Document Open Access Logo

Path Patterns Visualization in Semantic Graphs

Author José Paulo Leal

PDF
Thumbnail PDF

File

OASIcs.SLATE.2018.15.pdf
  • Filesize: 0.73 MB
  • 15 pages

Document Identifiers

Author Details

José Paulo Leal
  • CRACS & INESC-Porto LA, Faculty of Sciences, University of Porto, Portugal

Funding

This work is partially funded by the ERDF through the COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, by National Funds through the FCT as part of project UID/EEA/50014/2013, and by FourEyes. FourEyes is a Research Line within project "TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact /NORTE-01-0145-FEDER-000020" financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).

Cite AsGet BibTex

José Paulo Leal. Path Patterns Visualization in Semantic Graphs. In 7th Symposium on Languages, Applications and Technologies (SLATE 2018). Open Access Series in Informatics (OASIcs), Volume 62, pp. 15:1-15:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/OASIcs.SLATE.2018.15

Abstract

Graphs with a large number of nodes and edges are difficult to visualize. Semantic graphs add to the challenge since their nodes and edges have types and this information must be mirrored in the visualization. A common approach to cope with this difficulty is to omit certain nodes and edges, displaying sub-graphs of smaller size. However, other transformations can be used to abstract semantic graphs and this research explores a particular one, both to reduce the graph's size and to focus on its path patterns. Antigraphs are a novel kind of graph designed to highlight path patterns using this kind of abstraction. They are composed of antinodes connected by antiedges, and these reflect respectively edges and nodes of the semantic graph. The prefix "anti" refers to this inversion of the nature of the main graph constituents. Antigraphs trade the visualization of nodes and edges by the visualization of graph path patterns involving typed edges. Thus, they are targeted to users that require a deep understanding of the semantic graph it represents, in particular of its path patterns, rather than to users wanting to browse the semantic graph's content. Antigraphs help programmers querying the semantic graph or designers of semantic measures interested in using it as a semantic proxy. Hence, antigraphs are not expected to compete with other forms of semantic graph visualization but rather to be used a complementary tool. This paper provides a precise definition both of antigraphs and of the mapping of semantic graphs into antigraphs. Their visualization is obtained with antigraphs diagrams. A web application to visualize and interact with these diagrams was implemented to validate the proposed approach. Diagrams of well-known semantic graphs are also presented and discussed.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Semantic networks
  • Human-centered computing → Graph drawings
Keywords
  • semantic graph visualization
  • linked data visualization
  • path pattern discovery
  • semantic graph transformation

Metrics

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

References

  1. James Abello, Frank Van Ham, and Neeraj Krishnan. ASK-GraphView: A large scale graph visualization system. IEEE Transactions on Visualization and Computer Graphics, 12(5):669-676, 2006. Google Scholar
  2. Daniel Archambault, Tamara Munzner, and David Auber. GrouseFlocks: Steerable exploration of graph hierarchy space. IEEE Transactions on Visualization and Computer Graphics, 14(4):900-913, 2008. URL: http://dx.doi.org/10.1109/TVCG.2008.34.
  3. David Auber. Tulip - A Huge Graph Visualization Framework, pages 105-126. Springer, 2004. URL: http://dx.doi.org/10.1007/978-3-642-18638-7_5.
  4. Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. DBpedia: A nucleus for a web of open data. In The Semantic Web, pages 722-735, 2007. Google Scholar
  5. Mathieu Bastian, Sebastien Heymann, and Mathieu Jacomy. Gephi: An open source software for exploring and manipulating networks. In 8th International AAAI Conference on Weblogs and Social Media, pages 361-362, 2009. Google Scholar
  6. Fabio Benedetti, Sonia Bergamaschi, and Laura Po. A visual summary for linked open data sources. In International Semantic Web Conference, 2014. Google Scholar
  7. Nikos Bikakis, John Liagouris, Maria Kromida, George Papastefanatos, and Timos Sellis. Towards scalable visual exploration of very large RDF graphs. In The Semantic Web: ESWC 2015 Satellite Events, pages 9-13. Springer International Publishing, 2015. URL: http://dx.doi.org/10.1007/978-3-319-25639-9_2.
  8. Christiane Fellbaum. Wordnet: An electronic lexical database. MIT Press, 1999. Google Scholar
  9. Nicola Guarino, Daniel Oberle, and Steffan Staab. What is an ontology? In Handbook on ontologies, pages 1-17. Springer, 2009. Google Scholar
  10. Sébastien Harispe, Sylvie Ranwez, Stefan Janaqi, and Jacky Montmain. Semantic similarity from natural language and ontology analysis. Synthesis Lectures on Human Language Technologies, 8(1):1-254, 2015. Google Scholar
  11. Tuukka Hastrup, Richard Cyganiak, and Uldis Bojārs. Browsing linked data with Fenfire. In Linked Data on the Web, 2008. Google Scholar
  12. Philipp Heim, Sebastian Hellmann, Jens Lehmann, Steffen Lohmann, and Timo Stegemann. RelFinder: Revealing relationships in RDF knowledge bases. In Semantic Multimedia, pages 182-187, 2009. URL: http://dx.doi.org/10.1007/978-3-642-10543-2_21.
  13. Johannes Hoffart, Fabian M. Suchanek, Klaus Berberich, and Gerhard Weikum. YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence, pages 3161-3165, 2013. Google Scholar
  14. Stephen G. Kobourov. Spring embedders and force directed graph drawing algorithms. CoRR, abs/1201.3011, 2012. URL: http://arxiv.org/abs/1201.3011.
  15. Zhiyuan Lin, Nan Cao, Hanghang Tong, Fei Wang, U. Kang, and Duen Horng Polo Chau. Demonstrating interactive multi-resolution large graph exploration. In Proceedings of the 2013 IEEE 13th International Conference on Data Mining Workshops, pages 1097-1100, 2013. URL: http://dx.doi.org/10.1109/ICDMW.2013.124.
  16. Kang Zhang, Haofen Wang, Thanh Tran, and Yong Yu. ZoomRDF: semantic fisheye zooming on RDF data. In 19th international conference on World Wide Web, pages 1329-1332, 2010. Google Scholar
  17. Michael Zinsmaier, Ulrik Brandes, Oliver Deussen, and Hendrik Strobelt. Interactive level-of-detail rendering of large graphs. IEEE Transactions on Visualization and Computer Graphics, 18(12):2486-2495, 2012. URL: http://dx.doi.org/10.1109/TVCG.2012.238.
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact