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KEFT: Knowledge Extraction and Graph Building from Statistical Data Tables

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Advances in Computational Collective Intelligence (ICCCI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1287))

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Abstract

Data provided by statistical models are commonly represented by textual, tabular or graphical form in documents (reports, articles, posters and presentations). These documents are often available in PDF format. Even though it makes accessing a particular information more difficult, it is interesting to process the PDF documents directly. We present KEFT, a solution in the statistical domain and we describe the fully functional pipeline to constructing a knowledge graph by extracting entities and relations from statistical Data Tables. We showcase how this approach can be used to construct a knowledge graph from different statistical studies.

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Notes

  1. 1.

    https://stanfordnlp.github.io/CoreNLP/index.html.

  2. 2.

    https://wiki.dbpedia.org/.

  3. 3.

    https://www.banque-france.fr/sites/default/files/media/2016/11/24/enquete-triennale-principaux-resultats.pdf.

  4. 4.

    https://eren.univ-paris13.fr/index.php/en/epidemiological-studies.html.

  5. 5.

    https://eren.univ-paris13.fr/index.php/en/.

References

  1. Saporta, G.: Probabilités, analyse des données et Statistique, 3rd edn. Technip, Paris (2011)

    MATH  Google Scholar 

  2. Heiberger, R.-M., Holland, B.: Statistical Analysis and Data Display, 2nd edn. Springer, New York (2015). https://doi.org/10.1007/978-1-4939-2122-5

    Book  MATH  Google Scholar 

  3. Klahold, A., Fathi, M.: Knowledge discovery from text (KDT). Computer Aided Writing, pp. 83–115. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-27439-9_8

    Chapter  Google Scholar 

  4. Lu, W., Zhang, Z., Lou, R., Dai, H., Yang, S., Wei, B.: Mining RDF from tables in Chinese encyclopedias. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds.) NLPCC -2015. LNCS (LNAI), vol. 9362, pp. 285–298. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25207-0_24

    Chapter  Google Scholar 

  5. Tekli, J., Charbel, N., Chbeir, R.: Building semantic trees from XML documents. J. Web Semant. 37(38), 1–24 (2016)

    Article  Google Scholar 

  6. Souili, A., Cavallucci, D., Rousselot, F.: Natural language processing (NLP) – a solution for knowledge extraction from patent unstructured data. Procedia Eng. 131, 635–643 (2015)

    Article  Google Scholar 

  7. Ronzano, F., Saggion, H.: Knowledge extraction and modeling from scientific publications. In: González-Beltrán, A., Osborne, F., Peroni, S. (eds.) SAVE-SD 2016. LNCS, vol. 9792, pp. 11–25. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-53637-8_2

    Chapter  Google Scholar 

  8. Klampfl, S., Jack, K., Kern, R.: A comparison of two unsupervised table recognition methods from digital scientific articles. D-Lib Mag. 20 (2014). CNRI Acct

    Google Scholar 

  9. Rastan, R., Paik, H.-Y., Shepherd, J., Ryu, S.-H., Beheshti, A.: TEXUS: table extraction system for PDF documents. In: Wang, J., Cong, G., Chen, J., Qi, J. (eds.) ADC 2018. LNCS, vol. 10837, pp. 345–349. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92013-9_30

    Chapter  Google Scholar 

  10. Ahmad, R., Afzal, M.-T., Qadir, M.-A.: Information extraction from PDF sources based on rule-based system using integrated formats. In: Sack, H., Dietze, S., Tordai, A., Lange, C. (eds.) SemWebEval 2016. CCIS, vol. 641, pp. 293–308. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46565-4_23

    Chapter  Google Scholar 

  11. Gentile, A.L., Gruhl, D., Ristoski, P., Welch, S.: Personalized knowledge graphs for the pharmaceutical domain. In: Ghidini, C., et al. (eds.) ISWC 2019. LNCS, vol. 11779, pp. 400–417. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30796-7_25

    Chapter  Google Scholar 

  12. Ermilov, I., Auer, S., Stadler, C.: User-driven semantic mapping of tabular data. In: Proceedings of the 9th International Conference on Semantic Systems, pp. 105–112. ACM. New York (2013). https://doi.org/10.1145/2506182.2506196

  13. Shigarov, A.-O.: Table understanding using a rule engine. Procedia Eng. Expert Syst. Appl. 42, 929–937 (2015)

    Article  Google Scholar 

  14. Shigarov, A., Altaev, A., Mikhailov, A., Paramonov, V., Cherkashin, E.: TabbyPDF: web-based system for PDF table extraction. In: Damaševičius, R., Vasiljevienė, G. (eds.) ICIST 2018. CCIS, vol. 920, pp. 257–269. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99972-2_20

    Chapter  Google Scholar 

  15. Meneton, P., et al.: A global view of the relationships between the main behavioural and clinical cardiovascular risk factors in the GAZEL prospective cohort. PLoS ONE 911, 01–20 (2016). Public Library of Science (PLoS). Andrea Icks

    Google Scholar 

  16. Barton, A., Rosier, A., Burgun, A., Ethier, J.-F.: The cardiovascular disease ontology. In: Frontiers in Artificial Intelligence and Applications, vol. 267, pp. 409–414. IOS Press (2014)

    Google Scholar 

  17. Divakar, H.-R., Prakash, B.-R., Mamatha, M.: An ontology based system for healthcare people to prevent cardiovascular diseases. Int. J. Recent Technol. Eng. 8, 983–988 (2019). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP

    Google Scholar 

  18. Gedzelman, S., Simonet, M., Bernhard, D., Diallo, D., Palmer, P.: Building an ontology of cardio-vascular diseases for concept-based information retrieval. In: 2005 Computers in Cardiology, Lyon, France, pp. 255–258. IEEE (2005). https://doi.org/10.1109/CIC.2005.1588085

  19. Wu, M., Liu, Y., Kang, H., Zheng, S., Li, J., Hou, L.: Building a controlled vocabulary for standardizing precision medicine terms. CoRR, abs/1807.01000, pp. 1–4 (2018)

    Google Scholar 

  20. Nguyen, P., Kertkeidkachorn, N., Ichise, R., Takeda, H.: MTab: matching tabular data to knowledge graph with probability models. In: CEUR Workshop Proceedings (eds.) Proceedings of the 14th International Workshop on Ontology Matching Co-Located with the 18th International Semantic Web Conference (ISWC 2019), Auckland, New Zealand, 26 October 2019, vol. 2536, pp. 191–192. CEUR-WS.org (2019)

    Google Scholar 

  21. Guo, X., Chen, Y., Chen, J., Du, X.: ITEM: extract and integrate entities from tabular data to RDF knowledge base. In: Du, X., Fan, W., Wang, J., Peng, Z., Sharaf, M.A. (eds.) APWeb 2011. LNCS, vol. 6612, pp. 400–411. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20291-9_45

    Chapter  Google Scholar 

  22. Cremaschi, M., Rula, A., Siano, A., De Paoli, F.: MantisTable: a tool for creating semantic annotations on tabular data. In: Hitzler, P., et al. (eds.) ESWC 2019. LNCS, vol. 11762, pp. 18–23. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32327-1_4

    Chapter  Google Scholar 

  23. Cannaviccio, M., Ariemma, L., Barbosa, D., Merialdo. P.: Leveraging Wikipedia table schemas for knowledge graph augmentation. In: Proceedings of the 21st International Workshop on the Web and Databases-WebDB 2018, pp. 1–6. ACM Press (2018). https://doi.org/10.1145/3201463.3201468

  24. Kertkeidkachorn, N., Ichise, R.: T2KG: an end-to-end system for creating knowledge graph from unstructured text. In: AAAI Workshops (eds.) The Workshops of the The Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, California, USA, 4–9 February 2017, vol. WS-17, pp. 743–749. AAAI Press (2017)

    Google Scholar 

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Author information

Authors and Affiliations

  1. BPH Center - INSERM U1219, Team ERIAS, Univ. Bordeaux, 33000, Bordeaux, France

    Rabia Azzi & Gayo Diallo

  2. LIMICS - INSERM UMRS 1142, Univ. Paris 13, Sorbonne Université, 93017, Bobigny Cedex, France

    Sylvie Despres

Authors
  1. Rabia Azzi
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  2. Sylvie Despres
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  3. Gayo Diallo
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Corresponding authors

Correspondence to Rabia Azzi or Gayo Diallo .

Editor information

Editors and Affiliations

  1. Wroclaw University of Economics and Business, Wrocław, Poland

    Marcin Hernes

  2. Wrocław University of Science and Technology, Wrocław, Poland

    Krystian Wojtkiewicz

  3. University of Newcastle, Newcastle, Australia

    Edward Szczerbicki

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Azzi, R., Despres, S., Diallo, G. (2020). KEFT: Knowledge Extraction and Graph Building from Statistical Data Tables. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_57

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  • DOI: https://doi.org/10.1007/978-3-030-63119-2_57

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63118-5

  • Online ISBN: 978-3-030-63119-2

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