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Indexing Data on the Web: A Comparison of Schema-Level Indices for Data Search

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Database and Expert Systems Applications (DEXA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12392))

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Abstract

Indexing the Web of Data offers many opportunities, in particular, to find and explore data sources. One major design decision when indexing the Web of Data is to find a suitable index model, i.e., how to index and summarize data. Various efforts have been conducted to develop specific index models for a given task. With each index model designed, implemented, and evaluated independently, it remains difficult to judge whether an approach generalizes well to another task, set of queries, or dataset. In this work, we empirically evaluate six representative index models with unique feature combinations. Among them is a new index model incorporating inferencing over RDFS and owl:sameAs. We implement all index models for the first time into a single, stream-based framework. We evaluate variations of the index models considering sub-graphs of size 0, 1, and 2 hops on two large, real-world datasets. We evaluate the quality of the indices regarding the compression ratio, summarization ratio, and F1-score denoting the approximation quality of the stream-based index computation. The experiments reveal huge variations in compression ratio, summarization ratio, and approximation quality for different index models, queries, and datasets. However, we observe meaningful correlations in the results that help to determine the right index model for a given task, type of query, and dataset.

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References

  1. Benedetti, F., Bergamaschi, S., Po, L.: Exposing the underlying schema of LOD sources. In: Joint IEEE/WIC/ACM WI and IAT, pp. 301–304. IEEE (2015)

    Google Scholar 

  2. Blume, T., Scherp, A.: FLuID: a meta model to flexibly define schema-level indices for the web of data. CoRR abs/1908.01528 (2019)

    Google Scholar 

  3. Blume, T., Scherp, A.: Indexing data on the web: a comparison of schema-level indices for data search - extended Technical report. CoRR abs/2006.07064 (2020)

    Google Scholar 

  4. Čebirić, Š., et al.: Summarizing semantic graphs: a survey. VLDB J. 28(3), 295–327 (2018). https://doi.org/10.1007/s00778-018-0528-3

    Article  Google Scholar 

  5. Ciglan, M., Nørvåg, K., Hluchý, L.: The SemSets model for ad-hoc semantic list search. In: WWW, pp. 131–140. ACM (2012)

    Google Scholar 

  6. Goasdoué, F., Guzewicz, P., Manolescu, I.: Incremental structural summarization of RDF graphs. In: EDBT, pp. 566–569. OpenProceedings.org (2019)

    Google Scholar 

  7. Gottron, T., Scherp, A., Krayer, B., Peters, A.: LODatio: using a schema-level index to support users infinding relevant sources of linked data. In: K-CAP, pp. 105–108. ACM (2013)

    Google Scholar 

  8. Hose, K., Schenkel, R., Theobald, M., Weikum, G.: Database foundations for scalable RDF processing. In: Polleres, A., et al. (eds.) Reasoning Web 2011. LNCS, vol. 6848, pp. 202–249. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23032-5_4

    Chapter  Google Scholar 

  9. Käfer, T., Abdelrahman, A., Umbrich, J., O’Byrne, P., Hogan, A.: Observing linked data dynamics. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 213–227. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38288-8_15

    Chapter  Google Scholar 

  10. Konrath, M., Gottron, T., Staab, S., Scherp, A.: SchemEX - efficient construction of a data catalogue by stream-based indexing of linked data. J. Web Sem. 16, 52–58 (2012)

    Article  Google Scholar 

  11. Lei, Y., Uren, V., Motta, E.: SemSearch: a search engine for the semantic web. In: Staab, S., Svátek, V. (eds.) EKAW 2006. LNCS (LNAI), vol. 4248, pp. 238–245. Springer, Heidelberg (2006). https://doi.org/10.1007/11891451_22

    Chapter  Google Scholar 

  12. Mihindukulasooriya, N., Poveda-Villalón, M., García-Castro, R., Gómez-Pérez, A.: Loupe - an online tool for inspecting datasets in the linked data cloud. In: ISWC Posters & Demos, vol. 1486. CEUR-WS.org (2015)

    Google Scholar 

  13. Neumann, T., Moerkotte, G.: Characteristic sets: accurate cardinality estimation for RDF queries with multiple joins. In: ICDE, pp. 984–994. IEEE (2011)

    Google Scholar 

  14. Pietriga, E., et al.: Browsing linked data catalogs with LODAtlas. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11137, pp. 137–153. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00668-6_9

    Chapter  Google Scholar 

  15. Schaible, J., Gottron, T., Scherp, A.: TermPicker: enabling the reuse of vocabulary terms by exploiting data from the linked open data cloud. In: Sack, H., Blomqvist, E., d’Aquin, M., Ghidini, C., Ponzetto, S.P., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9678, pp. 101–117. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-34129-3_7

    Chapter  Google Scholar 

  16. Spahiu, B., Porrini, R., Palmonari, M., Rula, A., Maurino, A.: ABSTAT: ontology-driven linked data summaries with pattern minimalization. In: Sack, H., Rizzo, G., Steinmetz, N., Mladenić, D., Auer, S., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9989, pp. 381–395. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47602-5_51

    Chapter  Google Scholar 

  17. Tran, T., Haase, P., Studer, R.: Semantic search – using graph-structured semantic models for supporting the search process. In: Rudolph, S., Dau, F., Kuznetsov, S.O. (eds.) ICCS-ConceptStruct 2009. LNCS (LNAI), vol. 5662, pp. 48–65. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03079-6_5

    Chapter  Google Scholar 

  18. Tran, T., Ladwig, G., Rudolph, S.: Managing structured and semi-structured RDF data using structure indexes. TKDE 25(9), 2076–2089 (2013)

    Google Scholar 

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Acknowledgment

This research was co-financed by the EU H2020 project MOVING (http://www.moving-project.eu/) under contract no 693092.

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Authors and Affiliations

  1. Kiel University, Kiel, Germany

    Till Blume

  2. Ulm University, Ulm, Germany

    Ansgar Scherp

Authors
  1. Till Blume
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  2. Ansgar Scherp
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Correspondence to Till Blume .

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Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Kotsis

  4. IFS, Vienna University of Technology, Vienna, Wien, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Blume, T., Scherp, A. (2020). Indexing Data on the Web: A Comparison of Schema-Level Indices for Data Search. In: Hartmann, S., Küng, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2020. Lecture Notes in Computer Science(), vol 12392. Springer, Cham. https://doi.org/10.1007/978-3-030-59051-2_18

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  • DOI: https://doi.org/10.1007/978-3-030-59051-2_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59050-5

  • Online ISBN: 978-3-030-59051-2

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