바로가기메뉴

본문 바로가기 주메뉴 바로가기

Design of a Knowledge Framework for Structured Journalism Service based on Scientific Column Database

Journal of the Korean Society for Library and Information Science / Journal of the Korean Society for Library and Information Science, (P)1225-598X; (E)2982-6292
2015, v.49 no.1, pp.341-360
https://doi.org/10.4275/KSLIS.2015.49.1.341



  • Downloaded
  • Viewed

Abstract

This paper proposes a noble service architecture based on scientific infographic as well as semi-automatic knowledge process for ‘KISTI’s Scent of Science’ database, which has been highly credited as a science journalism service in Korea. Unlike other specialized scientific databases for domain experts and scientists, the database aims at providing comprehensible and intuitive information about various important scientific concepts which may seem not to be easily understandable to general public. In order to construct a knowledge-base from the database, we deeply analyze the traits of the database and then establish a semi-automatic approach to identify and extract various scientific intelligence from its contents. Furthermore, this paper defines a scientific infographic service platform based on the knowledge-base by offering its detailed structure, methods and characteristics, which shows a progressive future direction for science journalism service.

keywords
과학저널리즘, 지식화, 인포그래픽, 구조화된 저널리즘, 지식서비스, Science Journalism, Knowledge Processing, Infographic, Structured Journalism, Knowledge Service

Reference

1.

강정수. 2014. 뉴욕타임스 혁신보고서의 교훈: 멋지게 실패하자! 슬로우 뉴스.

2.

김두희. 2015. 동아사이언스. [onlilne] <http://www.dongascience.com/>

3.

박종인. 2012. 사이언스 저널리즘. Communication Books.

4.

KISTI, 2015. KISTI의 과학향기. [online] <http://scent.ndsl.kr>

5.

Bankoff, J. 2015. VOX MEDIA. [online] <http://www.voxmedia.com/>

6.

Choi, S. P., Chun, H. W., Jeong, C. H., Song, S. K., and Jung, H. 2012. SINDI-WALKS:Workbench for PLOT-based Technological Information Extraction and Management. Green Computing and Communications (GreenCom).

7.

Choi, S. P., Song, S. K., Jung, Geierhos, H., M., and Myaeng, S. H. 2012. Scientific literature retrieval based on terminological paraphrases using predicate argument tuple. SoftTech 2012.

8.

Choi, S. P., Lee, S., Jung, H., and Song, S. 2014. An intensive case study on kernel-based relation extraction. Multimed. Tools Appl, 71(2), 741-767.

9.

Huffington, A. 2015. The Huffington Post. [online] <http://www.huffingtonpost.com/>

10.

Kobyliński, Ł., and Przepiórkowski, A. 2008. Definition Extraction with Balanced Random Forests. Advances in Natural Language Processing 2008, 237-247.

11.

Peretti, J. 2015. BuzzFeed. [online] <http://www.buzzfeed.com/>

12.

Riloff, E., and Jones, R. 1999. Learning dictionaries for information extraction by multi-level bootstrapping. AAAI/IAAI, pp. 474-479. [1] The New York Times. 2014. NYT Innovation Report 2014.

13.

Soderland, S. 1999. Learning information extraction rules for semi-structured and free text.Machine Learning, 34(1-3).

14.

Song, S., Oh, H., Myaeng, S. H., Choi, S. P., Chun, H., Choi, Y., and Jeong, C. 2011. Procedural Knowledge Extraction on MEDLINE Abstracts. Active Media Technology 2011, 345-354.

15.

Tarling, J. 2013. Storylines vs object oriented news. Top Drawer Sausage. [online]<http://topdrawersausage.net/2013/07/07/storylines-vs-object-oriented-news/>

Journal of the Korean Society for Library and Information Science