Using Triple Pattern Fragments to Enable Streaming of Top-k Shortest Paths via the Web

  • Laurens De VochtEmail author
  • Ruben Verborgh
  • Erik Mannens
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 641)


Searching for relationships between Linked Data resources is typically interpreted as a pathfinding problem: looking for chains of intermediary nodes (hops) forming the connection or bridge between these resources in a single dataset or across multiple datasets. In many cases centralizing all needed linked data in a certain (specialized) repository or index to be able to run the algorithm is not possible or at least not desired. To address this, we propose an approach to top-k shortest pathfinding, which optimally translates a pathfinding query into sequences of triple pattern fragment requests. Triple Pattern Fragments were recently introduced as a solution to address the availability of data on the Web and the scalability of linked data client applications, preventing data processing bottlenecks on the server. The results are streamed to the client, thus allowing clients to do asynchronous processing of the top-k shortest paths. We explain how this approach behaves using a training dataset, a subset of DBpedia with 10 million triples, and show the trade-offs to a SPARQL approach where all the data is gathered in a single triple store on a single machine. Furthermore we investigate the scalability when increasing the size of the subset up to 110 million triples.



The research activities that have been described in this paper were funded by Ghent University, iMinds (Interdisciplinary institute for Technology) a research institute founded by the Flemish Government, the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT), the Fund for Scientific Research-Flanders (FWO-Flanders), and the European Union.


  1. 1.
    Brander, A.W., Sinclair, M.C.: A comparative study of k-shortest path algorithms. In: Merabti, M., Carew, M., Ball, F. (eds.) Performance Engineering of Computer and Telecommunications Systems, pp. 370–379. Springer, London (1996)CrossRefGoogle Scholar
  2. 2.
    Cedeno, J.P.: A framework for top-K queries over weighted RDF graphs. Ph.D. thesis, Arizona State University (2010)Google Scholar
  3. 3.
    Cheng, G., Zhang, Y., Qu, Y.: Explass: exploring associations between entities via top-K ontological patterns and facets. In: Mika, P., et al. (eds.) ISWC 2014, Part II. LNCS, vol. 8797, pp. 422–437. Springer, Heidelberg (2014)Google Scholar
  4. 4.
    De Vocht, L., Beecks, C., Verborgh, R., Seidl, T., Mannens, E., Van de Walle, R.: Improving semantic relatedness in paths for storytelling with linked data on the web. In: Gandon, F., Guéret, C., Villata, S., Breslin, J., Faron-Zucker, C., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9341, pp. 31–35. Springer, Switzerland (2015). CrossRefGoogle Scholar
  5. 5.
    De Vocht, L., Coppens, S., Verborgh, R., Vander Sande, M., Mannens, E., Van de Walle, R.: Discovering meaningful connections between resources in the web of data. In: Proceedings of the 6th Workshop on Linked Data on the Web (LDOW2013) (2013)Google Scholar
  6. 6.
    Eppstein, D.: Finding the k shortest paths. SIAM J. Comput. 28(2), 652–673 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Fang, L., Sarma, A.D., Yu, C., Bohannon, P.: REX: explaining relationships between entity pairs. Proc. VLDB Endow. 5(3), 241–252 (2011)CrossRefGoogle Scholar
  8. 8.
    Fernández, J.D., Martínez-Prieto, M.A., Gutierrez, C.: Compact representation of large RDF data sets for publishing and exchange. In: Patel-Schneider, P.F., et al. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 193–208. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Moore, J.L., Steinke, F., Tresp, V.: A novel metric for information retrieval in semantic networks. In: García-Castro, R., Fensel, D., Antoniou, G. (eds.) ESWC 2011. LNCS, vol. 7117, pp. 65–79. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Shibuya, T., Imai, H.: New flexible approaches for multiple sequence alignment. J. Comput. Biol. 4(3), 385–413 (1997)CrossRefGoogle Scholar
  11. 11.
    Verborgh, R., et al.: Querying datasets on the web with high availability. In: Mika, P., et al. (eds.) ISWC 2014, Part I. LNCS, vol. 8796, pp. 180–196. Springer, Heidelberg (2014)Google Scholar
  12. 12.
    Verborgh, R., Vander Sande, M., Hartig, O., Van Herwegen, J., De Vocht, L., De Meester, B., Haesendonck, G., Colpaert, P.: Triple pattern fragments: a low-cost knowledge graph interface for the Web. J. Web Semant. 37–38, 184–206 (2016)CrossRefGoogle Scholar
  13. 13.
    Zhang, D.Y., Wu, W., Ouyang, C.F.: Top-k shortest path query processing in RDF graphs. Chin. J. Electron. 43(8), 1531–1537 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Laurens De Vocht
    • 1
    Email author
  • Ruben Verborgh
    • 1
  • Erik Mannens
    • 1
  1. 1.Data Science LabGhent University, iMindsGhentBelgium

Personalised recommendations