Frontiers of Computer Science

, Volume 12, Issue 5, pp 1029–1031 | Cite as

The more irresistible Hi(SRIQ) for meta-modeling and meta-query answering

  • Zhenzhen Gu
  • Songmao Zhang


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This work was supported by the National Key Research and Development Program of China (2016YFB1000902) and the National Natural Science Foundation of China (Grant Nos. 61232015, 61621003).

Supplementary material

11704_2018_7105_MOESM1_ESM.ppt (178 kb)
Supplementary material, approximately 178 KB.


  1. 1.
    Calvanese D, Eiter T, Ortiz M. Answering regular path queries in expressive description logics via alternating tree-automata. Information and Computation, 2014, 237: 12–55MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Motik B. On the properties of metamodeling in OWL. Journal of Logic and Computation, 2007, 17(4): 617–637MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    De Giacomo G, Lenzerini M, Rosati R. Higher-order description logics for domain metamodeling. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2011, 183–188Google Scholar
  4. 4.
    Motz R, Rohrer E, Severi P. The description logic SHIQ with a flexible meta-modeling hierarchy. Journal of Web Semantics: Science, Services and Agents on the World Wide Web, 2015, 35(4): 214–234CrossRefGoogle Scholar
  5. 5.
    Gu Z, Zhang S. Querying large and expressive biomedical ontologies. In: Proceedings of IEEE International Conference on High Performance Computing and Communications. 2015, 491–496Google Scholar
  6. 6.
    Gu Z. Meta-modeling extension of Horn-SROIQ and query answering. In: Proceedings of International Workshop on Description Logics. 2016Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

Personalised recommendations