Abstract
In various research domains, data providers themselves annotate their own data with keywords from a controlled vocabulary. However, since selecting keywords requires extensive knowledge of the domain and the controlled vocabulary, even data providers have difficulty in selecting appropriate keywords from the vocabulary. Therefore, we propose a method for recommending relevant keywords in a controlled vocabulary to data providers. We focus on a keyword definition, and calculate the similarity between an abstract text of data and the keyword definition. Moreover, considering that there are unnecessary words in the calculation, we extract CorKeD (Corpus-based Keyword Decisive) words from a target domain corpus so that we can measure the similarity appropriately. We conduct an experiment on earth science data, and verify the effectiveness of extracting the CorKeD words, which are the terms that better characterize the domain.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
Chemistry : Journal of the American Chemical Society
Physics : The European physical journal
Biology : International journal of biological sciences, Journal of evolutionary biology.
- 7.
References
Olsen, L.M., Major, G., Shein, K., Scialdone, J., Ritz, S., Stevens, T., Morahan, M., Aleman, A., Vogel, R., Leicester, S., Weir, H., Meaux, M., Grebas, S., Solomon, C., Holland, M., Northcutt, T., Restrepo, R.A., Bilodeau, R.: NASA/Global Change Master Directory (GCMD) Earth Science Keywords. Version 8.0.0.0.0 (2013)
Tuarob, S., Pouchard, L.C., Giles, C.L.: Automatic tag recommendation for metadata annotation using probabilistic topic modeling. In: JCDL, pp. 239–248 (2013)
Shimizu, T., Sueki, T., Yoshikawa, M.: Supporting keyword selection in generating earth science metadata. In: COMPSAC, pp. 603–604 (2013)
Kubo, J., Tsuji, K., Sugimoto, S.: Automatic term recognition using the corpora of the different academic areas (in Japanese). J. Jpn Soc. Inf. Knowl. 20(1), 15–31 (2010)
Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: RecSys, pp. 61–68 (2009)
Chernyak, E.: An approach to the problem of annotation of research publications. In: WSDM, pp. 429–434 (2015)
Santos, A.P., Rodrigues, F.: Multi-label hierarchical text classification using the acm taxonomy. In: EPIA, pp. 553–564 (2009)
Lu, Y.T., Yu, S.I., Chang, T.C., Hsu, J.Y.J.: A content-based method to enhance tag recommendation. In: IJCAI, pp. 2064–2069 (2009)
Belem, F., Martins, E., Pontes, T., Almeida, J., Goncalves, M.: Associative tag recommendation exploiting multiple textual features. In: SIGIR, pp. 1033–1042 (2011)
Paik, J.H.: A novel TF-IDF weighting scheme for effective ranking. In: SIGIR, pp. 343–352 (2013)
Utiyama, M., Chujo, K., Yamamoto, E., Isahara, H.: A comparison of measures for extracting domain-specific lexicons for english education (in Japanese). J. Nat. Lang. Process. 11(3), 165–197 (2004)
Uchimoto, K., Sekine, S., Murata, M., Ozaku, H., Isahara, H.: Term recognition using corpora from different fields. Terminology 6(2), 233–256 (2001)
Salton, G.: Automatic text processing: the transformation, analysis, and retrieval of information by computer. Addison-Wesley, Boston (1989)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Ramage, D., Hall, D., Nallapati, R., Manning, C.D.: Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora. In: EMNLP, pp. 248–256 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ishida, Y., Shimizu, T., Yoshikawa, M. (2015). A Keyword Recommendation Method Using CorKeD Words and Its Application to Earth Science Data. In: Zuccon, G., Geva, S., Joho, H., Scholer, F., Sun, A., Zhang, P. (eds) Information Retrieval Technology. AIRS 2015. Lecture Notes in Computer Science(), vol 9460. Springer, Cham. https://doi.org/10.1007/978-3-319-28940-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-28940-3_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28939-7
Online ISBN: 978-3-319-28940-3
eBook Packages: Computer ScienceComputer Science (R0)