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Domain-Specific Knowledge Graph Construction for Semantic Analysis

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The Semantic Web: ESWC 2020 Satellite Events (ESWC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12124))

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Abstract

Knowledge graphs are widely used for systematic representation of real-world data. Large-scale, general purpose knowledge graphs, having millions of facts, have been constructed through automated techniques from publicly available datasets such as Wikipedia. However, these knowledge graphs are typically incomplete and often fail to correctly capture the semantics of the data. This holds true particularly for domain-specific data, where the generic techniques for automated knowledge graph creation often fail due to several challenges, such as lack of training data, semantic ambiguities and absence of representative ontologies. The focus of this thesis is on automated knowledge graph construction for the cultural heritage domain. The goal is to tackle the research challenges encountered during the creation of an ontology and a knowledge graph from digitized collections of cultural heritage data. This paper identifies the specific research problems for these tasks and presents a methodology and approach for a solution, along with preliminary results.

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Notes

  1. 1.

    https://wpi.art/.

  2. 2.

    https://digi.ub.uni-heidelberg.de/diglit/koepplin1974bd1/0084, 0095.

  3. 3.

    https://digi.ub.uni-heidelberg.de/diglit/studio1894/0019.

  4. 4.

    Linked Open Data: http://www.w3.org/DesignIssues/LinkedData.

  5. 5.

    OpenGLAM: http://openglam.org.

  6. 6.

    Europeana: http://europeana.eu.

  7. 7.

    DPLA: https://dp.la/.

  8. 8.

    https://www.wikidata.org.

  9. 9.

    https://www.clips.uantwerpen.be/conll2003/ner/.

  10. 10.

    https://www-nlpir.nist.gov/related_projects/muc/proceedings/muc_7_proceedings/overview.html.

References

  1. Allinson, J.: OpenART: Open Metadata for Art Research at the Tate. Bull. Am. Soc. Inf. Sci. Technol. 38(3), 43–48 (2012)

    Article  Google Scholar 

  2. Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E.R., Mitchell, T.M.: Toward an Architecture for Never-Ending Language Learning. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence, pp. 1306–1313 (2010)

    Google Scholar 

  3. Carriero, Valentina Anita., et al.: ArCo: The Italian cultural heritage knowledge graph. In: Ghidini, C., et al. (eds.) ISWC 2019. LNCS, vol. 11779, pp. 36–52. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30796-7_3

    Chapter  Google Scholar 

  4. Cimiano, Philipp, Völker, Johanna: Text2Onto. In: Montoyo, Andrés, Muńoz, Rafael, Métais, Elisabeth (eds.) NLDB 2005. LNCS, vol. 3513, pp. 227–238. Springer, Heidelberg (2005). https://doi.org/10.1007/11428817_21

    Chapter  Google Scholar 

  5. Crofts, N., Doerr, M., Gill, T., Stead, S., Stiff, M.: Definition of the CIDOC conceptual reference model. ICOM/CIDOC Documentation Standards Group. CIDOC CRM Special Interest Group 5 (2008)

    Google Scholar 

  6. van Dalen-Oskam, K., et al.: Named entity recognition and resolution for literary studies. Comput. Linguist. Netherlands J. 4, 121–136 (2014)

    Google Scholar 

  7. Diaz, G.I., Fokoue, A., Sadoghi, M.: EmbedS: Scalable, ontology-aware graph embeddings. In: Proceedings of the EDBT Conference, pp. 433–436 (2018)

    Google Scholar 

  8. Dijkshoorn, C., et al.: The Rijksmuseum collection as linked data. Semant. Web 9(2), 221–230 (2018)

    Article  Google Scholar 

  9. Ehrmann, M., Colavizza, G., Rochat, Y., Kaplan, F.: Diachronic evaluation of NER systems on old newspapers. In: Proceedings of the 13th Conference on Natural Language Processing (KONVENS 2016), pp. 97–107 (2016)

    Google Scholar 

  10. Galárraga, L., Razniewski, S., Amarilli, A., Suchanek, F.M.: Predicting Completeness in Knowledge Bases. In: Proceedings of the 10th ACM International Conference on Web Search and Data Mining, pp. 375–383 (2017)

    Google Scholar 

  11. Hao, J., Chen, M., Yu, W., Sun, Y., Wang, W.: Universal representation learning of knowledge bases by jointly embedding instances and ontological concepts. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1709–1719 (2019)

    Google Scholar 

  12. Hellmund, T., et al.: Introducing the HERACLES ontology-semantics for cultural heritage management. Heritage 1(2), 377–391 (2018)

    Article  Google Scholar 

  13. Jain, N., Krestel, R.: Who is Mona L.? Identifying mentions of artworks in historical archives. In: Doucet, Antoine, Isaac, Antoine, Golub, Koraljka, Aalberg, Trond, Jatowt, Adam (eds.) TPDL 2019. LNCS, vol. 11799, pp. 115–122. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30760-8_10

    Chapter  Google Scholar 

  14. Lehmann, J., et al.: DBpedia- A large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6(2), 167–195 (2015)

    Article  Google Scholar 

  15. Ling, X., Weld, D.S.: Fine-grained entity recognition. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence, pp. 94–100 (2012)

    Google Scholar 

  16. Mahdisoltani, F., Biega, J., Suchanek, F.: YAGO3: a knowledge base from multilingual Wikipedias. In: 7th Biennial Conference on Innovative Data Systems Research. CIDR Conference (2014)

    Google Scholar 

  17. Navigli, R., Velardi, P.: Learning domain ontologies from document warehouses and dedicated web sites. Comput. Linguist. 30(2), 151–179 (2004)

    Article  MATH  Google Scholar 

  18. Rodriquez, K.J., Bryant, M., Blanke, T., Luszczynska, M.: Comparison of named entity recognition tools for raw OCR text. In: Konvens, pp. 410–414 (2012)

    Google Scholar 

  19. Shin, J., Wu, S., Wang, F., De Sa, C., Zhang, C., Ré, C.: Incremental knowledge base construction using DeepDive. In: Proceedings of the VLDB Endowment International Conference on Very Large Data Bases, vol. 8, p. 1310 (2015)

    Google Scholar 

  20. Tsai, C.T., Mayhew, S., Roth, D.: Cross-lingual named entity recognition via Wikification. In: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, pp. 219–228 (2016)

    Google Scholar 

  21. Van Hooland, S., Verborgh, R.: Linked Data for Libraries, Archives and Museums: How to Clean, Link and Publish Your Metadata. Facet Publishing, London (2014)

    Google Scholar 

  22. Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)

    Article  Google Scholar 

  23. Yang, H., Callan, J.: A metric-based framework for automatic taxonomy induction. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pp. 271–279. Association for Computational Linguistics (2009)

    Google Scholar 

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Acknowledgement

I am thankful to my advisor Ralf Krestel for his feedback and Felix Naumann and Fabian Suchanek for their valuable comments. I would also like to thank Elena Demidova for guidance and suggestions during revisions.

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Authors and Affiliations

  1. Hasso-Plattner-Institut, University of Potsdam, Potsdam, Germany

    Nitisha Jain

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  1. Nitisha Jain
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Correspondence to Nitisha Jain .

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Editors and Affiliations

  1. University of Erlangen-Nuremberg, Nuremberg, Germany

    Andreas Harth

  2. National Research Council, Rome, Italy

    Valentina Presutti

  3. Eurecom, Sophia Antipolis, France

    Raphaël Troncy

  4. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Maribel Acosta

  5. Institute for Information Business at WU Wien, Vienna, Austria

    Axel Polleres

  6. Institute for Information Business at WU Wien, Vienna, Austria

    Javier D. Fernández

  7. Siemens AG Österreich, Vienna, Austria

    Josiane Xavier Parreira

  8. Linköping University, Linköping, Sweden

    Olaf Hartig

  9. Aalborg University, Aalborg, Denmark

    Katja Hose

  10. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Michael Cochez

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Jain, N. (2020). Domain-Specific Knowledge Graph Construction for Semantic Analysis. In: Harth, A., et al. The Semantic Web: ESWC 2020 Satellite Events. ESWC 2020. Lecture Notes in Computer Science(), vol 12124. Springer, Cham. https://doi.org/10.1007/978-3-030-62327-2_40

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  • DOI: https://doi.org/10.1007/978-3-030-62327-2_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62326-5

  • Online ISBN: 978-3-030-62327-2

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