Skip to main content
SpringerLink
Log in

Summarization of Massive RDF Graphs Using Identifier Classification

  • Conference paper
  • First Online:
Graph-Based Representation and Reasoning (ICCS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI))

Included in the following conference series:

  • 205 Accesses

Abstract

The size of massive knowledge graphs (KGs) and the lack of prior information regarding the schemas, ontologies and vocabularies they use frequently makes them hard to understand and visualize. Graph summarization techniques can help by abstracting details of the original graph to produce a reduced summary that can more easily be explored. Identifiers often carry latent information which could be used for classification of the entities they represent. Particularly, IRI namespaces can be used to classify RDF resources. Namespaces, used in some RDF serialization formats as a shortening mechanism for resource IRIs, have no role in the semantics of RDF. Nevertheless, there is often a hidden meaning behind the decision of grouping resources under a common prefix and assigning an alias to it. We improved on previous work on a namespace-based approach to KG summarization that classifies resources using their namespaces. Producing the summary graph is fast, light on computing resources and requires no previous domain knowledge. The summary graph can be used to analyze the namespace inter-dependencies of the original graph. We also present chilon, a tool for calculating namespace-based KG summaries. Namespaces are gathered from explicit declarations in the graph serialization, community contributions or resource IRI prefix analysis. We applied chilon to publicly available KGs, used it to generate interactive visualizations of the summaries, and discuss the results obtained.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Named after Chilon of Sparta, one of the Seven Sages of Greece, who coined the ancient proverb “less is more” or “brevity is a way of philosophy”.

  2. 2.

    https://github.com/linkml/prefixmaps/blob/main/src/prefixmaps/data/merged.csv.

  3. 3.

    For example, http://example.org/ \(\rightarrow \) (http://example.org/foo/, http://example.org/bar/).

  4. 4.

    Ontology available at https://andrefs.github.io/chilon_rs/ns-graph-summ.ttl.

References

  1. Alexander, K., Cyganiak, R., Hausenblas, M., Zhao, J.: Describing linked datasets with the VoID vocabulary (2011)

    Google Scholar 

  2. Beckett, D.: RDF 1.1 N-Triples (2014). https://www.w3.org/TR/n-triples/

  3. Beckett, D., Berners-Lee, T., Prud’hommeaux, E., Carothers, G.: RDF 1.1 Turtle (2014)

    Google Scholar 

  4. Bergman, M.K.: A Knowledge Representation Practionary. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98092-8

    Book  MATH  Google Scholar 

  5. Berners-Lee, T.: Linked data - design issues (2006). http://www.w3.org/DesignIssues/LinkedData.html

  6. Bizer, C., Heath, T., Berners-Lee, T.: Linked data: the story so far. In: Semantic Services, Interoperability and Web Applications: Emerging Concepts. IGI global (2011)

    Google Scholar 

  7. Bonifati, A., Dumbrava, S., Kondylakis, H.: Graph summarization. arXiv preprint arXiv:2004.14794 (2020)

  8. Čebirić, Š, et al.: Summarizing semantic graphs: a survey. VLDB J. 28, 295–327 (2019). https://doi.org/10.1007/s00778-018-0528-3

    Article  Google Scholar 

  9. da Costa, A.R.S.L.: Sumariação de grafos semânticos de grande dimensão usando espaços de nomes. Master’s thesis, Faculty of Sciences of the University of Porto (2022)

    Google Scholar 

  10. da Costa, A.R.S.L., Santos, A., Leal, J.P.: Large semantic graph summarization using namespaces. In: 11th Symposium on Languages, Applications and Technologies, SLATE 2022. Schloss Dagstuhl-Leibniz-Zentrum für Informatik (2022)

    Google Scholar 

  11. Cyganiak, R., Wood, D., Lanthaler, M., Klyne, G., Carroll, J.J., McBride, B.: RDF 1.1 concepts and abstract syntax. W3C Recommendation, 25 February 2014

    Google Scholar 

  12. Debattista, J., Lange, C., Auer, S., Cortis, D.: Evaluating the quality of the LOD cloud: an empirical investigation. Semant. Web 9(6), 859–901 (2018)

    Article  Google Scholar 

  13. Duerst, M., Suignard, M.: RFC 3987: Internationalized Resource Identifiers (IRIs) (2005)

    Google Scholar 

  14. Färber, M.: The Microsoft academic knowledge graph: a linked data source with 8 billion triples of scholarly data. In: Ghidini, C., et al. (eds.) ISWC 2019. LNCS, vol. 11779, pp. 113–129. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30796-7_8

    Chapter  Google Scholar 

  15. Färber, M., Menne, C., Harth, A.: A linked data wrapper for CrunchBase. Semant. Web 9(4), 505–515 (2018)

    Article  Google Scholar 

  16. Haller, A., Fernández, J.D., Kamdar, M.R., Polleres, A.: What are links in linked open data? A characterization and evaluation of links between knowledge graphs on the web. J. Data Inf. Qual. (JDIQ) 12(2), 1–34 (2020)

    Article  Google Scholar 

  17. Hassanzadeh, O., Consens, M.P.: Linked movie data base. In: LDOW (2009)

    Google Scholar 

  18. Hofmann, A., Perchani, S., Portisch, J., Hertling, S., Paulheim, H.: DBkWik: towards knowledge graph creation from thousands of Wikis. In: ISWC (2017)

    Google Scholar 

  19. Janowicz, K., Hitzler, P., Adams, B., Kolas, D., Vardeman, C., II.: Five stars of linked data vocabulary use. Semant. Web 5(3), 173–176 (2014)

    Article  Google Scholar 

  20. Kondylakis, H., Kotzinos, D., Manolescu, I.: RDF graph summarization: principles, techniques and applications (tutorial). In: EDBT/ICDT 2019–22nd International Conference on Extending Database Technology-Joint Conference (2019)

    Google Scholar 

  21. Krishnan, A.: Making search easier (2018). https://www.aboutamazon.com/news/innovation-at-amazon/making-search-easier

  22. Lehmann, J., et al.: DBpedia-a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6(2), 167–195 (2015)

    Article  Google Scholar 

  23. Ley, M.: DBLP: some lessons learned. VLDB Endow. 2(2), 1493–1500 (2009)

    Article  Google Scholar 

  24. Liu, Y., Safavi, T., Dighe, A., Koutra, D.: Graph summarization methods and applications: a survey. ACM Comput. Surv. (CSUR) 51(3), 1–34 (2018)

    Article  Google Scholar 

  25. Matuszek, C., Witbrock, M., Cabral, J., DeOliveira, J.: An introduction to the syntax and content of Cyc. UMBC Computer Science and Electrical Engineering Department Collection (2006)

    Google Scholar 

  26. McCrae, J., Fellbaum, C., Cimiano, P.: Publishing and linking wordnet using lemon and RDF. In: Proceedings of the 3rd Workshop on Linked Data in Linguistics (2014)

    Google Scholar 

  27. Pellissier Tanon, T., Weikum, G., Suchanek, F.: YAGO 4: a reason-able knowledge base. In: Harth, A., et al. (eds.) ESWC 2020. LNCS, vol. 12123, pp. 583–596. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49461-2_34

    Chapter  Google Scholar 

  28. Singhal, A., et al.: Introducing the knowledge graph: things, not strings. Official Google blog, 16 May 2012

    Google Scholar 

  29. Tchechmedjiev, A., et al.: ClaimsKG: a knowledge graph of fact-checked claims. In: Ghidini, C., et al. (eds.) ISWC 2019. LNCS, vol. 11779, pp. 309–324. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30796-7_20

    Chapter  Google Scholar 

  30. Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)

    Article  Google Scholar 

Download references

Acknowledgments

This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project LA/P/0063/2020. André Fernandes dos Santos: Ph.D. Grant SFRH/BD/129225/2017 from Fundação para a Ciência e Tecnologia (FCT), Portugal.

Author information

Authors and Affiliations

  1. CRACS & INESC Tec LA/Faculty of Sciences, University of Porto, Porto, Portugal

    André Fernandes dos Santos & José Paulo Leal

Authors
  1. André Fernandes dos Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José Paulo Leal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to André Fernandes dos Santos .

Editor information

Editors and Affiliations

  1. Universidad de Málaga, Málaga, Spain

    Manuel Ojeda-Aciego

  2. FernUniversität in Hagen, Hagen, Germany

    Kai Sauerwald

  3. Humboldt-Universität zu Berlin, Berlin, Germany

    Robert Jäschke

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

dos Santos, A.F., Leal, J.P. (2023). Summarization of Massive RDF Graphs Using Identifier Classification. In: Ojeda-Aciego, M., Sauerwald, K., Jäschke, R. (eds) Graph-Based Representation and Reasoning. ICCS 2023. Lecture Notes in Computer Science(). Springer, Cham. https://doi.org/10.1007/978-3-031-40960-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-40960-8_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40959-2

  • Online ISBN: 978-3-031-40960-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation