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Structural Summarization of Semantic Graphs Using Quotients

Authors Ansgar Scherp , David Richerby , Till Blume , Michael Cochez , Jannik Rau

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Author Details

Ansgar Scherp
  • Ulm University, Germany
David Richerby
  • University of Essex, UK
Till Blume
  • Ernst and Young Research, Berlin, Germany
Michael Cochez
  • Vrije Universiteit Amsterdam, The Netherlands
  • Elsevier Discovery Lab, Amsterdam, The Netherlands
Jannik Rau
  • Ulm University, Germany

Funding

  • Scherp, Ansgar: Co-funded by the CodeInspector project (No. 504226141) of the DFG, German Research Foundation.
  • Cochez, Michael: Partially funded by the Graph-Massivizer project, funded by the Horizon Europe programme of the European Union (grant 101093202).

Cite AsGet BibTex

Ansgar Scherp, David Richerby, Till Blume, Michael Cochez, and Jannik Rau. Structural Summarization of Semantic Graphs Using Quotients. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 12:1-12:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/TGDK.1.1.12

Abstract

Graph summarization is the process of computing a compact version of an input graph while preserving chosen features of its structure. We consider semantic graphs where the features include edge labels and label sets associated with a vertex. Graph summaries are typically much smaller than the original graph. Applications that depend on the preserved features can perform their tasks on the summary, but much faster or with less memory overhead, while producing the same outcome as if they were applied on the original graph. In this survey, we focus on structural summaries based on quotients that organize vertices in equivalence classes of shared features. Structural summaries are particularly popular for semantic graphs and have the advantage of defining a precise graph-based output. We consider approaches and algorithms for both static and temporal graphs. A common example of quotient-based structural summaries is bisimulation, and we discuss this in detail. While there exist other surveys on graph summarization, to the best of our knowledge, we are the first to bring in a focused discussion on quotients, bisimulation, and their relation. Furthermore, structural summarization naturally connects well with formal logic due to the discrete structures considered. We complete the survey with a brief description of approaches beyond structural summaries.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Graph algorithms
  • Theory of computation → Graph algorithms analysis
  • General and reference → Surveys and overviews
Keywords
  • graph summarization
  • quotients
  • stratified bisimulation

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References

  1. Dean Allemang and James A. Hendler. Semantic Web for the Working Ontologist - Effective Modeling in RDFS and OWL, Second Edition. Morgan Kaufmann, 2011. URL: http://www.elsevierdirect.com/product.jsp?isbn=9780123859655.
  2. James F. Allen. Maintaining knowledge about temporal intervals. Commun. ACM, 26(11):832-843, 1983. URL: https://doi.org/10.1145/182.358434.
  3. Anas Alzogbi and Georg Lausen. Similar structures inside RDF-graphs. In Workshop on Linked Data on the Web. CEUR-WS.org, 2013. URL: http://ceur-ws.org/Vol-996/papers/ldow2013-paper-05.pdf.
  4. M. A. Baazizi, H. Ben Lahmar, D. Colazzo, G. Ghelli, and C. Sartiani. Schema inference for massive JSON datasets. In Proceedings of the 20th International Conference on Extending Database Technology, EDBT, pages 222-233. OpenProceedings.org, 2017. URL: https://doi.org/10.5441/002/EDBT.2017.21.
  5. Charles W. Bachman. Data structure diagrams. Data Base, 1(2):4-10, 1969. URL: https://doi.org/10.1145/1017466.1017467.
  6. Fabio Benedetti, Sonia Bergamaschi, and Laura Po. Exposing the underlying schema of LOD sources. In Web Intelligence (WI), pages 301-304. IEEE, 2015. URL: https://doi.org/10.1109/WI-IAT.2015.99.
  7. Laurent Bindschaedler, Jasmina Malicevic, Baptiste Lepers, Ashvin Goel, and Willy Zwaenepoel. Tesseract: distributed, general graph pattern mining on evolving graphs. In European Conf. on Comp. Systems (EuroSys), pages 458-473. ACM, 2021. URL: https://doi.org/10.1145/3447786.3456253.
  8. Till Blume. Semantic structural graph summaries for evolving and distributed graphs. PhD thesis, University of Ulm, Germany, 2022. URL: https://nbn-resolving.org/urn:nbn:de:bsz:289-oparu-46050-1.
  9. Till Blume, David Richerby, and Ansgar Scherp. Incremental and parallel computation of structural graph summaries for evolving graphs. In Int. Conf. on Inform. and Knowledge Management (CIKM), pages 75-84. ACM, 2020. URL: https://doi.org/10.1145/3340531.3411878.
  10. Till Blume, David Richerby, and Ansgar Scherp. FLUID: A common model for semantic structural graph summaries based on equivalence relations. Theoretical Computer Science, 854:136-158, 2021. URL: https://doi.org/10.1016/J.TCS.2020.12.019.
  11. Till Blume and Ansgar Scherp. Indexing data on the web: A comparison of schema-level indices for data search. In Database and Expert Systems Applications (DEXA), pages 277-286, 2020. URL: https://doi.org/10.1007/978-3-030-59051-2_18.
  12. Paolo Boldi and Sebastiano Vigna. Axioms for centrality. Internet Math., 10(3-4):222-262, 2014. URL: https://doi.org/10.1080/15427951.2013.865686.
  13. Jeroen Bollen, Jasper Steegmans, Jan Van den Bussche, and Stijn Vansummeren. Learning graph neural networks using exact compression. In Proceedings of the 6th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), pages 8:1-8:9. ACM, 2023. URL: https://doi.org/10.1145/3594778.3594878.
  14. A. Bonifati, S. Dumbrava, and H. Kondylakis. Graph summarization. CoRR, abs/2004.14794, 2020. URL: https://arxiv.org/abs/2004.14794.
  15. Redouane Bouhamoum, Zoubida Kedad, and Stéphane Lopes. Incremental schema discovery at scale for RDF data. In The Semantic Web - 18th International Conference, ESWC, volume 12731 of Lecture Notes in Computer Science, pages 195-211. Springer, 2021. URL: https://doi.org/10.1007/978-3-030-77385-4_12.
  16. D. Brickley and R.V. Guha. RDF Schema 1.1, 2014. URL: https://www.w3.org/TR/2014/REC-rdf-schema-20140225/.
  17. Peter Buneman and Slawek Staworko. RDF graph alignment with bisimulation. Proc. VLDB Endow., 9(12):1149-1160, 2016. URL: https://doi.org/10.14778/2994509.2994531.
  18. Stéphane Campinas, Thomas Perry, Diego Ceccarelli, Renaud Delbru, and Giovanni Tummarello. Introducing RDF graph summary with application to assisted SPARQL formulation. In Database and Expert Systems Applications (DEXA), pages 261-266. IEEE, 2012. URL: https://doi.org/10.1109/DEXA.2012.38.
  19. Šejla Čebirić, François Goasdoué, Haridimos Kondylakis, Dimitris Kotzinos, Ioana Manolescu, Georgia Troullinou, and Mussab Zneika. Summarizing semantic graphs: a survey. VLDB J., 28(3):295-327, 2019. URL: https://doi.org/10.1007/S00778-018-0528-3.
  20. Šejla Čebirić, François Goasdoué, and Ioana Manolescu. A framework for efficient representative summarization of RDF graphs. In Int. Semantic Web Conf. (ISWC). CEUR-WS.org, 2017. URL: http://ceur-ws.org/Vol-1963/paper512.pdf.
  21. S. Ceri, G. Gottlob, and L. Tanca. What you always wanted to know about Datalog (but never dared to ask). Trans. Knowl. Data Eng., 1(1):146-166, 1989. URL: https://doi.org/10.1109/69.43410.
  22. Marek Ciglan, Kjetil Nørvåg, and Ladislav Hluchý. The SemSets model for ad-hoc semantic list search. In World Wide Web Conf. (WWW), pages 131-140, 2012. URL: https://doi.org/10.1145/2187836.2187855.
  23. Mariano P. Consens, Valeria Fionda, Shahan Khatchadourian, and Giuseppe Pirrò. S+EPPs: Construct and explore bisimulation summaries, plus optimize navigational queries; all on existing SPARQL systems. PVLDB, 8(12):2028-2031, 2015. URL: https://doi.org/10.14778/2824032.2824128.
  24. Richard Cyganiak, David Wood, and Markus Lanthaler. RDF 1.1 Concepts and Abstract Syntax, 2014. URL: http://www.w3.org/TR/2014/REC-rdf11-concepts-20140225.
  25. Reinhard Diestel. Graph Theory, volume 173 of Graduate Texts in Mathematics. Springer, 5th edition, 2016. URL: https://doi.org/10.1007/978-3-662-53622-3.
  26. Li Ding, Joshua Shinavier, Zhenning Shangguan, and Deborah L. McGuinness. SameAs networks and beyond: Analyzing deployment status and implications of owl:sameAs in Linked Data. In Int. Semantic Web Conf. (ISWC), pages 145-160. Springer, 2010. URL: https://doi.org/10.1007/978-3-642-17746-0_10.
  27. Agostino Dovier, Carla Piazza, and Alberto Policriti. An efficient algorithm for computing bisimulation equivalence. Theor. Comput. Sci., 311(1-3):221-256, 2004. URL: https://doi.org/10.1016/S0304-3975(03)00361-X.
  28. Chi Thang Duong, Dung Hoang, Hongzhi Yin, Matthias Weidlich, Quoc Viet Hung Nguyen, and Karl Aberer. Efficient streaming subgraph isomorphism with graph neural networks. VLDB Endow., 14(5):730-742, 2021. URL: https://doi.org/10.14778/3446095.3446097.
  29. M. Dürst and M. Suignard. RFC 3987 int.ized resource identifiers (IRIs), 2005. URL: https://www.ietf.org/rfc/rfc3987.txt.
  30. Gregor Engels, Claus Lewerentz, Wilhelm Schäfer, Andy Schürr, and Bernhard Westfechtel. Graph transformations and model-driven engineering. In Graph Transformations and Model-Driven Engineering, pages 1-5. Springer, 2010. URL: https://doi.org/10.1007/978-3-642-17322-6_1.
  31. Ronald Fagin. Generalized first-order spectra and polynomial-time recognizable sets. In Complexity of Computation, volume 7, pages 43-73, 1974. Google Scholar
  32. W. Fan, J. Li, J. Luo, Z. Tan, X. Wang, and Y. Wu. Incremental graph pattern matching. In Management of Data (SIGMOD), pages 925-936. ACM, 2011. URL: https://doi.org/10.1145/1989323.1989420.
  33. Wenfei Fan, Jianzhong Li, Xin Wang, and Yinghui Wu. Query preserving graph compression. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 157-168. ACM, 2012. URL: https://doi.org/10.1145/2213836.2213855.
  34. Wenfei Fan, Yuanhao Li, Muyang Liu, and Can Lu. Making graphs compact by lossless contraction. In SIGMOD '21: International Conference on Management of Data, pages 472-484. ACM, 2021. URL: https://doi.org/10.1145/3448016.3452797.
  35. Wenfei Fan, Yuanhao Li, Muyang Liu, and Can Lu. A hierarchical contraction scheme for querying big graphs. In SIGMOD '22: International Conference on Management of Data, pages 1726-1740. ACM, 2022. URL: https://doi.org/10.1145/3514221.3517862.
  36. Wenfei Fan, Yinghui Wu, and Jingbo Xu. Adding counting quantifiers to graph patterns. In Management of Data (SIGMOD), pages 1215-1230. ACM, 2016. URL: https://doi.org/10.1145/2882903.2882937.
  37. Yixiang Fang, Xin Huang, Lu Qin, Ying Zhang, Wenjie Zhang, Reynold Cheng, and Xuemin Lin. A survey of community search over big graphs. VLDB J., 29(1):353-392, 2020. URL: https://doi.org/10.1007/S00778-019-00556-X.
  38. Javier D. Fernández, Miguel A. Martínez-Prieto, Claudio Gutierrez, Axel Polleres, and Mario Arias. Binary RDF representation for publication and exchange (HDT). J. Web Semant., 19:22-41, 2013. URL: https://doi.org/10.1016/J.WEBSEM.2013.01.002.
  39. Alessandro Generale, Till Blume, and Michael Cochez. Scaling R-GCN training with graph summarization. In Companion of The Web Conference 2022, pages 1073-1082. ACM, 2022. URL: https://doi.org/10.1145/3487553.3524719.
  40. François Goasdoué, Pawel Guzewicz, and Ioana Manolescu. Incremental structural summarization of RDF graphs. In Proceedings of the International Conference on Extending Database Technology (EDBT), pages 566-569. OpenProceedings.org, 2019. URL: https://doi.org/10.5441/002/EDBT.2019.57.
  41. François Goasdoué, Pawel Guzewicz, and Ioana Manolescu. RDF graph summarization for first-sight structure discovery. VLDB J., 29(5):1191-1218, 2020. URL: https://doi.org/10.1007/S00778-020-00611-Y.
  42. Roy Goldman and Jennifer Widom. DataGuides: Enabling query formulation and optimization in semistructured databases. In VLDB'97, Proceedings of 23rd International Conference on Very Large Data Bases, pages 436-445. Morgan Kaufmann, 1997. URL: http://www.vldb.org/conf/1997/P436.PDF.
  43. Thomas Gottron, Ansgar Scherp, Bastian Krayer, and Arne Peters. LODatio: using a schema-level index to support users in finding relevant sources of linked data. In Knowledge Capture (K-CAP), pages 105-108. ACM, 2013. URL: https://doi.org/10.1145/2479832.2479841.
  44. Mahdi Hajiabadi, Venkatesh Srinivasan, and Alex Thomo. Dynamic graph summarization: Optimal and scalable. In IEEE International Conference on Big Data, pages 545-554. IEEE, 2022. URL: https://doi.org/10.1109/BIGDATA55660.2022.10020422.
  45. William L. Hamilton. Graph Representation Learning. Morgan and Claypool, 2020. URL: https://www.cs.mcgill.ca/~wlh/grl_book/.
  46. Wook-Shin Han, Jinsoo Lee, Minh-Duc Pham, and Jeffrey Xu Yu. iGraph: A framework for comparisons of disk-based graph indexing techniques. Proceedings of the International Conference on Very Large Data Bases (VLDB) Endowment, 3(1):449-459, 2010. URL: https://doi.org/10.14778/1920841.1920901.
  47. Jan Hegewald, Felix Naumann, and Melanie Weis. XStruct: Efficient schema extraction from multiple and large XML documents. In Proceedings of the International Conference on Data Engineering Workshops (ICDE), page 81. IEEE Computer Society, 2006. URL: https://doi.org/10.1109/ICDEW.2006.166.
  48. Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, and others. Knowledge graphs. CoRR, abs/2003.02320, 2020. URL: https://arxiv.org/abs/2003.02320.
  49. Neil Immerman. Descriptive Complexity. Springer, 1999. URL: https://doi.org/10.1007/978-1-4612-0539-5.
  50. Xiaowei Jiang, Xiang Zhang, Feifei Gao, Chunan Pu, and Peng Wang. Graph compression strategies for instance-focused semantic mining. In Chinese Semantic Web Symposium/Chinese Web Science Conf., pages 50-61. Springer, 2013. URL: https://doi.org/10.1007/978-3-642-54025-7_5.
  51. Tobias Käfer, Ahmed Abdelrahman, Jürgen Umbrich, Patrick O'Byrne, and Aidan Hogan. Observing linked data dynamics. In Proceedings of the Extended Semantic Web Conference (ESWC), volume 7882 of Lecture Notes in Computer Science, pages 213-227. Springer, 2013. URL: https://doi.org/10.1007/978-3-642-38288-8_15.
  52. A. Kansal and F. Spezzano. A scalable graph-coarsening based index for dynamic graph databases. In Int. Conf. on Information and Knowledge Management (CIKM), pages 207-216. ACM, 2017. URL: https://doi.org/10.1145/3132847.3133003.
  53. Raghav Kaushik, Pradeep Shenoy, Philip Bohannon, and Ehud Gudes. Exploiting local similarity for indexing paths in graph-structured data. In Int. Conf. on Data Engineering (ICDE), pages 129-140. IEEE, 2002. URL: https://doi.org/10.1109/ICDE.2002.994703.
  54. K. Kellou-Menouer and Z. Kedad. Schema discovery in RDF data sources. In Int. Conf. on Conceptual Modeling (ER), volume 9381 of Lecture Notes in Computer Science, pages 481-495. Springer, 2015. URL: https://doi.org/10.1007/978-3-319-25264-3_36.
  55. Arijit Khan, Sourav S. Bhowmick, and Francesco Bonchi. Summarizing static and dynamic big graphs. VLDB Endowment, 10(12):1981-1984, 2017. URL: https://doi.org/10.14778/3137765.3137825.
  56. Kifayat-Ullah Khan, Waqas Nawaz, and Young-Koo Lee. Set-based approximate approach for lossless graph summarization. Computing, 97(12):1185-1207, 2015. URL: https://doi.org/10.1007/S00607-015-0454-9.
  57. Aleks Kissinger, Alex Merry, and Matvey Soloviev. Pattern graph rewrite systems. In Int. Workshop on Developments in Computational Models (DCM), pages 54-66, 2012. URL: https://doi.org/10.4204/EPTCS.143.5.
  58. Jihoon Ko, Yunbum Kook, and Kijung Shin. Incremental lossless graph summarization. In Conf. on Knowledge Discovery and Data Mining (SIGKDD), pages 317-327. ACM, 2020. URL: https://doi.org/10.1145/3394486.3403074.
  59. M. Konrath, T. Gottron, S. Staab, and A. Scherp. SchemEX - efficient construction of a data catalogue by stream-based indexing of linked data. J. Web Semant., 16:52-58, 2012. URL: https://doi.org/10.1016/J.WEBSEM.2012.06.002.
  60. Danai Koutra, U Kang, Jilles Vreeken, and Christos Faloutsos. Summarizing and understanding large graphs. Stat. Anal. Data Min., 8(3):183-202, 2015. URL: https://doi.org/10.1002/SAM.11267.
  61. Michihiro Kuramochi and George Karypis. Frequent subgraph discovery. In Int. Conf. on Data Mining (ICDM), pages 313-320. IEEE, 2001. URL: https://doi.org/10.1109/ICDM.2001.989534.
  62. Kostis Kyzirakos, Manos Karpathiotakis, Konstantina Bereta, George Garbis, Charalampos Nikolaou, Panayiotis Smeros, Stella Giannakopoulou, Kallirroi Dogani, and Manolis Koubarakis. The spatiotemporal RDF store Strabon. In Advances in Spatial and Temporal Databases - 13th International Symposium, volume 8098 of Lecture Notes in Computer Science, pages 496-500. Springer, 2013. URL: https://doi.org/10.1007/978-3-642-40235-7_35.
  63. Kristen LeFevre and Evimaria Terzi. Grass: Graph structure summarization. In SIAM International Conference on Data Mining, SDM, pages 454-465. SIAM, 2010. URL: https://doi.org/10.1137/1.9781611972801.40.
  64. Leonid Libkin. Elements of Finite Model Theory. Springer, 2004. URL: https://doi.org/10.1007/978-3-662-07003-1.
  65. Xingjie Liu, Yuanyuan Tian, Qi He, Wang-Chien Lee, and John McPherson. Distributed graph summarization. In Int. Conf. on Information and Knowledge Management (CIKM), pages 799-808. ACM, 2014. URL: https://doi.org/10.1145/2661829.2661862.
  66. Yike Liu, Tara Safavi, Abhilash Dighe, and Danai Koutra. Graph summarization methods and applications: A survey. ACM Comput. Surv., 51(3):62:1-62:34, 2018. URL: https://doi.org/10.1145/3186727.
  67. Yongming Luo, George H. L. Fletcher, Jan Hidders, Paul De Bra, and Yuqing Wu. Regularities and dynamics in bisimulation reductions of big graphs. In First International Workshop on Graph Data Management Experiences and Systems, GRADES, page 13. CWI/ACM, 2013. URL: https://doi.org/10.1145/2484425.2484438.
  68. Yongming Luo, George H. L. Fletcher, Jan Hidders, Yuqing Wu, and Paul De Bra. External memory k-bisimulation reduction of big graphs. In 22nd ACM International Conference on Information and Knowledge Management, CIKM'13, pages 919-928. ACM, 2013. URL: https://doi.org/10.1145/2505515.2505752.
  69. Jan Martens, Jan Friso Groote, Lars B. van den Haak, Pieter Hijma, and Anton Wijs. A linear parallel algorithm to compute bisimulation and relational coarsest partitions. In Formal Aspects of Component Software (FACS), volume 13077 of LNCS, pages 115-133. Springer, 2021. URL: https://doi.org/10.1007/978-3-030-90636-8_7.
  70. D. L. McGuinness and F. van Harmelen. OWL Web Ontology Language, 2014. [Online, accessed: December 6, 2023]. URL: https://www.w3.org/TR/2004/REC-owl-features-20040210/.
  71. Nandana Mihindukulasooriya, María Poveda-Villalón, Raúl García-Castro, and Asunción Gómez-Pérez. Loupe - an online tool for inspecting datasets in the linked data cloud. In Int. Semantic Web Conf. (ISWC). CEUR-WS.org, 2015. URL: http://ceur-ws.org/Vol-1486/paper_113.pdf.
  72. Tova Milo and Dan Suciu. Index structures for path expressions. In Int. Conf. Database Theory (ICDT), pages 277-295. Springer, 1999. URL: https://doi.org/10.1007/3-540-49257-7_18.
  73. Seunghwan Min, Sung Gwan Park, Kunsoo Park, Dora Giammarresi, Giuseppe F. Italiano, and Wook-Shin Han. Symmetric continuous subgraph matching with bidirectional dynamic programming. Proc. VLDB Endow., 14(8):1298-1310, 2021. URL: https://doi.org/10.14778/3457390.3457395.
  74. Muhammad Anis Uddin Nasir, Cigdem Aslay, Gianmarco De Francisci Morales, and Matteo Riondato. Tiptap: Approximate mining of frequent k-subgraph patterns in evolving graphs. ACM Trans. on Knowledge Discovery from Data, 15(3), apr 2021. URL: https://doi.org/10.1145/3442590.
  75. Saket Navlakha, Rajeev Rastogi, and Nisheeth Shrivastava. Graph summarization with bounded error. In ACM SIGMOD International Conference on Management of Data, pages 419-432. ACM, 2008. URL: https://doi.org/10.1145/1376616.1376661.
  76. Svetlozar Nestorov, Jeffrey D. Ullman, Janet L. Wiener, and Sudarshan S. Chawathe. Representative objects: Concise representations of semistructured, hierarchial data. In Proceedings 13th Intl Conference on Data Engineering, pages 79-90. IEEE Computer Society, 1997. URL: https://doi.org/10.1109/ICDE.1997.581741.
  77. T. Neumann and G. Moerkotte. Characteristic sets: Accurate cardinality estimation for RDF queries with multiple joins. In Int. Conf. on Data Engineering (ICDE), pages 984-994. IEEE, 2011. URL: https://doi.org/10.1109/ICDE.2011.5767868.
  78. Robert Paige and Robert Endre Tarjan. Three partition refinement algorithms. SIAM J. Comput., 16(6):973-989, 1987. URL: https://doi.org/10.1137/0216062.
  79. Alexandros Pappas, Georgia Troullinou, Giannis Roussakis, Haridimos Kondylakis, and Dimitris Plexousakis. Exploring importance measures for summarizing RDF/S KBs. In The Semantic Web ESWC, pages 387-403. Springer, 2017. URL: https://doi.org/10.1007/978-3-319-58068-5_24.
  80. Emmanuel Pietriga, Hande Gözükan, Caroline Appert, Marie Destandau, Sejla Čebirić, François Goasdoué, and Ioana Manolescu. Browsing linked data catalogs with LODAtlas. In Int. Semantic Web Conf. (ISWC), pages 137-153. Springer, 2018. URL: https://doi.org/10.1007/978-3-030-00668-6_9.
  81. Detlef Plump. Essentials of term graph rewriting. Electron. Notes Theor. Comput. Sci., 51:277-289, 2001. URL: https://doi.org/10.1016/S1571-0661(04)80210-X.
  82. Miao Qiao, Hao Zhang, and Hong Cheng. Subgraph matching: on compression and computation. Proceedings of the International Conference on Very Large Data Bases (VLDB) Endowment, 11(2):176-188, 2017. URL: https://doi.org/10.14778/3149193.3149198.
  83. Chen Qun, Andrew Lim, and Kian Win Ong. D(k)-index: An adaptive structural summary for graph-structured data. In Management of Data (SIGMOD), pages 134-144. ACM, 2003. URL: https://doi.org/10.1145/872757.872776.
  84. Mohammad Rashid, Giuseppe Rizzo, Nandana Mihindukulasooriya, Marco Torchiano, and Óscar Corcho. KBQ - A tool for knowledge base quality assessment using evolution analysis. In Proceedings of Workshops and Tutorials of the International Conference on Knowledge Capture (K-CAP), volume 2065 of CEUR Workshop Proceedings, pages 58-63. CEUR-WS.org, 2017. URL: http://ceur-ws.org/Vol-2065/paper13.pdf.
  85. Jannik Rau, David Richerby, and Ansgar Scherp. Computing k-bisimulations for large graphs: A comparison and efficiency analysis. In Graph Transformation - 16th International Conference, ICGT 2023, volume 13961 of Lecture Notes in Computer Science, pages 223-242. Springer, 2023. URL: https://doi.org/10.1007/978-3-031-36709-0_12.
  86. Davide Sangiorgi. On the origins of bisimulation and coinduction. ACM Trans. Program. Lang. Syst., 31(4):15:1-15:41, 2009. URL: https://doi.org/10.1145/1516507.1516510.
  87. Johann Schaible, Thomas Gottron, and Ansgar Scherp. TermPicker: Enabling the reuse of vocabulary terms by exploiting data from the Linked Open Data cloud. In The Semantic Web (ESWC), pages 101-117. Springer, 2016. URL: https://doi.org/10.1007/978-3-319-34129-3_7.
  88. Alexander Schätzle, Antony Neu, Georg Lausen, and Martin Przyjaciel-Zablocki. Large-scale bisimulation of RDF graphs. In Semantic Web Information Management Workshop, pages 1-8. ACM, 2013. URL: https://doi.org/10.1145/2484712.2484713.
  89. Kijung Shin, Amol Ghoting, Myunghwan Kim, and Hema Raghavan. SWeG: Lossless and lossy summarization of web-scale graphs. In World Wide Web Conf. (WWW), pages 1679-1690. ACM, 2019. URL: https://doi.org/10.1145/3308558.3313402.
  90. Qi Song, Yinghui Wu, and Xin Luna Dong. Mining summaries for knowledge graph search. In ICDM, pages 1215-1220. IEEE, 2016. URL: https://doi.org/10.1109/ICDM.2016.0162.
  91. Blerina Spahiu, Riccardo Porrini, Matteo Palmonari, Anisa Rula, and Andrea Maurino. ABSTAT: ontology-driven linked data summaries with pattern minimalization. In The Semantic Web (ESWC), pages 381-395, 2016. URL: https://doi.org/10.1007/978-3-319-47602-5_51.
  92. Giorgio Stefanoni, Boris Motik, and Egor V. Kostylev. Estimating the cardinality of conjunctive queries over RDF data using graph summarisation. In Proceedings of the 2018 World Wide Web Conference, pages 1043-1052. ACM, 2018. URL: https://doi.org/10.1145/3178876.3186003.
  93. Yuanyuan Tian, Richard A. Hankins, and Jignesh M. Patel. Efficient aggregation for graph summarization. In ACM SIGMOD International Conference on Management of Data, SIGMOD, pages 567-580. ACM, 2008. URL: https://doi.org/10.1145/1376616.1376675.
  94. Yuanyuan Tian and Jignesh M. Patel. Interactive graph summarization. In Link Mining: Models, Algorithms, and Applications, pages 389-409. Springer, 2010. URL: https://doi.org/10.1007/978-1-4419-6515-8_15.
  95. Thanh Tran, Günter Ladwig, and Sebastian Rudolph. Managing structured and semistructured RDF data using structure indexes. Trans. Knowl. Data Eng., 25(9):2076-2089, 2013. URL: https://doi.org/10.1109/TKDE.2012.134.
  96. Thanh Tran, Haofen Wang, Sebastian Rudolph, and Philipp Cimiano. Top-k exploration of query candidates for efficient keyword search on graph-shaped (RDF) data. In Proceedings of the 25th International Conference on Data Engineering, pages 405-416. IEEE Computer Society, 2009. URL: https://doi.org/10.1109/ICDE.2009.119.
  97. Georgia Troullinou, Haridimos Kondylakis, Evangelia Daskalaki, and Dimitris Plexousakis. Ontology understanding without tears: The summarization approach. Semantic Web, 8(6):797-815, 2017. URL: https://doi.org/10.3233/SW-170264.
  98. Octavian Udrea, Andrea Pugliese, and V. S. Subrahmanian. GRIN: A graph based RDF index. In AAAI, pages 1465-1470. AAAI Press, 2007. URL: http://www.aaai.org/Library/AAAI/2007/aaai07-232.php.
  99. Johanna Völker and Mathias Niepert. Statistical schema induction. In Extended Semantic Web Conf. (ESWC), pages 124-138. Springer, 2011. URL: https://doi.org/10.1007/978-3-642-21034-1_9.
  100. Lanjun Wang, Oktie Hassanzadeh, Shuo Zhang, Juwei Shi, Limei Jiao, Jia Zou, and Chen Wang. Schema management for document stores. Proceedings of the International Conference on Very Large Data Bases (VLDB) Endowment, 8(9):922-933, 2015. URL: https://doi.org/10.14778/2777598.2777601.
  101. Qiu Yue Wang, Jeffrey Xu Yu, and Kam-Fai Wong. Approximate graph schema extraction for semi-structured data. In Advances in Database Technology (EDBT), pages 302-316. Springer, 2000. URL: https://doi.org/10.1007/3-540-46439-5_21.
  102. Jennifer Widom. Data management for XML: research directions. IEEE Data Eng. Bull., 22(3):44-52, 1999. URL: http://sites.computer.org/debull/99sept/jennifer.ps.
  103. Xifeng Yan and Jiawei Han. gSpan: Graph-based substructure pattern mining. In Int. Conf. on Data Mining (ICDM), pages 721-724. IEEE, 2002. URL: https://doi.org/10.1109/ICDM.2002.1184038.
  104. D. Yuan, P. Mitra, H. Yu, and C. L. Giles. Iterative graph feature mining for graph indexing. In Int. Conf. on Data Engineering (ICDE), pages 198-209. IEEE, 2012. URL: https://doi.org/10.1109/ICDE.2012.11.
  105. D. Yuan, P. Mitra, H. Yu, and C. L. Giles. Updating graph indices with a one-pass algorithm. In Management of Data (SIGMOD), pages 1903-1916. ACM, 2015. URL: https://doi.org/10.1145/2723372.2746482.
  106. Ning Zhang, Yuanyuan Tian, and Jignesh M. Patel. Discovery-driven graph summarization. In Int. Conf. on Data Engineering (ICDE), pages 880-891. IEEE, 2010. URL: https://doi.org/10.1109/ICDE.2010.5447830.
  107. Xiang Zhang, Gong Cheng, and Yuzhong Qu. Ontology summarization based on RDF sentence graph. In World Wide Web (WWW), pages 707-716. ACM, 2007. URL: https://doi.org/10.1145/1242572.1242668.
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