ETRI Journal

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

Ontology-lexicon-based question answering over linked data

  • Jabalameli, Mehdi (Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan) ;
  • Nematbakhsh, Mohammadali (Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan) ;
  • Zaeri, Ahmad (Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan)
  • Received : 2018.06.11
  • Accepted : 2019.07.29
  • Published : 2020.04.03
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, Linked Open Data has become a large set of knowledge bases. Therefore, the need to query Linked Data using question answering (QA) techniques has attracted the attention of many researchers. A QA system translates natural language questions into structured queries, such as SPARQL queries, to be executed over Linked Data. The two main challenges in such systems are lexical and semantic gaps. A lexical gap refers to the difference between the vocabularies used in an input question and those used in the knowledge base. A semantic gap refers to the difference between expressed information needs and the representation of the knowledge base. In this paper, we present a novel method using an ontology lexicon and dependency parse trees to overcome lexical and semantic gaps. The proposed technique is evaluated on the QALD-5 benchmark and exhibits promising results.

Keywords

References

  1. C. Unger et al., An Introduction to Question Answering over Linked Data, in Proc. Reason. Web. Reason. Web Big Data Era, Athens, Greece, Sept. 2014, pp. 100-140.
  2. K. Höffner et al., Survey on challenges of question answering in the semantic web, Semant. Web J. 8 (2016), 895-920. https://doi.org/10.3233/SW-160247
  3. J. P. McCrae and C. Unger, Design patterns for engineering the ontology-lexicon interface, in Towar. Multiling. Semant. Web SE-2, P. Buitelaar and P. Cimiano (eds), Springer, Berlin Heidelberg, 2014, pp. 15-30.
  4. A. Bonifati, W. Martens, and T. Timm, DARQL: Deep Analysis of SPARQL Queries, in Companion Proc. Web Conf., Lyon, France, Apr. 2018, pp. 187-190.
  5. G. Xiao et al., Efficient Handling of SPARQL OPTIONAL for OBDA, in Semant. Web - ISWC 2018, D. Vrandecic et al. (eds), Springer International Publishing, Cham, 2018, pp. 354-373.
  6. P. Cimiano et al., Towards portable natural language interfaces to knowledge bases-The case of the ORAKEL system, Data Knowl. Eng. 65 (2008), 325-354. https://doi.org/10.1016/j.datak.2007.10.007
  7. C. Unger and P. Cimiano, Pythia: compositional meaning construction for ontology-based question answering on the semantic web, in Proc. Natural Language Process. Inf. Syst., Alicante, Spain, June 2011, pp. 153-160.
  8. C. Unger et al., Template-Based Question Answering over RDF Data, in Proc. 21st Int. Conf. World Wide Web, Lyon, France, Apr. 2018, pp. 639-648.
  9. A. Abujabal et al., Automated template generation for question answering over knowledge graphs, in Proc. Int. Conf. World Wide Web, Perth, Australia, Apr. 2017, pp. 1191-1200.
  10. D. Punjani et al., Template-Based Question Answering over Linked Geospatial Data, in Proc. Work. Geogr. Inf. Retr. - GIR'18, Seattle, WA, USA, Nov. 2018, pp. 7:1-10.
  11. J.-D. Kim and K. B. Cohen, Natural language query processing for SPARQL generation: A prototype system for SNOMED-CT, in Proc. BioLINK 2013 Roles Text Min. Biomed. Knowl. Discov. Transl. Med., Berlin, Germany, July 2017, pp. 32-38.
  12. L. Biermann, S. Walter, and P. Cimiano, A guided template-based question answering system over knowledge graphs, in Proc. 21st Int. Conf. Knowl. Eng. Knowl. Manag., Nancy, France, Nov. 2018, pp. 1-4.
  13. J. Berant et al., Semantic parsing on freebase from question-answer pairs, in Proc. Empir. Methods Nat. Lang. Process., Seattle, WA, USA, Oct. 2013, pp. 1533-1544.
  14. Q. Cai and A. Yates, Large-scale semantic parsing via schema matching and lexicon extension, in Proc. 51st Annu. Meet. Assoc. Comput. Linguist. (Volume 1 Long Pap.), Sofia, Bulgaria, Aug. 2013, pp. 423-433.
  15. M. Yahya et al., Robust question answering over the web of linked data, in Proc. 22nd ACM Int. Conf. Conf. Inf. Knowl. Manag. - CIKM '13, San Francisco, CA, USA, Oct. 2013, pp. 1107-1116.
  16. S. He, Question answering over linked data using first-order logic, in Proc. Conf. Empir. Methods Nat. Lang. Process, Doha, Qatar, Oct. 2014, pp. 1092-1103.
  17. S. Shekarpour et al., SINA: Semantic interpretation of user queries for question answering on interlinked data, J. Web Semant. 30 (2015), 39-51. https://doi.org/10.1016/j.websem.2014.06.002
  18. A. Freitas et al., Querying linked data graphs using semantic relatedness: A vocabulary independent approach, Data Knowl. Eng. 88 (2013), 126-141. https://doi.org/10.1016/j.datak.2013.08.003
  19. C. Unger et al., Question answering over linked data (QALD-5), in Proc. Work Notes Conf. Labs Evaluation Forum, Toulouse, France, Sept, 2015, pp. 1-10.
  20. C. Zhu et al., Semantic Technology, in A Graph Traversal Based Approach to Answer Non-Aggregation Questions over DBpedia, G. Qi (ed), Springer International Publishing, Cham, 2016, pp. 219-234.
  21. T. Hamon, N. Grabar, and F. Mougin, Querying biomedical linked data with natural language questions, Semant. Web 8 (2017), 581-599. https://doi.org/10.3233/SW-160244
  22. D. Mamgai et al., An improved automated question answering system from lecture videos, in Proc. 2nd Int. Conf. Commun. Comput. Netw., Chandigarh, India, 2018, pp. 653-659.
  23. M. Devi and M. Dua, ADANS: an agriculture domain question answering system using ontologies, in Int. Greater Noida, India, May, Conf. Comput. Commun. Autom., Greater Noida, India, 2017, pp. 122-127.
  24. C. D. Manning et al., The stanford CoreNLP natural language processing toolkit, in Proc. Annu. Meet. Assoc. Comput. Linguist. Syst. Demonstr., Baltimore, MD, USA, June, 2014, pp. 55-60.
  25. P. N. Mendes et al., DBpedia spotlight : Shedding light on the web of documents, in Proc. 7th Int. Conf. Semant. Syst., Graz, Austria, Sept. 2011, pp. 1-8.
  26. G. A. Miller, WordNet: a lexical database for English, Commun. ACM 38 (1995), 39-41. https://doi.org/10.1145/219717.219748
  27. C. Unger et al., A Lemon Lexicon for DBpedia, in Proc. 2013th Int. Conf. NLP & DBpedia, Sydney, Australia, Oct. 2013, pp. 103-108.
  28. S. Ruseti et al., QAnswer - enhanced entity matching for question answering over linked data, in Conf. Labs Evaluation forum, Toulouse, France, Sept. 2015.
  29. R. Beaumont, B. Grau, and A. L. Ligozat, SemGraphQA@ QALD-5: LIMSI Participation at QALD-5@CLEF, in Conf. Labs Evaluation forum, Toulouse, France, 2015.
  30. P. Baudis and J. Sedivy, Modeling of the question answering task in the YodaQA system in Conf. Labs Evaluation forum, Toulouse, France, Sept, 2015, pp. 222-228.
  31. S. Walter, C. Unger, and P. Cimiano, M-ATOLL: A framework for the lexicalization of ontologies in multiple languages, in Int. Semantic Web Conf., Riva del Garda, Italy, Oct. 2014, pp. 472-476.
  32. S. Walter, C. Unger, and P. Cimiano, Automatic acquisition of adjective lexicalizations of restriction classes: a machine learning approach, J. Data Semant. 6 (2016), 113-123.

Cited by

  1. Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review vol.12, pp.5, 2021, https://doi.org/10.3390/info12050200
  2. Comprehensive Knowledge Archive Network harvester improvement for efficient open-data collection and management vol.43, pp.5, 2020, https://doi.org/10.4218/etrij.2020-0298
  3. A Knowledge-Based Sense Disambiguation Method to Semantically Enhanced NL Question for Restricted Domain vol.12, pp.11, 2020, https://doi.org/10.3390/info12110452
  4. SParseQA: Sequential word reordering and parsing for answering complex natural language questions over knowledge graphs vol.235, 2020, https://doi.org/10.1016/j.knosys.2021.107626