Skip to main content
SpringerLink
Log in
Book cover

China Conference on Knowledge Graph and Semantic Computing

CCKS 2018: Knowledge Graph and Semantic Computing. Knowledge Computing and Language Understanding pp 136–142Cite as

Survey on Schema Induction from Knowledge Graphs

  • Conference paper
  • First Online:

  • 983 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 957))

Abstract

With the rapid growth of knowledge graphs, schema induction, as a task of extracting relations or constraints from a knowledge graph for the classes and properties, becomes more critical and urgent. Schema induction plays an important role to facilitate many applications like integrating, querying and maintaining knowledge graphs. To provide a comprehensive survey of schema induction, in this paper, we overview existing schema induction approaches by mainly considering their learning methods, the types of learned axioms and the external resources that may be used during the learning process. Based on the comparison, we point out the challenges and directions for schema induction.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://www.w3.org/TR/owl-ref/.

  2. 2.

    Set operations mean the intersection, union or negation of classes.

  3. 3.

    Although some existing works like [7] could generate association rules from KGs, we do not include them in this paper since their goal is not to generate schemas.

References

  1. Bühmann, L., Lehmann, J.: Universal OWL axiom enrichment for large knowledge bases. In: ten Teije, A., et al. (eds.) EKAW 2012. LNCS (LNAI), vol. 7603, pp. 57–71. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33876-2_8

    Chapter  Google Scholar 

  2. Bühmann, L., Lehmann, J.: Pattern based knowledge base enrichment. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 33–48. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41335-3_3

    Chapter  Google Scholar 

  3. Cimiano, P.: Ontology Learning and Population from Text - Algorithms, Evaluation and Applications. Springer, New York (2006). https://doi.org/10.1007/978-0-387-39252-3

    Book  Google Scholar 

  4. Ell, B., Hakimov, S., Cimiano, P.: Statistical induction of coupled domain/range restrictions from RDF knowledge bases. In: van Erp, M., Hellmann, S., McCrae, J.P., Chiarcos, C., Choi, K.-S. (eds.) ISWC 2016. LNCS, vol. 10579, pp. 27–40. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-68723-0_3

    Chapter  Google Scholar 

  5. Fleischhacker, D., Völker, J.: Inductive learning of disjointness axioms. In: Meersman, R., et al. (eds.) OTM 2011. LNCS, vol. 7045, pp. 680–697. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25106-1_20

    Chapter  Google Scholar 

  6. Fleischhacker, D., Völker, J., Stuckenschmidt, H.: Mining RDF data for property axioms. In: Meersman, R., et al. (eds.) OTM 2012. LNCS, vol. 7566, pp. 718–735. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33615-7_18

    Chapter  Google Scholar 

  7. Galarraga, L., Teflioudi, C., Hose, K., Suchanek, F.M.: Fast rule mining in ontological knowledge bases with AMIE+. VLDB J. 24(6), 707–730 (2015)

    Article  Google Scholar 

  8. Gao, H., Qi, G., Ji, Q.: Schema induction from incomplete semantic data. In: Intelligent Data Analysis (2018, to appear)

    Google Scholar 

  9. Hellmann, S., Lehmann, J., Auer, S., Sheth, A.: Learning of OWL class descriptions on very large knowledge bases. Int. J. Semant. Web Inf. Syst. 5(2), 25–48 (2009)

    Article  Google Scholar 

  10. Irny, R., Kumar, P.S.: Mining inverse and symmetric axioms in linked data. In: JIST, pp. 215–231 (2017)

    Google Scholar 

  11. Lehmann, J., Hitzler, P.: A refinement operator based learning algorithm for the \(\cal{ALC}\) description logic. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds.) ILP 2007. LNCS (LNAI), vol. 4894, pp. 147–160. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78469-2_17

    Chapter  MATH  Google Scholar 

  12. Lehmann, J., Hitzler, P.: Concept learning in description logics using refinement operators. Mach. Learn. 78(1–2), 203–250 (2010)

    Article  MathSciNet  Google Scholar 

  13. Meilicke, C., Völker, J., Stuckenschmidt, H.: Learning disjointness for debugging mappings between lightweight ontologies. In: Gangemi, A., Euzenat, J. (eds.) EKAW 2008. LNCS (LNAI), vol. 5268, pp. 93–108. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87696-0_11

    Chapter  Google Scholar 

  14. Muñoz, E., Nickles, M.: Mining cardinalities from knowledge bases. In: Benslimane, D., Damiani, E., Grosky, W.I., Hameurlain, A., Sheth, A., Wagner, R.R. (eds.) DEXA 2017. LNCS, vol. 10438, pp. 447–462. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64468-4_34

    Chapter  Google Scholar 

  15. Rizzo, G., d’Amato, C., Fanizzi, N., Esposito, F.: Terminological cluster trees for disjointness axiom discovery. In: Blomqvist, E., Maynard, D., Gangemi, A., Hoekstra, R., Hitzler, P., Hartig, O. (eds.) ESWC 2017. LNCS, vol. 10249, pp. 184–201. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58068-5_12

    Chapter  Google Scholar 

  16. Sheu, P., Yu, H., Ramamoorthy, C.V., Joshi, A.K.: Machine Learning Methods for Ontology Mining. Wiley-IEEE Press, Hoboken (2010)

    Google Scholar 

  17. Subhashree, S., Irny, R., Sreenivasa Kumar, P.: Review of approaches for linked data ontology enrichment. In: Negi, A., Bhatnagar, R., Parida, L. (eds.) ICDCIT 2018. LNCS, vol. 10722, pp. 27–49. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-72344-0_2

    Chapter  Google Scholar 

  18. Toepper, G., Knuth, M., Sack, H.: DBpedia ontology enrichment for inconsistency detection. In: I-SEMANTICS, pp. 33–40 (2012)

    Google Scholar 

  19. Völker, J., Vrandečić, D., Sure, Y., Hotho, A.: Learning disjointness. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 175–189. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72667-8_14

    Chapter  Google Scholar 

  20. Völker, J., Fleischhacker, D., Stuckenschmidt, H.: Automatic acquisition of class disjointness. J. Web Semant. 35, 124–139 (2015)

    Article  Google Scholar 

  21. Völker, J., Niepert, M.: Statistical schema induction. In: Antoniou, G., et al. (eds.) ESWC 2011. LNCS, vol. 6643, pp. 124–138. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21034-1_9

    Chapter  Google Scholar 

  22. Zhu, M., Gao, Z., Pan, J.Z., Zhao, Y., Ying, X., Quan, Z.: Tbox learning from incomplete data by inference in BelNet+. Knowl.-Based Syst. 75(C), 30–40 (2014)

    Google Scholar 

Download references

Acknowledgements

This paper is sponsored by NSFC 61602259 and U1736204.

Author information

Authors and Affiliations

  1. School of Modern Posts and Institute of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing, China

    Qiu Ji

  2. School of Computer Science and Engineering, Southeast University, Nanjing, China

    Guilin Qi, Huan Gao & Tianxing Wu

Authors
  1. Qiu Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guilin Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huan Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tianxing Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qiu Ji .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Jun Zhao

  2. Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Frank van Harmelen

  3. Tsinghua University, Beijing, China

    Jie Tang

  4. Institute of Software, Chinese Academy of Sciences, Beijing, China

    Xianpei Han

  5. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Quan Wang

  6. Xihua University, Chendu, China

    Xianyong Li

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ji, Q., Qi, G., Gao, H., Wu, T. (2019). Survey on Schema Induction from Knowledge Graphs. In: Zhao, J., Harmelen, F., Tang, J., Han, X., Wang, Q., Li, X. (eds) Knowledge Graph and Semantic Computing. Knowledge Computing and Language Understanding. CCKS 2018. Communications in Computer and Information Science, vol 957. Springer, Singapore. https://doi.org/10.1007/978-981-13-3146-6_12

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-3146-6_12

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3145-9

  • Online ISBN: 978-981-13-3146-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation