Skip to main content

A General Process for the Semantic Annotation and Enrichment of Electronic Program Guides

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1029))

Abstract

Electronic Program Guides (EPGs) are usual resources aimed to inform the audience about the programming being transmitted by TV stations and cable/satellite TV providers. However, they only provide basic metadata about the TV programs, while users may want to obtain additional information related to the content they are currently watching. This paper proposes a general process for the semantic annotation and subsequent enrichment of EPGs using external knowledge bases and natural language processing techniques with the aim to tackle the lack of immediate availability of related information about TV programs. Additionally, we define an evaluation approach based on a distributed representation of words that can enable TV content providers to verify the effectiveness of the system and perform an automatic execution of the enrichment process. We test our proposal using a real-world dataset and demonstrate its effectiveness by using different knowledge bases, word representation models and similarity measures. Results showed that DBpedia and Google Knowledge Graph knowledge bases return the most relevant content during the enrichment process, while word2vec and fasttext models with Words Mover’s Distance as similarity function can be combined to validate the effectiveness of the retrieval task.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   64.99
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.

    http://www.w3.org/TR/annotation-model/.

  2. 2.

    https://www.directv.com.ec/movil/ProgramGuide/ProgramGuide.

  3. 3.

    https://scrapy.org/.

  4. 4.

    http://wiki.dbpedia.org/about/language-chapters.

  5. 5.

    https://radimrehurek.com/gensim/.

  6. 6.

    http://purl.org/ontology/po/.

  7. 7.

    http://www.openannotation.org/spec/core/20130208.

  8. 8.

    http://nerd.eurecom.fr/ontology.

  9. 9.

    http://marmotta.apache.org/.

References

  1. Ibrahim, A., Choi, H.J.: Role of annotation in electronic process guide (EPG). In: Future Generation Communication and Networking (FGCN 2007), vol. 2, pp. 569–572. IEEE (2007)

    Google Scholar 

  2. Saquicela, V., Espinoza-Mejía, M., Palacio, K., Albán, H.: Enriching electronic program guides using semantic technologies and external resources. In: 2014 XL Latin American Computing Conference (CLEI), pp. 1–8. IEEE (2014)

    Google Scholar 

  3. Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3(Feb), 1137–1155 (2003)

    Google Scholar 

  4. Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquisition 5(2), 199–220 (1993)

    Article  Google Scholar 

  5. Aroyo, L., Nixon, L., Miller, L.: Notube: the television experience enhanced by online social and semantic data. In: 2011 IEEE International Conference on Consumer Electronics-Berlin (ICCE-Berlin), pp. 269–273. IEEE (2011)

    Google Scholar 

  6. Yuan, X., Lai, W., Mei, T., Hua, X.S., Wu, X.Q., Li, S.: Automatic video genre categorization using hierarchical SVM. In: 2006 IEEE International Conference on Image Processing, pp. 2905–2908. IEEE (2006)

    Google Scholar 

  7. Smoliar, S.W., Zhang, H.: Content based video indexing and retrieval. IEEE Multimedia 1(2), 62–72 (1994)

    Article  Google Scholar 

  8. Wang, J., Duan, L., Xu, L., Lu, H., Jin, J.S.: TV ad video categorization with probabilistic latent concept learning. In: Proceedings of the International Workshop on Workshop on Multimedia Information Retrieval, pp. 217–226. ACM (2007)

    Google Scholar 

  9. Kunjithapatham, A., Rathod, P., Gibbs, S.J., Sheshagiri, M.: Enabling topic-based discovery of television programs. In: Proceedings of the International Workshop on Cross-Media Information Access and Mining (CIAM 2009), p. 43 (2009)

    Google Scholar 

  10. Macedo, P., Cardoso, J., Pinto, A.M.: Enriching electronic programming guides with web data. In: Proceeding of the 2nd International Workshop on Linked Media (LiME2014), Crete, Greece (2014)

    Google Scholar 

  11. Narducci, F., Musto, C., de Gemmis, M., Lops, P., Semeraro, G.: TV-program retrieval and classification: a comparison of approaches based on machine learning. Inf. Syst. Front. 20, 1157–1171 (2017)

    Article  Google Scholar 

  12. Rocchio, J.J.: Relevance feedback in information retrieval. In: The Smart Retrieval System-Experiments in Automatic Document Processing (1971)

    Google Scholar 

  13. Zhang, L., Thalhammer, A., Rettinger, A., Färber, M., Mogadala, A., Denaux, R.: The xLiMe system: cross-lingual and cross-modal semantic annotation, search and recommendation over live-TV, news and social media streams. J. Web Semant. 46, 20–30 (2017)

    Article  Google Scholar 

  14. Ristoski, P., Paulheim, H.: Semantic web in data mining and knowledge discovery: a comprehensive survey. Web Semant.: Sci. Serv. Agents World Wide Web 36, 1–22 (2016)

    Article  Google Scholar 

  15. Saquicela, V., Vilches-Blázquez, L.M., Corcho, O.: Adding semantic annotations into (geospatial) restful services. Int. J. Semant. Web Inf. Syst. 8(2), 51–71 (2012)

    Article  Google Scholar 

  16. Dong, X.L., et al.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, 24–27 August 2014, New York, NY, USA, pp. 601–610 (2014)

    Google Scholar 

  17. Daiber, J., Jakob, M., Hokamp, C., Mendes, P.N.: Improving efficiency and accuracy in multilingual entity extraction. In: Proceedings of the 9th International Conference on Semantic Systems (I-Semantics) (2013)

    Google Scholar 

  18. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_52

    Chapter  Google Scholar 

  19. Baeza-Yates, R., Ribeiro-Neto, B., et al.: Modern Information Retrieval, vol. 463. ACM Press, New York (1999)

    Google Scholar 

  20. Hinton, G., McClelland, J., Rumelhart, D.: Distributed representations. Inparallel distributed processing: explorations in the microstructure of cognition (1986)

    Google Scholar 

  21. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  22. Cardellino, C.: Spanish Billion Words Corpus and Embeddings, March 2016. http://crscardellino.me/SBWCE/

  23. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606 (2016)

  24. Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016)

  25. Mohammad, S.M., Hirst, G.: Distributional measures of semantic distance: A survey. arXiv preprint arXiv:1203.1858 (2012)

  26. Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: International Conference on Machine Learning, pp. 957–966 (2015)

    Google Scholar 

  27. Scharffe, F., de Bruijn, J.: A language to specify mappings between ontologies. In: SITIS, pp. 267–271 (2005)

    Google Scholar 

  28. Ratcliff, J.W., Metzener, D.E.: Pattern-matching-the gestalt approach. Dr Dobbs J. 13(7), 46 (1988)

    Google Scholar 

  29. Atasu, K., et al.: Linear-complexity relaxed word mover’s distance with GPU acceleration. CoRR abs/1711.07227 (2017). http://arxiv.org/abs/1711.07227

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Santiago Gonzalez-Toral .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gonzalez-Toral, S., Espinoza-Mejia, M., Palacio-Baus, K., Saquicela, V. (2019). A General Process for the Semantic Annotation and Enrichment of Electronic Program Guides. In: Villazón-Terrazas, B., Hidalgo-Delgado, Y. (eds) Knowledge Graphs and Semantic Web. KGSWC 2019. Communications in Computer and Information Science, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-030-21395-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-21395-4_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21394-7

  • Online ISBN: 978-3-030-21395-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics