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Integration of Knowledge Bases and External Information Sources via Magic Properties and Query-Driven Entity Linking

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Information Integration and Web Intelligence (iiWAS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14416))

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Abstract

A knowledge base (KB) accumulates human knowledge in resource description framework (RDF), which forms a knowledge graph consisting of subject-predicate-object triples. Although RDF KBs are essential information sources for various knowledge processing, many non-RDF information sources exist. Therefore, integrating RDF KBs and external sources will be beneficial in supporting various applications. However, external sources have diverse access methods and input/output formats. In addition, their integration with the KB involves identifying correct entities which correspond to objects in the external sources, which is challenging. In this paper, we present an architecture for an integrated environment (named Knowledge Mediator) in which the user can query the KB as if external sources were an integral part using the Magic Properties of SPARQL. We also propose novel query-driven on-demand entity linking, to select correct entities in the KB for objects in the external sources.

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Change history

  • 22 November 2023

    A correction has been published.

Notes

  1. 1.

    https://github.com/AtomGraph/CSV2RDF.

  2. 2.

    https://www.w3.org/TR/rdf11-concepts/.

  3. 3.

    https://www.w3.org/TR/2013/REC-sparql11-query-20130321/.

  4. 4.

    https://www.w3.org/Submission/spin-modeling/#spin-magic.

  5. 5.

    https://jena.apache.org/documentation/query/writing_propfuncs.html.

  6. 6.

    https://www.w3.org/2009/sparql/wiki/Feature:JavaScriptFunctions.

References

  1. Altwaijry, H., Mehrotra, S., Kalashnikov, D.V.: QuERy: a framework for integrating entity resolution with query processing. Proc. VLDB Endow. 9(3), 120–131 (2015)

    Article  Google Scholar 

  2. Asprino, L., Daga, E., Gangemi, A., Mulholland, P.: Knowledge graph construction with a Façade: a unified method to access heterogeneous data sources on the web. ACM Trans. Internet Technol. 23(1), 1–31 (2023)

    Article  Google Scholar 

  3. Bhattacharya, I., Getoor, L.: Query-time entity resolution. J. Artif. Int. Res. 30(1), 621–657 (2007)

    Google Scholar 

  4. Bunescu, R., Paşca, M.: Using encyclopedic knowledge for named entity disambiguation. In: Proceedings 11th Conference of the European Chapter of the Association for Computational Linguistics, pp. 9–16 (2006)

    Google Scholar 

  5. Buron, M., et al.: Ontology-based RDF integration of heterogeneous data. In: Proceedings 23rd International Conference on Extending Database Technology, pp. 299–310 (2020)

    Google Scholar 

  6. Buron, M., Goasdoué, F., Manolescu, I., Mugnier, M.L.: Obi-Wan: ontology-based RDF integration of heterogeneous data. Proc. VLDB Endow. 13(12), 2933–2936 (2020)

    Article  Google Scholar 

  7. Calvanese, D., et al.: Ontop: answering SPARQL queries over relational databases. Semant. Web 8(3), 471–487 (2017)

    Article  Google Scholar 

  8. Calvanese, D., Giese, M., Hovland, D., Rezk, M.: Ontology-based integration of cross-linked datasets. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 199–216. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25007-6_12

    Chapter  Google Scholar 

  9. Christophides, V., Efthymiou, V., Palpanas, T., Papadakis, G., Stefanidis, K.: An overview of end-to-end entity resolution for big data. ACM Comput. Surv. 53(6), 1–42 (2021). https://doi.org/10.1145/3418896

    Article  Google Scholar 

  10. Cucerzan, S.: Large-scale named entity disambiguation based on Wikipedia data. In: Proceedings 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 708–716 (2007)

    Google Scholar 

  11. Ekaputra, F., et al.: Ontology-based data integration in multi-disciplinary engineering environments: a review. Open J. Inf. Syst. 4(1), 1–26 (2017)

    Google Scholar 

  12. Ferragina, P., Scaiella, U.: TAGME: on-the-fly annotation of short text fragments (by Wikipedia entities). In: Proceedings 19th ACM International Conference on Information and Knowledge Management, pp. 1625–1628 (2010)

    Google Scholar 

  13. Görlitz, O., Staab, S.: SPLENDID: SPARQL endpoint federation exploiting VOID descriptions. In: Proceedings 2nd International Conference on Consuming Linked Data, pp. 13–24 (2011)

    Google Scholar 

  14. Mahdisoltani, F., Biega, J.A., Suchanek, F.M.: YAGO3: a knowledge base from multilingual Wikipedias. In: Proceedings 7th Conference on Innovative Data Systems Research (2015)

    Google Scholar 

  15. Saleem, M., et al.: CostFed: cost-based query optimization for SPARQL endpoint federation. Procedia Comput. Sci. 137, 163–174 (2018)

    Article  Google Scholar 

  16. Saleem, M., Ngonga Ngomo, A.-C.: HiBISCuS: hypergraph-based source selection for SPARQL endpoint federation. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 176–191. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07443-6_13

    Chapter  Google Scholar 

  17. Schwarte, A., Haase, P., Hose, K., Schenkel, R., Schmidt, M.: FedX: optimization techniques for federated query processing on linked data. In: Aroyo, L., et al. (eds.) The Semantic Web – ISWC 2011, pp. 601–616. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25073-6_38

    Chapter  Google Scholar 

  18. Sevgili, Ö., et al.: Neural entity linking: a survey of models based on deep learning. Semant. Web 13(3), 527–570 (2022)

    Article  Google Scholar 

  19. Shen, W., et al.: Entity linking with a knowledge base: issues, techniques, and solutions. IEEE Trans. Knowl. Data Eng. 27(2), 443–460 (2015)

    Article  Google Scholar 

  20. Tamašauskaitė, G., Groth, P.: Defining a knowledge graph development process through a systematic review. ACM Trans. Softw. Eng. Methodol. 32(1), 1–40 (2023). https://doi.org/10.1145/3522586

    Article  Google Scholar 

  21. Xiao, G., Hovland, D., Bilidas, D., Rezk, M., Giese, M., Calvanese, D.: Efficient ontology-based data integration with canonical IRIs. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 697–713. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_45

    Chapter  Google Scholar 

  22. Xin, K., et al.: Large-scale entity alignment via knowledge graph merging, partitioning and embedding. In: Proceedings of the 31st ACM International Conference on Information and Knowledge Management, pp. 2240–2249 (2022)

    Google Scholar 

  23. Yamada, I., et al.: Global entity disambiguation with BERT. In: Proceedings 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 3264–3271 (2022)

    Google Scholar 

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Acknowledgement

This work was supported in part by “Research and Development Project of the Enhanced Infrastructures for Post-5G Information and Communication Systems” (JPNP20017) commissioned by NEDO, JSPS Grants-in-Aid for Scientific Research (JP23H03399, JP22K19802, JP22H03694), JST CREST (JP-MJCR22M2), AMED (JP21zf0127005), and collaboration research with Sky Co., LTD.

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Authors and Affiliations

  1. University of Tsukuba, Tsukuba, Japan

    Yuuki Ohmori, Hiroyuki Kitagawa & Toshiyuki Amagasa

  2. Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan

    Yuuki Ohmori, Hiroyuki Kitagawa & Akiyoshi Matono

Authors
  1. Yuuki Ohmori
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  2. Hiroyuki Kitagawa
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  3. Toshiyuki Amagasa
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  4. Akiyoshi Matono
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Corresponding author

Correspondence to Yuuki Ohmori .

Editor information

Editors and Affiliations

  1. Monash University, Melbourne, VIC, Australia

    Pari Delir Haghighi

  2. La Trobe University, Melbourne, VIC, Australia

    Eric Pardede

  3. University of Auckland, Auckland, New Zealand

    Gillian Dobbie

  4. University of Auckland, Auckland, New Zealand

    Vithya Yogarajan

  5. Udayana University, Denpasar, Indonesia

    Ngurah Agus Sanjaya ER

  6. Johannes Kepler University Linz, Linz, Austria

    Gabriele Kotsis

  7. Johannes Kepler University Linz, Linz, Austria

    Ismail Khalil

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Ohmori, Y., Kitagawa, H., Amagasa, T., Matono, A. (2023). Integration of Knowledge Bases and External Information Sources via Magic Properties and Query-Driven Entity Linking. In: Delir Haghighi, P., et al. Information Integration and Web Intelligence. iiWAS 2023. Lecture Notes in Computer Science, vol 14416. Springer, Cham. https://doi.org/10.1007/978-3-031-48316-5_30

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  • DOI: https://doi.org/10.1007/978-3-031-48316-5_30

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  • Print ISBN: 978-3-031-48315-8

  • Online ISBN: 978-3-031-48316-5

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