Skip to main content
SpringerLink
Log in

Ontology based user query interpretation for semantic multimedia contents retrieval

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Users who are familiar with the existing keyword-based search have problems of not being able to configure the formal query because they don’t have generic knowledge on knowledge base when using the semantic-based retrieval system. User wants the search results which are more accurate and match the user’s search intents with the existing keyword-based search and the same search keyword without the need to recognize what technology the currently used retrieval system is based on to provide the search results. In order to do the semantic analysis of the ambiguous search keyword entered by users who are familiar with the existing keyword-based search, ontological knowledge base constructed based on refined meta-data is necessary, and the keyword semantic analysis technique which reflects user’s search intents from the well-established knowledge base and can generate accurate search results is necessary. In this paper, therefore, by limiting the knowledge base construction to multimedia contents meta-data, the applicable prototype has been implemented and its performance in the same environment as Smart TV has been evaluated. Semantic analysis of user’s search keyword is done, evaluated and recommended through the proposed ontological knowledge base framework so that accurate search results that match user’s search intents can be provided.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Chen 2010 et al (2010) “Effective and efficient keyword query interpretation using a hybrid graph.” Proc. 11th Int. Conf. Web Information Systems Engineering (WISE’10)

  2. El Sayad I et al (2012) Toward a higher-level visual representation for content-based image retrieval. Multimed Tool Appl 60(2):455–482

    Article  Google Scholar 

  3. Freebase, http://wiki.freebase.com/wiki/What_is_Freebase%3F

  4. Kim J-m et al (2011) Research for efficient search methods of IPTV broadcasting program. J Korean Inst Inf Technol 9:161–171

    Google Scholar 

  5. Lee D-G et al (2011) Social search algorithm considering recent interests of user. J Korean Inst Inf Technol 9:187–194

    Google Scholar 

  6. Lei Y et al (2006) SemSearch: a search engine for the semantic web. Managing Knowl World Networks LNCS 4248:238–245

    Article  Google Scholar 

  7. Mäkelä E (2005) “Survey of semantic search research”. In: Proceedings of the Seminar on Knowledge Management on the Semantic Web. Department of Computer Science, University of Helsinki

  8. Parthasarathy K et al (2011) “Algorithm for answer graph construction for keyword queries on RDF Data.” Proc. Int. World Wide Web Conf. 2011 (WWW2011)

  9. Rotter P (2012) Multimedia information retrieval based on pairwise comparison and its application to visual search. Multimed Tool Appl 60(3):573

    Article  Google Scholar 

  10. SPARQL Query Language (http://www.w3c.org/TR/rdf-sparql-query/), W3C, 2008

  11. Tran T et al (2007) Ontology-based interpretation of keywords for semantic search. LNCS 4825:238–345

    Google Scholar 

  12. Tran T et al (2007) Ontology-based interpretation of keywords for semantic search. Semant Web LNCS 4825:523–536

    Article  Google Scholar 

  13. Tran T et al (2009) “Top-k exploration of query candidates for efficient keyword search on Graph-Shaped (RDF) Data”. ICDE’09 Proceedings of the 2009 IEEE international Conference on Data Engineering, pp405–416

  14. Tran T et al (2009) Semantic search – using graph-structured semantic models for supporting the search process. Concept Structures: Leveraging Semant Technol LNCS 5662:48–65

    Google Scholar 

  15. Wang H et al (2008) “Q2Semantic: a lightweight keyword interface to semantic search.” Proc. 5th European Semantic Web Conf. Semantic Web: Research and Applications (ESWC’08), pp.584–598

  16. Zhou Q et al (2007) SPARK: adapting keyword query to semantic search. Semant Web LNCS 4825:694–707

    Article  Google Scholar 

Download references

Acknowledgment

This research was financially supported by the Ministry of Education, Science Technology (MEST) and National Research Foundation of Korea(NRF) through the Human Resource Training Project for Regional Innovation

Author information

Authors and Affiliations

  1. Electronics and Telecommunications Research Institute, Daejeon, Korea

    Moo-Hun Lee

  2. Department of Multimedia, Sungkyul University, Anyang-si, Korea

    Seungmin Rho

  3. Department of Computer Engineering, Hannam University, Daejeon, Korea

    Eui-In Choi

Authors
  1. Moo-Hun Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seungmin Rho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eui-In Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eui-In Choi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lee, MH., Rho, S. & Choi, EI. Ontology based user query interpretation for semantic multimedia contents retrieval. Multimed Tools Appl 73, 901–915 (2014). https://doi.org/10.1007/s11042-013-1383-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-013-1383-2

Keyword

Search

Navigation