Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
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
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
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
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
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)
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
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)
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)
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)
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
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
Acknowledgments
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Yamaguchi, A., Kozaki, K., Lenz, K., Yamamoto, Y., Masuya, H., Kobayashi, N. (2016). Semantic Data Acquisition by Traversing Class–Class Relationships Over Linked Open Data. In: Li, YF., et al. Semantic Technology. JIST 2016. Lecture Notes in Computer Science(), vol 10055. Springer, Cham. https://doi.org/10.1007/978-3-319-50112-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-50112-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50111-6
Online ISBN: 978-3-319-50112-3
eBook Packages: Computer ScienceComputer Science (R0)