Semantic Data Acquisition by Traversing Class–Class Relationships Over Linked Open Data

  • Atsuko YamaguchiEmail author
  • Kouji Kozaki
  • Kai Lenz
  • Yasunori Yamamoto
  • Hiroshi Masuya
  • Norio Kobayashi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10055)


Linked Open Data (LOD), a powerful mechanism for linking different datasets published on the World Wide Web, is expected to increase the value of data through mashups of various datasets on the Web. One of the important requirements for LOD is to be able to find a path of resources connecting two given classes. Because each class contains many instances, inspecting all of the paths or combinations of the instances results in an explosive increase of computational complexity. To solve this problem, we have proposed an efficient method that obtains and prioritizes a comprehensive set of connections over resources by traversing class–class relationships of interest. To put our method into practice, we have been developing a tool for LOD exploration. In this paper, we introduce the technologies used in the tool, focusing especially on the development of a measure for predicting whether a path of class–class relationships has connected triples or not. Because paths without connected triples can be predicted and removed, using the prediction measure enables us to display more paths from which users can obtain data that interests them.


Linked data Class–class relationships Data integration Path finding 



This work was supported by JSPS KAKENHI Grant Number 25280081, 24120002 and the National Bioscience Database Center (NBDC) of the Japan Science and Technology Agency (JST).


  1. 1.
    Heath, T., Bizer, C.: Linked Data: Evolving the Web into a Global Data Space. Synthesis Lectures on the Semantic Web: Theory and Technology, 1st edn. 1: 1, 1–136. Morgan & Claypool (2011)Google Scholar
  2. 2.
    Jupp, S., Malone, J., Bolleman, J., Brandizi, M., Davies, M., Garcia, L., Gaulton, A., Gehant, S., Laibe, C., Redaschi, N., Wimalaratne, S.M., Martin, M., Le Novére, N., Parkinson, H., Birney, E., Jenkinson, A.M.: The EBI RDF platform: linked open data for the life sciences. Bioinformatics 30(9), 1338–1339 (2014)CrossRefGoogle Scholar
  3. 3.
    Belleau, F., Nolin, M.A., Tourigny, N., Rigault, P., Morissette, J.: Bio2RDF: towards a mashup to build bioinformatics knowledge systems. J. Biomed. Inf. 41(5), 706–716 (2008)CrossRefGoogle Scholar
  4. 4.
    Redaschi, N., UniProt Consortium: UniProt in RDF: tackling data integration and distributed annotation with the semantic web. Nat. Precedings (2009). doi: 10.1038/npre.2009.3193.1
  5. 5.
    Fu, G., Batchelor, C., Dumontier, M., Hastings, J., Willighagen, E., Bolton, E.: PubChemRDF: towards the semantic annotation of PubChem compound and substance databases. J. Cheminformatics 7(34) (2015). doi: 10.1186/s13321-015-0084-4
  6. 6.
    Heim, P., Hellmann, S., Lehmann, J., Lohmann, S., Stegemann, T.: RelFinder: revealing relationships in RDF knowledge bases. In: Chua, T.-S., Kompatsiaris, Y., Mérialdo, B., Haas, W., Thallinger, G., Bailer, W. (eds.) SAMT 2009. LNCS, vol. 5887, pp. 182–187. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-10543-2_21 CrossRefGoogle Scholar
  7. 7.
    Popov, I.O., Schraefel, M.C., Hall, W., Shadbolt, N.: Connecting the dots: a multi-pivot approach to data exploration. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 553–568. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-25073-6_35 CrossRefGoogle Scholar
  8. 8.
    Ferré, S.: Sparklis: a SPARQL endpoint explorer for expressive question answering. In: Proceedings of the ISWC 2014 Posters & Demonstrations Track, CEUR Workshop Proceedings 1272, Riva del Garda, Italy (2014)Google Scholar
  9. 9.
    Oren, E., Delbru, R., Decker, S.: Extending faceted navigation for RDF data. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 559–572. Springer, Heidelberg (2006). doi: 10.1007/11926078_40 CrossRefGoogle Scholar
  10. 10.
    Qu, Y., Ge, W., Cheng, G., Gao, Z.: Class association structure derived from linked objects. In: Proceedings of the Web Science Conference (WebSci 2009: Society On-Line), Athens, Greece (2009)Google Scholar
  11. 11.
    Yamaguchi, A., Kozaki, K., Lenz, K., Wu, H., Kobayashi, N.: An intelligent SPARQL query builder for exploration of various life-science databases. In: The 3rd International Workshop on Intelligent Exploration of Semantic Data (IESD 2014), CEUR Workshop Proceedings 1279, Riva del Garda, Italy (2014)Google Scholar
  12. 12.
    Villalon, P., Suárez-Figueroa, M.C., Gómez-Pérez, A.: A double classification of common pitfalls in ontologies. In: Workshop on Ontology Quality (OntoQual 2010), Lisbon, Portugal (2010)Google Scholar
  13. 13.
    Yamaguchi, A., Kozaki, K., Lenz, K., Wu, H., Yamamoto, Y., Kobayashi, N.: Efficiently finding paths between classes to build a SPARQL query for life-science databases. In: Qi, G., Kozaki, K., Pan, J.Z., Yu, S. (eds.) JIST 2015. LNCS, vol. 9544, pp. 321–330. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-31676-5_24 CrossRefGoogle Scholar
  14. 14.
    Yamamoto, Y., Yamaguchi, A., Bono, H., Takagi, T.: Allie: a database and a search service of abbreviations and long forms. Database (2011). doi: 10.1093/database/bar013 Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Atsuko Yamaguchi
    • 1
    Email author
  • Kouji Kozaki
    • 2
  • Kai Lenz
    • 3
  • Yasunori Yamamoto
    • 1
  • Hiroshi Masuya
    • 3
    • 4
  • Norio Kobayashi
    • 3
    • 4
    • 5
  1. 1.Database Center for Life Science (DBCLS)Research Organization of Information and SystemsKashiwaJapan
  2. 2.The Institute of Scientific and Industrial Research (ISIR)Osaka UniversityOsakaJapan
  3. 3.Advanced Center for Computing and Communication (ACCC), RIKENWako, SaitamaJapan
  4. 4.BioResource Center (BRC), RIKENTsukubaJapan
  5. 5.RIKEN CLST-JEOL Collaboration CenterChuo-ku, KobeJapan

Personalised recommendations