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SchenQL: A Concept of a Domain-Specific Query Language on Bibliographic Metadata

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Digital Libraries at the Crossroads of Digital Information for the Future (ICADL 2019)

Abstract

Information access needs to be uncomplicated, users rather use incorrect data which is easily received than correct information which is harder to obtain. Querying bibliographic metadata from digital libraries mainly supports simple textual queries. A user’s demand for answering more sophisticated queries could be fulfilled by the usage of SQL. As such means are highly complex and challenging even for trained programmers, a domain-specific query language is needed to provide a straightforward way to access data.

In this paper we present SchenQL, a simple query language focused on bibliographic metadata in the area of computer science while using the vocabulary of domain-experts. By facilitating a plain syntax and fundamental aggregate functions, we propose an easy-to-learn domain-specific query language capable of search and exploration. It is suitable for domain-experts as well as casual users while still providing the possibility to answer complicated queries.

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References

  1. Amaral, V., Helmer, S., Moerkotte, G.: A visual query language for HEP analysis. In: Nuclear Science Symposium Conference Record, vol. 2, pp. 829–833 (2003)

    Google Scholar 

  2. Amer-Yahia, S., Lakshmanan, L.V.S., Yu, C.: SocialScope: enabling information discovery on social content sites. In: CIDR 2009 (2009)

    Google Scholar 

  3. Ballard, B.W., Stumberger, D.E.: Semantic acquisition in TELI: a transportable, user-customized natural language processor. In: ACL, pp. 20–29 (1986)

    Google Scholar 

  4. Baeza-Yates, R.A., Ribeiro-Neto, B.A.: Modern Information Retrieval - The Concepts and Technology Behind Search, Second edn. Pearson Education Ltd., Harlow (2011). ISBN 978-0-321-41691-9

    Google Scholar 

  5. Bates, M.J.: Task force recommendation 2.3: research and design review: improving user access to library catalog and portal information: final report (version 3). In: Proceedings of the Bicentennial Conference on Bibliographic Control for the New Millennium (2003)

    Google Scholar 

  6. Beall, J.: The weaknesses of full-text searching. J. Acad. Librarianship 34(5), 439–443 (2008)

    Article  Google Scholar 

  7. Berget, G., Sandnes, F.R.: Why textual search interfaces fail: a study of cognitive skills needed to construct successful queries. Inf. Res. 24(1), n1 (2019)

    Google Scholar 

  8. Bloehdorn, S., et al.: Ontology-based question answering for digital libraries. In: Kovács, L., Fuhr, N., Meghini, C. (eds.) ECDL 2007. LNCS, vol. 4675, pp. 14–25. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74851-9_2

    Chapter  Google Scholar 

  9. Borodin, A., Kiselev, Y., Mirvoda, S., Porshnev, S.: On design of domain-specific query language for the metallurgical industry. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2015. CCIS, vol. 521, pp. 505–515. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18422-7_45

    Chapter  Google Scholar 

  10. Buffereau, B., Picouet, P.: STIL: an extended resource description framework and an advanced query language for metadatabases. In: Li Lee, M., Tan, K.-L., Wuwongse, V. (eds.) DASFAA 2006. LNCS, vol. 3882, pp. 849–858. Springer, Heidelberg (2006). https://doi.org/10.1007/11733836_62

    Chapter  Google Scholar 

  11. Crawford, W.: MARC for Library Use: Understanding the USMARC Formats. Knowledge Industry Publications, Inc. (1994)

    Google Scholar 

  12. Collberg, C.S.: A fuzzy visual query language for a domain-specific web search engine. In: Hegarty, M., Meyer, B., Narayanan, N.H. (eds.) Diagrams 2002. LNCS (LNAI), vol. 2317, pp. 176–190. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-46037-3_20

    Chapter  Google Scholar 

  13. http://www.core.edu.au/conference-portal

  14. Curtiss, M., et al.: Unicorn: a system for searching the social graph. PVLDB 6(11), 1150–1161 (2013)

    Article  Google Scholar 

  15. Dries, A., Nijssen, S., De Raedt, L.: BiQL: a query language for analyzing information networks. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 147–165. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31830-6_11

    Chapter  Google Scholar 

  16. Guidi, F., Schena, I.: A query language for a metadata framework about mathematical resources. In: Asperti, A., Buchberger, B., Davenport, J.H. (eds.) MKM 2003. LNCS, vol. 2594, pp. 105–118. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36469-2_9

    Chapter  MATH  Google Scholar 

  17. Hirsch, J.E.: An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. 102(46), 16569–16572 (2005)

    Article  Google Scholar 

  18. Kreutz, C.K., Wolz, M., Schenkel, R.: SchenQL - A Domain-Specific Query Language on Bibliographic Metadata. In: CoRR abs/1906.06132 (2019)

    Google Scholar 

  19. Leser, U.: A query language for biological networks. In: ECCB/JBI 2005, p. 39 (2005)

    Google Scholar 

  20. Ley, M.: DBLP - some lessons learned. PVLDB 2(2), 1493–1500 (2009)

    Article  Google Scholar 

  21. Li, Y., Yang, H., Jagadish, H.V.: Constructing a generic natural language interface for an XML database. In: Ioannidis, Y., et al. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 737–754. Springer, Heidelberg (2006). https://doi.org/10.1007/11687238_44

    Chapter  Google Scholar 

  22. Lim, E.-P., Lu, Y.: Distributed query processing for clustered and bibliographic databases. In: DASFAA, pp. 441–450 (1997)

    Google Scholar 

  23. Madaan, A.: Domain specific multi-stage query language for medical document repositories. PVLDB 6(12), 1410–1415 (2013)

    Google Scholar 

  24. Parr, T.J., Quong, R.W.: ANTLR: a predicated- LL(k) parser generator. Softw. Pract. Exper. 25(7), 789–810 (1995)

    Google Scholar 

  25. Quoc, L., Mikolov, T.: Distributed representations of sentences and documents. In: ICML, pp. 1188–1196 (2014)

    Google Scholar 

  26. Rohil, M.K., Rohil, R.K., Rohil, D., Runthala, A.: Natural language interfaces to domain specific knowledge bases: an illustration for querying elements of the periodic table. In: ICCI*CC 2018, pp. 517–523 (2018)

    Google Scholar 

  27. San Martín, M., Gutiérrez, C., Wood, P.T.: SNQL: a social networks query and transformation language. In: AMW 2011 (2011)

    Google Scholar 

  28. Schaefer, A., Jordan, M., Klas, C.-P., Fuhr, N.: Active support for query formulation in virtual digital libraries: a case study with DAFFODIL. In: Rauber, A., Christodoulakis, S., Tjoa, A.M. (eds.) ECDL 2005. LNCS, vol. 3652, pp. 414–425. Springer, Heidelberg (2005). https://doi.org/10.1007/11551362_37

    Chapter  Google Scholar 

  29. https://www.semanticscholar.org/

  30. Seo, J., Guo, S., Lam, M.S.: SociaLite: an efficient graph query language based on datalog. IEEE Trans. Knowl. Data Eng. 27(7), 1824–1837 (2015)

    Article  Google Scholar 

  31. Sheng, L., Özsoyoǧlu, Z.M., Özsoyoǧlu, G.: A graph query language and its query processing. In: ICDE 1999, pp. 572–581 (1999)

    Google Scholar 

  32. Tian, H., Sunderraman, R., Calin-Jageman, R., Yang, H., Zhu, Y., Katz, P.S.: NeuroQL: a domain-specific query language for neuroscience data. In: Grust, T., et al. (eds.) EDBT 2006. LNCS, vol. 4254, pp. 613–624. Springer, Heidelberg (2006). https://doi.org/10.1007/11896548_46

    Chapter  Google Scholar 

  33. https://www.wikidata.org/wiki/Wikidata:Main_Page

  34. Xu, B., et al.: NADAQ: natural language database querying based on deep learning. IEEE Access 7, 35012–35017 (2019)

    Article  Google Scholar 

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Correspondence to Christin Katharina Kreutz .

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Kreutz, C.K., Wolz, M., Schenkel, R. (2019). SchenQL: A Concept of a Domain-Specific Query Language on Bibliographic Metadata. In: Jatowt, A., Maeda, A., Syn, S. (eds) Digital Libraries at the Crossroads of Digital Information for the Future. ICADL 2019. Lecture Notes in Computer Science(), vol 11853. Springer, Cham. https://doi.org/10.1007/978-3-030-34058-2_22

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  • DOI: https://doi.org/10.1007/978-3-030-34058-2_22

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