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TableProcessor: The Tool for the Analysis and the Interpretation of Web Tables to Create the Geo Knowledge Base of Kazakhstan

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Artificial Intelligence in Models, Methods and Applications (AIES 2022)

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 457))

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Abstract

The data retrieval from the tables is actual task, since the number of publicly available high-quality tables on the Internet containing useful relational data reaches hundreds of millions. The search engines usually ignore the basic semantics of such structures when indexing and do not work well with tabular data. The web tables do not adhere to any single presentation scheme, what, of course, is a minus for the task. This article proposes the method for disclosing the semantics of tables, followed by a description in the form of a knowledge base suitable for automatic analysis and based on the heuristic selection of a candidate object for the main thematic entity with the subsequent calculation of the suitability assessment of each selected entity for the objects in the lines of the table and similarity assessment between the pairs of candidate objects to determine the best likelihood assessments of entities by line. The tool was developed for interpreting semi-structured tables from the open website of the Bureau of National Statistics of the Republic of Kazakhstan. The files JSON were obtained, for which the title, the main column of objects, the attributes, their values ​​and the text after the tables are automatically detected. These files are ready for the usage in GIS to display the attribute data of the regions of Kazakhstan. The TableProcessor tool was developed for interpreting and analyzing web tables, based on the knowledge base on geo-objects of Kazakhstan will be formed, which will be supplemented with new knowledge.

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References

  1. Barakhnin, V.B., Fedotov, A.M.: Building models of documentary and factographic retrieval in digital libraries. Autom. Doc. Math. Linguist. 48(6), 296–304 (2014)

    Google Scholar 

  2. Wittgenstein, L.: Logisch-philosophische abhandlung. Annalen der Naturphilosophie, vol. XIV(3/4). Verlag Unesma, S.185–262 (1921)

    Google Scholar 

  3. Chen, P.P.: The entity-relational model. Toward a unified view of data. ACM TODS 1, 9–36 (1976)

    Article  Google Scholar 

  4. Cafarella, M., Halevy, A., Lee, H., Madhavan, J., Yu, C., Wang, Z.D., Wu, E.: Ten years of webtables. PVLDB 11(12), 2140–2149 (2018). https://doi.org/10.14778/3229863.3240492

  5. Mulwad, V., Finin, T., Joshi, A.: Semantic message passing for generating linked data from tables. In: International Semantic Web Conference, Lecture Notes in Computer Science, pp. 363–378. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41335-3_23

  6. Yakout, M., Ganjam, K., Chakrabarti, K., Chaudhuri, S.: InfoGather: entity augmentation and attribute discovery by holistic matching with web tables. In: Proceedings of SIGMOD, pp. 97–108 (2012)

    Google Scholar 

  7. Hayes, P.: RDF Semantics. W3C Recommendation (2004). http://www.w3.org/TR/rdf-mt/

  8. Limaye, G., Sarawagi, S., Chakrabarti, S.: Annotating and searching web tables using entities types and relationships. PVLDB 3(1–2), 1338–1347 (2010)

    Google Scholar 

  9. Lehmberg, O., Ritze, D., Meusel, R., Bizer, C.: A large public corpus of web tables containing time and context metadata. In: Proceedings of the 25th International Conference Companion on World Wide Web, WWW ’16 Companion, pp. 75–76 (2016)

    Google Scholar 

  10. Zhang, Sh., Balog, K.: Ad hoc table retrieval using semantic similarity. In: Proceedings of the 2018 World Wide Web Conference, WWW ’18, pp. 1553–1562 (2018)

    Google Scholar 

  11. Chen, X., Chiticariu, L., Danilevsky, M., Evfimievski, A., Sen, P.: A rectangle mining method for understanding the semantics of financial tables. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), pp. 268–273 (2017). https://doi.org/10.1109/ICDAR.2017.52

  12. Kunihiro, T., Masafumi, O., Shinji, N., Takeshi, O.: An efficient probabilistic approach for semantically annotating tables. In: 33rd AAAI Conference on Artificial Intelligence, AAAI 2019 (2019)

    Google Scholar 

  13. Zhang, Z.: Effective and efficient semantic table interpretation using tableminer+. Semantic Web 8(6), 921–957 (2017)

    Article  Google Scholar 

  14. Nugumanova, A., Akhmed-Zaki, D., Mansurova, M., Baiburin, Y., Maulit, A.: NMF-based approach to automatic term extraction. Expert Syst. Appl. 199, 117179 (2022)

    Article  Google Scholar 

  15. Mansurova, M., Barakhnin, V., Kyrgyzbayeva, M., Kadyrbek, N.: Named entity extraction model based on the random walk method SIST 2021. In: 2021 IEEE International Confer-ence on Smart Information Systems and Technologies, 9465992 (2021)

    Google Scholar 

  16. Zhang, Sh., Balog K.: Web table extraction, retrieval and augmentation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (2019)

    Google Scholar 

  17. Hignette, G., Buche, P, Dibie-Barthélemy, J., Haemmerlé, O.: Fuzzy annotation of web data tables driven by a domain ontology. In: Proceedings of the Z. Zhang / Effective and Efficient Semantic Table Interpretation using TableMiner+ 31 6th European Semantic Web Conference on The Semantic Web: Research and Applications, ESWC’2009, pages 638– 653, Berlin, Heidelberg (2009). Springer-Verlag. https://doi.org/10.1007/978-3-642-02121-3_47

  18. Buche, P., Dibie-Barthélemy, J., Ibanescu, L., Soler, L.: Fuzzy web data tables integration guided by an ontological and terminological resource. IEEE Trans. Knowl. Data Eng. 25(4), 805–819 (2013). https://doi.org/10.1109/TKDE.2011.245

    Article  Google Scholar 

  19. Cremaschi, M., Paoli, F.D., Rula, A., Blerina, S.: A fully automated approach to a complete Semantic Table Interpretation. Futur. Gener. Comput. Syst. 112, 478–500 (2020)

    Article  Google Scholar 

  20. Chabot, Y., Labbe, T., Liu, J., Troncy, R.: An end-to-end context-free tabular data semantic annotation system. In: Proceedings of the Semantic Web Challenge on Tabular Data To Knowledge Graph Matching Co-Located with the 18th International Semantic Web Conference, ISWC 2019, CEUR-WS.org (2019)

    Google Scholar 

  21. Graphical Model https://www.sciencedirect.com/topics/computer-science/graphical-model

  22. Chen, J., Jiménez-Ruiz, E., Horrocks, I., Sutton, C.C.: Embedding the semantics of web tables for column type prediction. In: 33rd AAAI Conference on Artificial Intelligence, AAAI 2019 (2019)

    Google Scholar 

  23. Chen, X., Chitikariu, L., Danilevsky, M., Evfimievski, A., Sen, P.: Box analysis method for understanding the semantics of financial tables. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), pp. 268–273 (2017). https://doi.org/10.1109/ICDAR.2017.52. https://www.cs.cornell.edu/~xlchen/resources/papers/TableExtraction.pdf

  24. Chen, Zh., Cafarella, M.: Automatic web spreadsheet data extraction. In: SSW ‘13: Proceedings of the 3rd International Workshop on Semantic Search Over the WebAugust 2013, Article No.: 1, pp. 1–8. https://doi.org/10.1145/2509908.2509909

  25. Barowy, D.W., Gulwani, S., Hart, T., Zorn, B.: FlashRelate: extracting relational data from semi-structured spreadsheets using examples. In: PLDI ‘15: Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 218–228 (2015). https://doi.org/10.1145/2737924.2737952

  26. Benno, K., Boncz PA, Jacopo, U.: Extracting Novel Facts from Tables for Knowledge Graph Completion (Extended version), CoRR, abs/1907.00083 (2019). http://arxiv.org/abs/1907.00083

  27. Pearl, J.: Probabilistic reasoning in intelligent systems - networks of plausible inference. Morgan Kaufmann Publishers Inc. (1989)

    Google Scholar 

  28. Varish, M., Tim, F., Anupam, J.: Semantic message passing for generating linked data from tables. In: International Semantic Web Conference, Lecture Notes in Computer Science, pages 363–378. Springer Berlin Heidelberg (2013). https://doi.org/10.1007/978-3-642-41335-3_23

  29. Nugumanova, A.B., Apayev, K.S., Baiburin, Y.M., Mansurova, M., Ospan, A.G.: Qurma: the pipeline for extracting tables to replenish the knowledge base. Bulletin of the KazNU. Mathematics, Mechanics, Computer Science series, [S.l.], vol. 114, no. 2 (2022). ISSN 2617-4871. https://bm.kaznu.kz/index.php/kaznu/article/view/1086

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Acknowledgements

This work was carried out and sponsored within the framework of the scientific project AP09261344 “Development of methods for automatic extraction of spatial objects from heterogeneous sources for information support of geographic information systems”.

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Authors and Affiliations

  1. Federal Research Center for Information and Computing Technologies, Novosibirsk, Russia

    Vladimir Barakhnin, Irina Grigorieva & Olga Kozhemyakina

  2. Al Farabi Kazakh National University, Almaty, Kazakhstan

    Madina Mansurova & Assel Ospan

Authors
  1. Vladimir Barakhnin
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  2. Madina Mansurova
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  3. Irina Grigorieva
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  4. Olga Kozhemyakina
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  5. Assel Ospan
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Corresponding author

Correspondence to Madina Mansurova .

Editor information

Editors and Affiliations

  1. Yuri Gagarin State Technical University of Saratov, Saratov, Russia

    Olga Dolinina

  2. Saint Petersburg, Russia

    Igor Bessmertny

  3. Yuri Gagarin State Technical University of Saratov, Saratov, Russia

    Alexander Brovko

  4. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

  5. Yuri Gagarin State Technical University of Saratov, Saratov, Russia

    Vitaly Pechenkin

  6. Yuri Gagarin State Technical University of Saratov, Saratov, Russia

    Alexey Lvov

  7. Technical Science, Novosibirsk Institute of the Software Systems, Novosibirsk Oblast, Russia

    Vadim Zhmud

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Barakhnin, V., Mansurova, M., Grigorieva, I., Kozhemyakina, O., Ospan, A. (2023). TableProcessor: The Tool for the Analysis and the Interpretation of Web Tables to Create the Geo Knowledge Base of Kazakhstan. In: Dolinina, O., et al. Artificial Intelligence in Models, Methods and Applications. AIES 2022. Studies in Systems, Decision and Control, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-22938-1_15

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