Skip to main content
SpringerLink
Log in

SRDF: A Novel Lexical Knowledge Graph for Whole Sentence Knowledge Extraction

  • Conference paper
  • First Online:
Language, Data, and Knowledge (LDK 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10318))

Included in the following conference series:

  • 1453 Accesses

Abstract

In this paper, we present a novel lexical knowledge graph called SRDF and describe an extraction system that automatically generates a SRDF graph from the Korean natural language sentence. In the semantic web, knowledge is expressed in the RDF triple form but natural language sentences consist of multiple relationships between the predicates and arguments. For this reason, we design a SRDF graph structure that combines open information extraction method with reification for the whole sentence knowledge extraction. In addition, to add semantics to a SRDF graph, we establish a link between the lexical argument and entity in ontological knowledge base using the Entity Linking system. The proposed knowledge graph is adaptable for many existing semantic web applications. We present the results of an experimental evaluation and demonstrate the use of SRDF graph in developing a Korean SPARQL template generation module in the OKBQA platform.

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

Access this chapter

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

Institutional subscriptions

Notes

  1. 1.

    Specifically, we used ETRI Korean NLP tool.

  2. 2.

    http://wisekb.kaist.ac.kr:8832/el_srdf.

  3. 3.

    http://4.okbqa.org.

References

  1. Ferrucci, D.A.: Introduction to “This is Watson”. IBM J. Res. Dev. 56(3.4), 1 (2012)

    Article  Google Scholar 

  2. Exobrain. http://www.exobrain.kr/exointro

  3. Wimalasuriya, D.C., Dou, D.: Ontology-based information extraction: an introduction and a survey of current approaches. J. Inf. Sci. 36, 306–323 (2012)

    Article  Google Scholar 

  4. Mintz, M., Bills, S., Snow, R., Jurafsky, D.: Distant supervision for relation extraction without labeled data. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, vol. 2, pp. 1003–1011 (2009)

    Google Scholar 

  5. Xu, W., Hoffmann, R., Zhao, L., Grishman, R.: Filling knowledge base gaps for distant supervision of relation extraction, vol. 2, pp. 665–670. ACL (2013)

    Google Scholar 

  6. Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: International Joint Conference on Artificial Intelligence, pp. 2670–2676 (2007)

    Google Scholar 

  7. Schmitz, M., Bart, R., Soderland, S., Etzioni, O.: Open language learning for information extraction. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 523–534. Association for Computational Linguistics (2012)

    Google Scholar 

  8. Del Corro, L., Gemulla, R.: Clausie: clause-based open information extraction. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 355–366. ACM (2013)

    Google Scholar 

  9. Fader, A., Zettlemoyer, L., Etzoini, O.: Open question answering over curated and extracted knowledge bases. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1156–1165. ACM (2014)

    Google Scholar 

  10. Banarescu, L., Bonial, C., Cai, S., Georgescu, M., Griffitt, K., Hermjakob, U., Knight, K., Palmer, M., Schneider, N.: Abstract meaning representation for sembanking. In: Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, pp. 178–186 (2013)

    Google Scholar 

  11. Martha, P., Dan, G., Paul, K.: The Proposition Bank: a corpus annotated with semantic roles. Comput. Linguist. J. 31, 71–106 (2005)

    Article  Google Scholar 

  12. Burns, G.A., Hermjakob, U., Ambite, J.L.: Abstract meaning representations as linked data. In: Groth, P., Simperl, E., Gray, A., Sabou, M., Krötzsch, M., Lecue, F., Flöck, F., Gil, Y. (eds.) ISWC 2016. LNCS, vol. 9982, pp. 12–20. Springer, Cham (2016). doi:10.1007/978-3-319-46547-0_2

    Chapter  Google Scholar 

  13. Wang, C., Xue, N., Pradhan, S., Pradhan, S.: A transition-based algorithm for AMR parsing. In: HLT-NAACL, pp. 366–375 (2015)

    Google Scholar 

  14. Prud, E., Seaborne, A.: SPARQL Query Language for RDF (2006)

    Google Scholar 

  15. Nguyen, V., Bodenreider, O., Sheth, A.: Don’t like RDF reification?: Making statements about statements using singleton property. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 759–770. ACM (2014)

    Google Scholar 

  16. Nam, S., Choi, G., Hahm, Y., Choi, K.: SRDF: extracting lexical knowledge graph for preserving sentence meaning. In: OKBQA 2016 (2016)

    Google Scholar 

  17. Kim, J., Choi, K.: KoEL: Korean entity linking system using sentence features and extended entity relations. Master’s thesis, KAIST, Daejeon, Korea, p. 40 (2017, in Korean)

    Google Scholar 

  18. OKBQA. http://www.okbqa.org

  19. Unger, C., Bühmann, L., Lehmann, J., Ngonga Ngomo, A.C., Gerber, D., Cimiano, P.: Template-based question answering over RDF data. In: Proceedings of the 21st International Conference on World Wide Web, pp. 639–648. ACM (2012)

    Google Scholar 

Download references

Acknowledgements

This work was supported by an Institute for Information & communications Technology Promotion (IITP) grant funded by the Korean government (MSIP) (2013-0-00109, WiseKB: Big data based self-evolving knowledge base and reasoning platform). This work was supported by an Industrial Strategic technology development program (10072064, Development of Novel Artificial Intelligence Technologies To Assist Imaging Diagnosis of Pulmonary, Hepatic, and Cardiac Diseases and Their Integration into Commercial Clinical PACS Platforms) funded by the Ministry of Trade Industry and Energy (MI, Korea). This work was supported by the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP (2015M3A9A7029735). This work was supported by an Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (R0124-16-0002, Emotional Intelligence Technology to Infer Human Emotion and Carry on Dialogue Accordingly).

Author information

Authors and Affiliations

  1. Machine Reading Lab, School of Computing, Semantic Web Research Center, KAIST, Daejeon, Republic of Korea

    Sangha Nam, GyuHyeon Choi & Key-Sun Choi

Authors
  1. Sangha Nam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. GyuHyeon Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Key-Sun Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Key-Sun Choi .

Editor information

Editors and Affiliations

  1. Universidad Politécnica de Madrid, Madrid, Spain

    Jorge Gracia

  2. Nanyang Technological University, Singapore, Singapore

    Francis Bond

  3. Insight Centre for Data Analytics, National University of Ireland, Galway, Galway, Ireland

    John P. McCrae

  4. Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland

    Paul Buitelaar

  5. Goethe-University Frankfurt, Frankfurt, Germany

    Christian Chiarcos

  6. University of Leipzig, Leipzig, Germany

    Sebastian Hellmann

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Nam, S., Choi, G., Choi, KS. (2017). SRDF: A Novel Lexical Knowledge Graph for Whole Sentence Knowledge Extraction. In: Gracia, J., Bond, F., McCrae, J., Buitelaar, P., Chiarcos, C., Hellmann, S. (eds) Language, Data, and Knowledge. LDK 2017. Lecture Notes in Computer Science(), vol 10318. Springer, Cham. https://doi.org/10.1007/978-3-319-59888-8_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59888-8_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59887-1

  • Online ISBN: 978-3-319-59888-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation