general datalog-based framework for tractable query answering over ontologies. Journal of Web Semantics, 14:57-83, July 2012. URL: https://doi.org/10.1016/j.websem.2012.03.001.
  28. Andrea Calì, Georg Gottlob, and Andreas Pieris. Query answering under non-guarded rules in Datalog+/-. In Pascal Hitzler and Thomas Lukasiewicz, editors, Proceedings of the 4th International Conference on Web Reasoning and Rule Systems (RR 2010), volume 6333 of LNCS, pages 1-17. Springer, 2010. Google Scholar
  29. David Carral, Irina Dragoste, Larry González, Ceriel Jacobs, Markus Krötzsch, and Jacopo Urbani. VLog: A rule engine for knowledge graphs. In Lecture Notes in Computer Science, pages 19-35. Springer International Publishing, 2019. URL: https://doi.org/10.1007/978-3-030-30796-7_2.
  30. S. Ceri, G. Gottlob, and L. Tanca. What you always wanted to know about datalog (and never dared to ask). IEEE Transactions on Knowledge and Data Engineering, 1(1):146-166, March 1989. URL: https://doi.org/10.1109/69.43410.
  31. Melisachew Wudage Chekol. Tensor decomposition for link prediction in temporal knowledge graphs. In Proceedings of the 11th on Knowledge Capture Conference. ACM, December 2021. URL: https://doi.org/10.1145/3460210.3493558.
  32. Melisachew Wudage Chekol, Giuseppe Pirrò, Joerg Schoenfisch, and Heiner Stuckenschmidt. Marrying uncertainty and time in knowledge graphs. In Satinder P. Singh and Shaul Markovitch, editors, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA, pages 88-94. AAAI Press, 2017. URL: http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14730.
  33. Shuo Chen, Lin Qiao, Biqi Liu, Jue Bo, Yuanning Cui, and Jing Li. Knowledge Graph Embedding Based on Hyperplane and Quantitative Credibility. In Machine Learning and Intelligent Communications, pages 583-594. Springer International Publishing, 2019. URL: https://doi.org/10.1007/978-3-030-32388-2_50.
  34. Jan Chomicki and Tomasz Imielinski. Temporal deductive databases and infinite objects. In Chris Edmondson-Yurkanan and Mihalis Yannakakis, editors, Proceedings of the Seventh ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, March 21-23, 1988, Austin, Texas, USA, pages 61-73. ACM, 1988. URL: https://doi.org/10.1145/308386.308416.
  35. Julien Corman, Juan L. Reutter, and Ognjen Savković. Semantics and validation of recursive SHACL. In Lecture Notes in Computer Science, pages 318-336. Springer International Publishing, 2018. URL: https://doi.org/10.1007/978-3-030-00671-6_19.
  36. Yuanfei Dai, Shiping Wang, Neal N. Xiong, and Wenzhong Guo. A survey on knowledge graph embedding: Approaches, applications and benchmarks. Electronics, 9(5):750, May 2020. URL: https://doi.org/10.3390/electronics9050750.
  37. Claudia d'Amato, Nicola Flavio Quatraro, and Nicola Fanizzi. Injecting Background Knowledge into Embedding Models for Predictive Tasks on Knowledge Graphs. In The Semantic Web, pages 441-457. Springer International Publishing, 2021. URL: https://doi.org/10.1007/978-3-030-77385-4_26.
  38. Ariyam Das, Sahil M. Gandhi, and Carlo Zaniolo. ASTRO: A datalog system for advanced stream reasoning. In Alfredo Cuzzocrea, James Allan, Norman W. Paton, Divesh Srivastava, Rakesh Agrawal, Andrei Z. Broder, Mohammed J. Zaki, K. Selçuk Candan, Alexandros Labrinidis, Assaf Schuster, and Haixun Wang, editors, Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22-26, 2018, pages 1863-1866. ACM, 2018. URL: https://doi.org/10.1145/3269206.3269223.
  39. Shib Sankar Dasgupta, Swayambhu Nath Ray, and Partha P. Talukdar. HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding. In Ellen Riloff, David Chiang, Julia Hockenmaier, and Jun'ichi Tsujii, editors, Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, pages 2001-2011. Association for Computational Linguistics, 2018. URL: https://doi.org/10.18653/v1/d18-1225.
  40. Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. Convolutional 2d knowledge graph embeddings. In Sheila A. McIlraith and Kilian Q. Weinberger, editors, Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, pages 1811-1818. AAAI Press, 2018. URL: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17366.
  41. Boyang Ding, Quan Wang, Bin Wang, and Li Guo. Improving knowledge graph embedding using simple constraints. In Iryna Gurevych and Yusuke Miyao, editors, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers, pages 110-121. Association for Computational Linguistics, 2018. URL: https://doi.org/10.18653/v1/P18-1011.
  42. Felix Distel. Learning description logic knowledge bases from data using methods from formal concept analysis. PhD thesis, Dresden University of Technology, 2011. Google Scholar
  43. Francesco M. Donini, Maurizio Lenzerini, Daniele Nardi, and Andrea Schaerf. AL-log: Integrating datalog and description logics. Journal of Intelligent and Cooperative Information Systems, 10(3):227-252, 1998. Google Scholar
  44. Thomas Eiter, Georg Gottlob, and Heikki Mannila. Disjunctive datalog. ACM Transactions on Database Systems, 22(3):364-418, September 1997. URL: https://doi.org/10.1145/261124.261126.
  45. Corinna Elsenbroich, Oliver Kutz, and Ulrike Sattler. A case for abductive reasoning over ontologies. In Bernardo Cuenca Grau, Pascal Hitzler, Conor Shankey, and Evan Wallace, editors, Proceedings of the OWLED*06 Workshop on OWL: Experiences and Directions, Athens, Georgia, USA, November 10-11, 2006, volume 216 of CEUR Workshop Proceedings. CEUR-WS.org, 2006. URL: http://ceur-ws.org/Vol-216/submission_25.pdf.
  46. Bahare Fatemi, Siamak Ravanbakhsh, and David Poole. Improved knowledge graph embedding using background taxonomic information. In AAAI, pages 3526-3533. AAAI Press, 2019. Google Scholar
  47. Mónica Figuera, Philipp D. Rohde, and Maria-Esther Vidal. Trav-SHACL: Efficiently validating networks of SHACL constraints. In Proceedings of the Web Conference 2021. ACM, April 2021. URL: https://doi.org/10.1145/3442381.3449877.
  48. Luis Galárraga, Christina Teflioudi, Katja Hose, and Fabian M. Suchanek. Fast rule mining in ontological knowledge bases with AMIE+. VLDB J., 24(6):707-730, 2015. Google Scholar
  49. Bernhard Ganter and Rudolph Wille. Formal Concept Analysis: Mathematical Foundations. Springer, 1997. Google Scholar
  50. Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, and Pascal Poupart. Diachronic Embedding for Temporal Knowledge Graph Completion. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04):3988-3995, April 2020. URL: https://doi.org/10.1609/aaai.v34i04.5815.
  51. Georg Gottlob, Erich Grädel, and Helmut Veith. Datalog LITE: a deductive query language with linear time model checking. ACM Trans. Comput. Log., 3(1):42-79, 2002. URL: https://doi.org/10.1145/504077.504079.
  52. Ricardo Guimarães, Ana Ozaki, Cosimo Persia, and Baris Sertkaya. Mining EL bases with adaptable role depth. In AAAI, pages 6367-6374. AAAI Press, 2021. Google Scholar
  53. Víctor Gutiérrez-Basulto and Steven Schockaert. From knowledge graph embedding to ontology embedding? an analysis of the compatibility between vector space representations and rules. In Proceedings of KR, 2018. Google Scholar
  54. Tom Hanika, Maximilian Marx, and Gerd Stumme. Discovering implicational knowledge in wikidata. In Diana Cristea, Florence Le Ber, and Baris Sertkaya, editors, ICFCA, volume 11511 of Lecture Notes in Computer Science, pages 315-323. Springer, 2019. Google Scholar
  55. Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutiérrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan F. Sequeda, Steffen Staab, and Antoine Zimmermann. Knowledge graphs. ACM Comput. Surv., 54(4):71:1-71:37, 2021. Google Scholar
  56. Matthew Horridge. Justification based explanation in ontologies. PhD thesis, University of Manchester, 2011. Google Scholar
  57. Ian Horrocks, Peter F. Patel-Schneider, Harold Boley, Said Tabet, Benjamin N. Grosof, and Mike Dean. SWRL: A Semantic Web Rule Language. W3C Member Submission, 21 May 2004. Available at URL: http://www.w3.org/Submission/SWRL/.
  58. Nitisha Jain, Trung-Kien Tran, Mohamed H. Gad-Elrab, and Daria Stepanova. Improving Knowledge Graph Embeddings with Ontological Reasoning. In Andreas Hotho, Eva Blomqvist, Stefan Dietze, Achille Fokoue, Ying Ding, Payam M. Barnaghi, Armin Haller, Mauro Dragoni, and Harith Alani, editors, ISWC, volume 12922 of Lecture Notes in Computer Science, pages 410-426. Springer, 2021. Google Scholar
  59. Yevgeny Kazakov, Markus Krötzsch, and František Simančík. ELK reasoner: Architecture and evaluation. In Ian Horrocks, Mikalai Yatskevich, and Ernesto Jimenez-Ruiz, editors, Proceedings of the OWL Reasoner Evaluation Workshop 2012 (ORE'12), volume 858 of CEUR Workshop Proceedings. CEUR-WS.org, 2012. Google Scholar
  60. Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, and Pascal Poupart. Representation learning for dynamic graphs: A survey. J. Mach. Learn. Res., 21:70:1-70:73, 2020. URL: http://jmlr.org/papers/v21/19-447.html.
  61. Mayank Kejriwal, Craig A. Knoblock, and Pedro Szekely. Knowledge Graphs. The MIT Press, March 2021. URL: https://www.ebook.de/de/product/39993807/mayank_kejriwal_craig_a_knoblock_pedro_szekely_knowledge_graphs.html.
  62. Holger Knublauch and Dimitris Kontokostas, editors. Shapes Constraint Language (SHACL). W3C Recommendation, 20 July 2017. Available at URL: http://www.w3.org/TR/shacl/.
  63. Roman Kontchakov, Carsten Lutz, David Toman, Frank Wolter, and Michael Zakharyaschev. The combined approach to ontology-based data access. In Toby Walsh, editor, Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI'11), pages 2656-2661. AAAI Press/IJCAI, 2011. Google Scholar
  64. Patrick Koopmann, Warren Del-Pinto, Sophie Tourret, and Renate A. Schmidt. Signature-based abduction for expressive description logics. In Diego Calvanese, Esra Erdem, and Michael Thielscher, editors, Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning, KR 2020, Rhodes, Greece, September 12-18, 2020, pages 592-602, 2020. URL: https://doi.org/10.24963/kr.2020/59.
  65. Markus Krötzsch, Maximilian Marx, Ana Ozaki, and Veronika Thost. Attributed description logics: Reasoning on knowledge graphs. In Jérôme Lang, editor, IJCAI, pages 5309-5313. ijcai.org, 2018. Google Scholar
  66. Markus Krötzsch, Sebastian Rudolph, and Pascal Hitzler. Description logic rules. In Malik Ghallab, Constantine D. Spyropoulos, Nikos Fakotakis, and Nikos Avouris, editors, Proceedings of the 18th European Conference on Artificial Intelligence (ECAI'08), pages 80-84. IOS Press, 2008. Google Scholar
  67. Markus Krötzsch, Sebastian Rudolph, and Peter H. Schmitt. On the semantic relationship between datalog and description logics. In Pascal Hitzler and Thomas Lukasiewicz, editors, Proceedings of the 4th International Conference on Web Reasoning and Rule Systems (RR 2010), volume 6333 of LNCS, pages 88-102. Springer, 2010. Google Scholar
  68. Markus Krötzsch, Maximilian Marx, Ana Ozaki, and Veronika Thost. Attributed description logics: Ontologies for knowledge graphs. In Lecture Notes in Computer Science, pages 418-435. Springer International Publishing, 2017. URL: https://doi.org/10.1007/978-3-319-68288-4_25.
  69. Jonathan Lajus, Luis Galárraga, and Fabian M. Suchanek. Fast and exact rule mining with AMIE 3. In Andreas Harth, Sabrina Kirrane, Axel-Cyrille Ngonga Ngomo, Heiko Paulheim, Anisa Rula, Anna Lisa Gentile, Peter Haase, and Michael Cochez, editors, ESWC, volume 12123 of Lecture Notes in Computer Science, pages 36-52. Springer, 2020. Google Scholar
  70. Julien Leblay, Melisachew Wudage Chekol, and Xin Liu. Towards Temporal Knowledge Graph Embeddings with Arbitrary Time Precision. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. ACM, October 2020. URL: https://doi.org/10.1145/3340531.3412028.
  71. Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N. Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick van Kleef, Sören Auer, and Christian Bizer. DBpedia – A large-scale, multilingual knowledge base extracted from Wikipedia. Semantic Web, 6:167-195, 2015. URL: https://doi.org/10.3233/SW-140134.
  72. Martin Leinberger, Philipp Seifer, Tjitze Rienstra, Ralf Lämmel, and Steffen Staab. Deciding SHACL shape containment through description logics reasoning. In Lecture Notes in Computer Science, pages 366-383. Springer International Publishing, 2020. URL: https://doi.org/10.1007/978-3-030-62419-4_21.
  73. Nicola Leone, Carlo Allocca, Mario Alviano, Francesco Calimeri, Cristina Civili, Roberta Costabile, Alessio Fiorentino, Davide Fuscà, Stefano Germano, Giovanni Laboccetta, Bernardo Cuteri, Marco Manna, Simona Perri, Kristian Reale, Francesco Ricca, Pierfrancesco Veltri, and Jessica Zangari. Enhancing DLV for large-scale reasoning. In Logic Programming and Nonmonotonic Reasoning, pages 312-325. Springer International Publishing, 2019. URL: https://doi.org/10.1007/978-3-030-20528-7_23.
  74. Zixuan Li, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, Huawei Shen, Yuanzhuo Wang, and Xueqi Cheng. Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning. In Fernando Diaz, Chirag Shah, Torsten Suel, Pablo Castells, Rosie Jones, and Tetsuya Sakai, editors, SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021, pages 408-417. ACM, 2021. URL: https://doi.org/10.1145/3404835.3462963.
  75. Siyuan Liao, Shangsong Liang, Zaiqiao Meng, and Qiang Zhang. Learning Dynamic Embeddings for Temporal Knowledge Graphs. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. ACM, March 2021. URL: https://doi.org/10.1145/3437963.3441741.
  76. Lifan Lin and Kun She. Tensor decomposition-based temporal knowledge graph embedding. In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, November 2020. URL: https://doi.org/10.1109/ictai50040.2020.00151.
  77. Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. Learning entity and relation embeddings for knowledge graph completion. In Blai Bonet and Sven Koenig, editors, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, USA, pages 2181-2187. AAAI Press, 2015. URL: http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9571.
  78. Yu Liu, Wen Hua, Jianfeng Qu, Kexuan Xin, and Xiaofang Zhou. Temporal knowledge completion with context-aware embeddings. World Wide Web, 24(2):675-695, March 2021. URL: https://doi.org/10.1007/s11280-021-00867-6.
  79. Frank Manola and Eric Miller, editors. Resource Description Framework (RDF): Primer. W3C Recommendation, 10 February 2004. Available at URL: http://www.w3.org/TR/rdf-primer/.
  80. Vinícius Bitencourt Matos, Ricardo Ferreira Guimarães, Yuri David Santos, and Renata Wassermann. Pseudo-contractions as gentle repairs. In Carsten Lutz, Uli Sattler, Cesare Tinelli, Anni-Yasmin Turhan, and Frank Wolter, editors, Description Logic, Theory Combination, and All That - Essays Dedicated to Franz Baader on the Occasion of His 60th Birthday, volume 11560 of Lecture Notes in Computer Science, pages 385-403. Springer, 2019. URL: https://doi.org/10.1007/978-3-030-22102-7_18.
  81. Mehrnoosh Mirtaheri, Mohammad Rostami, Xiang Ren, Fred Morstatter, and Aram Galstyan. One-shot learning for temporal knowledge graphs. In Danqi Chen, Jonathan Berant, Andrew McCallum, and Sameer Singh, editors, 3rd Conference on Automated Knowledge Base Construction, AKBC 2021, Virtual, October 4-8, 2021, 2021. URL: https://doi.org/10.24432/C55K56.
  82. Yavor Nenov, Robert Piro, Boris Motik, Ian Horrocks, Zhe Wu, and Jay Banerjee. RDFox: A highly-scalable RDF store. In The Semantic Web - ISWC 2015, pages 3-20. Springer International Publishing, 2015. URL: https://doi.org/10.1007/978-3-319-25010-6_1.
  83. Runyu Ni, Zhonggui Ma, Kaihang Yu, and Xiaohan Xu. Specific Time Embedding for Temporal Knowledge Graph Completion. In 2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI∗CC). IEEE, September 2020. URL: https://doi.org/10.1109/iccicc50026.2020.9450214.
  84. W3C OWL Working Group. OWL 2 Web Ontology Language: Document Overview. W3C Recommendation, 27 october 2009. Available at URL: http://www.w3.org/TR/owl2-overview/.
  85. Ana Ozaki, Markus Krötzsch, and Sebastian Rudolph. Temporally attributed description logics. In Carsten Lutz, Uli Sattler, Cesare Tinelli, Anni-Yasmin Turhan, and Frank Wolter, editors, Description Logic, Theory Combination, and All That - Essays Dedicated to Franz Baader on the Occasion of His 60th Birthday, volume 11560 of Lecture Notes in Computer Science, pages 441-474. Springer, 2019. Google Scholar
  86. Özgür Lütfü Özçep, Mena Leemhuis, and Diedrich Wolter. Cone semantics for logics with negation. In Christian Bessiere, editor, IJCAI, pages 1820-1826. ijcai.org, 2020. Google Scholar
  87. Paolo Pareti, George Konstantinidis, and Fabio Mogavero. Satisfiability and containment of recursive SHACL. CoRR, abs/2108.13063, 2021. URL: http://arxiv.org/abs/2108.13063.
  88. Paolo Pareti, George Konstantinidis, Fabio Mogavero, and Timothy J. Norman. SHACL satisfiability and containment. In Lecture Notes in Computer Science, pages 474-493. Springer International Publishing, 2020. URL: https://doi.org/10.1007/978-3-030-62419-4_27.
  89. Bijan Parsia, Nicolas Matentzoglu, Rafael S. Gonçalves, Birte Glimm, and Andreas Steigmiller. The OWL reasoner evaluation (ORE) 2015 competition report. Journal of Automated Reasoning, 59(4):455-482, February 2017. URL: https://doi.org/10.1007/s10817-017-9406-8.
  90. Eric Prud'hommeaux and Andy Seaborne, editors. SPARQL Query Language for RDF. W3C Recommendation, 15 January 2008. Available at URL: http://www.w3.org/TR/rdf-sparql-query/.
  91. Júlia Pukancová and Martin Homola. Abductive Reasoning with Description Logics: Use Case in Medical Diagnosis. In Diego Calvanese and Boris Konev, editors, Proceedings of the 28th International Workshop on Description Logics, Athens,Greece, June 7-10, 2015, volume 1350 of CEUR Workshop Proceedings. CEUR-WS.org, 2015. URL: http://ceur-ws.org/Vol-1350/paper-60.pdf.
  92. Matthew Richardson and Pedro Domingos. Markov logic networks. Machine Learning, 62(1-2):107-136, January 2006. URL: https://doi.org/10.1007/s10994-006-5833-1.
  93. Tim Rocktäschel, Sameer Singh, and Sebastian Riedel. Injecting logical background knowledge into embeddings for relation extraction. In Rada Mihalcea, Joyce Yue Chai, and Anoop Sarkar, editors, NAACL HLT 2015, The 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, USA, May 31 - June 5, 2015, pages 1119-1129. The Association for Computational Linguistics, 2015. URL: https://doi.org/10.3115/v1/n15-1118.
  94. Riccardo Rosati. DL+log: A tight integration of description logics and disjunctive datalog. In Patrick Doherty, John Mylopoulos, and Christopher A. Welty, editors, Proceedings of the 10th International Conference on Principles of Knowledge Representation and Reasoning (KR'06), pages 68-78. AAAI Press, 2006. Google Scholar
  95. Sebastian Rudolph, Markus Krötzsch, and Pascal Hitzler. Description logic reasoning with decision diagrams: Compiling SHIQ to disjunctive datalog. In Amit Sheth, Steffen Staab, Mike Dean, Massimo Paolucci, Diana Maynard, Timothy Finin, and Krishnaprasad Thirunarayan, editors, Proceedings of the 7th International Semantic Web Conference (ISWC'08), volume 5318 of LNCS, pages 435-450. Springer, 2008. Google Scholar
  96. Ali Sadeghian, Mohammadreza Armandpour, Anthony Colas, and Daisy Zhe Wang. ChronoR: Rotation Based Temporal Knowledge Graph Embedding. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, pages 6471-6479. AAAI Press, 2021. URL: https://ojs.aaai.org/index.php/AAAI/article/view/16802.
  97. Floriano Scioscia, Michele Ruta, Ivano Bilenchi, Filippo Gramegna, Eugenio Di Sciascio, and Davide Loconte. Owl reasoner evaluation results, 2021. URL: https://doi.org/10.5281/ZENODO.5013799.
  98. Pengpeng Shao, Dawei Zhang, Guohua Yang, Jianhua Tao, Feihu Che, and Tong Liu. Tucker decomposition-based temporal knowledge graph completion. Knowledge-Based Systems, 238:107841, February 2022. URL: https://doi.org/10.1016/j.knosys.2021.107841.
  99. Umang Sharan and Jennifer Neville. Temporal-relational classifiers for prediction in evolving domains. In 2008 Eighth IEEE International Conference on Data Mining. IEEE, December 2008. URL: https://doi.org/10.1109/icdm.2008.125.
  100. Rob Shearer, Boris Motik, and Ian Horrocks. Hermit: A Highly-Efficient OWL Reasoner. In Catherine Dolbear, Alan Ruttenberg, and Ulrike Sattler, editors, OWLED, volume 432 of CEUR Workshop Proceedings. CEUR-WS.org, 2008. Google Scholar
  101. Evren Sirin, Bijan Parsia, Bernardo Cuenca Grau, Aditya Kalyanpur, and Yarden Katz. Pellet: A practical OWL-DL reasoner. Journal of Web Semantics, 5(2):51-53, 2007. Google Scholar
  102. Andreas Steigmiller, Thorsten Liebig, and Birte Glimm. Konclude: System description. Journal of Web Semantics, 27-28:78-85, August 2014. URL: https://doi.org/10.1016/j.websem.2014.06.003.
  103. Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. Rotate: Knowledge graph embedding by relational rotation in complex space. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net, 2019. URL: https://openreview.net/forum?id=HkgEQnRqYQ.
  104. Xiaoli Tang, Rui Yuan, Qianyu Li, Tengyun Wang, Haizhi Yang, Yundong Cai, and Hengjie Song. Timespan-Aware Dynamic Knowledge Graph Embedding by Incorporating Temporal Evolution. IEEE Access, 8:6849-6860, 2020. URL: https://doi.org/10.1109/access.2020.2964028.
  105. Edward Thomas, Jeff Z. Pan, and Yuan Ren. TrOWL: Tractable OWL 2 reasoning infrastructure. In Lecture Notes in Computer Science, pages 431-435. Springer Berlin Heidelberg, 2010. URL: https://doi.org/10.1007/978-3-642-13489-0_38.
  106. Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. Complex embeddings for simple link prediction. In Maria-Florina Balcan and Kilian Q. Weinberger, editors, Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016, volume 48 of JMLR Workshop and Conference Proceedings, pages 2071-2080. JMLR.org, 2016. URL: http://proceedings.mlr.press/v48/trouillon16.html.
  107. Dmitry Tsarkov and Ian Horrocks. FaCT++ description logic reasoner: System description. In Ulrich Furbach and Natarajan Shankar, editors, Proceedings of the 3rd International Joint Conference on Automated Reasoning (IJCAR'06), volume 4130 of LNCS, pages 292-297. Springer, 2006. Google Scholar
  108. Jeffrey Ullman. Principles of database and knowledge-base systems. Computer Science Press, Rockville, Md, 1988. Google Scholar
  109. Jeffrey D. Ullman and Allen Van Gelder. Parallel complexity of logical query programs. Algorithmica, 3(1-4):5-42, November 1988. URL: https://doi.org/10.1007/bf01762108.
  110. Jacopo Urbani, Ceriel Jacobs, and Markus Krötzsch. Column-oriented datalog materialization for large knowledge graphs. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'15). AAAI Press, 2016. To appear. Google Scholar
  111. Boris Villazón-Terrazas, Nuria García-Santa, Yuan Ren, Alessandro Faraotti, Honghan Wu, Yuting Zhao, Guido Vetere, and Jeff Z. Pan. Knowledge graph foundations. In Jeff Z. Pan, Guido Vetere, José Manuél Gómez-Pérez, and Honghan Wu, editors, Exploiting Linked Data and Knowledge Graphs in Large Organisations, pages 17-55. Springer, 2017. Google Scholar
  112. Denny Vrandečić and Markus Krötzsch. Wikidata: A free collaborative knowledgebase. Commun. ACM, 57(10), 2014. Google Scholar
  113. Przemyslaw Andrzej Walega, Mark Kaminski, and Bernardo Cuenca Grau. Reasoning over streaming data in metric temporal datalog. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, pages 3092-3099. AAAI Press, 2019. URL: https://doi.org/10.1609/aaai.v33i01.33013092.
  114. Dingmin Wang, Pan Hu, Przemyslaw Andrzej Walega, and Bernardo Cuenca Grau. Meteor: Practical reasoning in datalog with metric temporal operators. CoRR, abs/2201.04596, 2022. URL: http://arxiv.org/abs/2201.04596.
  115. Jingbin Wang, Wang Zhang, Xinyuan Chen, Jing Lei, and Xiaolian Lai. 3DRTE: 3D Rotation Embedding in Temporal Knowledge Graph. IEEE Access, 8:207515-207523, 2020. URL: https://doi.org/10.1109/access.2020.3036897.
  116. Zhihao Wang and Xin Li. Hybrid-TE: Hybrid Translation-Based Temporal Knowledge Graph Embedding. In 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, November 2019. URL: https://doi.org/10.1109/ictai.2019.00205.
  117. Simon Werner, Achim Rettinger, Lavdim Halilaj, and Jürgen Lüttin. RETRA: Recurrent transformers for learning temporally contextualized knowledge graph embeddings. In The Semantic Web, pages 425-440. Springer International Publishing, 2021. URL: https://doi.org/10.1007/978-3-030-77385-4_25.
  118. Kemas Wiharja, Jeff Z. Pan, Martin J. Kollingbaum, and Yu Deng. Schema aware iterative knowledge graph completion. Journal of Web Semantics, 65:100616, December 2020. URL: https://doi.org/10.1016/j.websem.2020.100616.
  119. Honghan Wu, Ronald Denaux, Panos Alexopoulos, Yuan Ren, and Jeff Z. Pan. Understanding knowledge graphs. In Jeff Z. Pan, Guido Vetere, José Manuél Gómez-Pérez, and Honghan Wu, editors, Exploiting Linked Data and Knowledge Graphs in Large Organisations, pages 147-180. Springer, 2017. Google Scholar
  120. Jiapeng Wu, Yishi Xu, Yingxue Zhang, Chen Ma, Mark Coates, and Jackie Chi Kit Cheung. TIE: A framework for embedding-based incremental temporal knowledge graph completion. In Fernando Diaz, Chirag Shah, Torsten Suel, Pablo Castells, Rosie Jones, and Tetsuya Sakai, editors, SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021, pages 428-437. ACM, 2021. URL: https://doi.org/10.1145/3404835.3462961.
  121. Tianxing Wu, Arijit Khan, Huan Gao, and Cheng Li. Efficiently embedding dynamic knowledge graphs. CoRR, abs/1910.06708, 2019. URL: http://arxiv.org/abs/1910.06708.
  122. Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, and Jens Lehmann. TeRo: A time-aware knowledge graph embedding via temporal rotation. In Proceedings of the 28th International Conference on Computational Linguistics. International Committee on Computational Linguistics, 2020. URL: https://doi.org/10.18653/v1/2020.coling-main.139.
  123. Chenjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Yazdi, and Jens Lehmann. Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding. In Lecture Notes in Computer Science, pages 654-671. Springer International Publishing, 2020. URL: https://doi.org/10.1007/978-3-030-62419-4_37.
  124. Yonghui Xu, Shengjie Sun, Huiguo Zhang, Chang'an Yi, Yuan Miao, Dong Yang, Xiaonan Meng, Yi Hu, Ke Wang, Huaqing Min, Hengjie Song, and Chuanyan Miao. Time-Aware Graph Embedding: A Temporal Smoothness and Task-Oriented Approach. ACM Transactions on Knowledge Discovery from Data, 16(3):1-23, June 2022. URL: https://doi.org/10.1145/3480243.
  125. Youri Xu, Haihong E, Meina Song, Wenyu Song, Xiaodong Lv, Haotian Wang, and Jinrui Yang. RTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion. In Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tür, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou, editors, Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, pages 5671-5681. Association for Computational Linguistics, 2021. URL: https://doi.org/10.18653/v1/2021.naacl-main.451.
  126. Jiasheng Zhang, Yongpan Sheng, Zheng Wang, and Jie Shao. TKGFrame: A Two-Phase Framework for Temporal-Aware Knowledge Graph Completion. In Web and Big Data, pages 196-211. Springer International Publishing, 2020. URL: https://doi.org/10.1007/978-3-030-60259-8_16.
  127. Yujing Zhou, Jia Peng, Lei Wang, Daren Zha, and Nan Mu. SEDE: semantic evolution-based dynamic knowledge graph embedding. Aust. J. Intell. Inf. Process. Syst., 16(4):64-73, 2019. URL: http://ajiips.com.au/papers/V16.4/v16n4_68-77.pdf.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact