Journal of the Korean Society for information Management (정보관리학회지)

Korean Society for Information Management (한국정보관리학회)
권호 표지
DOI QR코드

DOI QR Code

Design and Implementation of Tag Coupling-based Boolean Query Matching System for Ranked Search Result

태그결합을 이용한 불리언 검색에서 순위화된 검색결과를 제공하기 위한 시스템 설계 및 구현

  • 김용 (전북대학교 문헌정보학과, 비판적사고와 논술연구소) ;
  • 주원균 (한국과학기술정보연구원(KISTI) NTIS센터)
  • Received : 2012.11.17
  • Accepted : 2012.12.19
  • Published : 2012.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

Since IR systems which adopt only Boolean IR model can not provide ranked search result, users have to conduct time-consuming checking process for huge result sets one by one. This study proposes a method to provide search results ranked by using coupling information between tags instead of index weight information in Boolean IR model. Because document queries are used instead of general user queries in the proposed method, key tags used as queries in a relevant document are extracted. A variety of groups of Boolean queries based on tag couplings are created in the process of extracting queries. Ranked search result can be extracted through the process of matching conducted with differential information among the query groups and tag significance information. To prove the usability of the proposed method, the experiment was conducted to find research trend analysis information on selected research information. Aslo, the service based on the proposed methods was provided to get user feedback for a year. The result showed high user satisfaction.

불리언 검색만을 제공하는 정보시스템들은 순위화된 검색 결과를 제공하지 않아 이용자들이 많은 시간을 들여 수많은 결과를 일일이 확인해야하는 단점이 있다. 따라서 본 연구에서는 불리언 검색 모델의 단점을 극복하기 위한 방법으로써 불리언 검색에서 적용되고 있는 색인 가중치 정보 대신에 태그 간의 결합 관계 정보를 이용하여 순위화된 검색 결과를 제공하기 위한 시스템을 제안한다. 본 연구에서 제안하고 있는 방법은 일반적인 키워드 질의 대신에 문서를 질의로 사용하기 때문에 해당 문서에서 질의로 사용하는 핵심태그를 추출한다. 질의 생성 과정에서는 태그결합도에 따라 다양한 그룹의 불리언 질의를 생성하고, 매칭 과정에서는 해당 질의어 그룹 간에 차별성 정보와 태그 중요도 정보를 이용하여 순위화를 처리한다. 본 연구에서 제안하고 있는 방법의 유용성을 평가하기 위하여 선정된 연구정보와 관련된 동향분석정보를 추출하는 과정에 적용하여 실험을 수행하였다. 또한 제안된 방법에 대한 이용자 평가를 위하여 다수의 이용자들을 대상으로 약 1년간 서비스를 제공하였으며 그 결과 높은 이용자 만족도를 확보할 수 있다고 조사되었다.

Keywords

Acknowledgement

Supported by : 한국연구재단

References

  1. 김은희, 정영미 (2010). 사용자 태그와 중심성 지수를 이용한 블로그 검색 성능 향상에 관한 연구. 정보관리학회지, 27(1), 61-77. http://dx.doi.org/10.3743/KOSIM.2010.27.1.061(Kim, Eun-Hee, & Chung, Young-Mee (2010). Enhancing the performance of blog retrieval by user tagging and social network analysis. Journal of the Korean Society for Information Management, 27(1), 61-77. http://dx.doi.org/10.3743/KOSIM.2010.27.1.061.)
  2. 이성재, 조수선 (2011). 위키피디아 기반의 의미 연관성을 이용한 태깅된 웹 이미지의 검색순위 조정. 멀티미디어학회논문지, 14(11), 1491-1499.(Lee, Seongjae, & Cho, Soosun (2011). Tagged web image retrieval re-ranking with Wikipedia-based semantic relatedness. Journal of Korea Multimedia Society, 14(11), 1491-1499.) https://doi.org/10.9717/kmms.2011.14.11.1491
  3. 이수상, 이순영 (2009). 차세대 검색서비스의 속성에 관한 연구. 정보관리학회지, 26(4), 93-112. http://dx.doi.org.10.3743/KOSIM.2009.26.4.093(Lee, Soo-Sang, & Lee, Soon-Young (2009). A study on the features of the next generation search services. Journal of the Korean Society for Information Management, 26(4), 93-112. http://dx.doi.org.10.3743/KOSIM.2009.26.4.093.)
  4. 이정미 (2007). 폭소노미의 개념적 접근과 웹 정보서비스에의 적용. 한국비블리아학회지, 8(2), 141-159.(Lee, Jeong-Mee (2007). A conceptual access to the folksonomy and its application on the web information services. Journal of Korean Biblia Society for Library and Information Science, 18(2), 141-159.)
  5. 엄태영, 김우주, 박상언 (2010). 태그 네트워크를 이용한 개인화 북마크 추천시스템. 한국전자거래학회지, 15(4), 181-195.(Eom, Tae Young, Kim, Wooju, & Park, Sangun (2010). Personalized bookmark recommendation system using tag network. Journal of Society for e-Business Studies, 15(4), 181-195.)
  6. 임영석, 이강표, 김현우, 안재민, 김형주 (2011). 사용자 활동 점수에 기반한 태그 검색 개선. 정보과학회논문지: 컴퓨팅의 실제 및 레터, 17(3), 150-158.(Lim, Young-Seok, Lee, Kang-Pyo, Kim, Hyun-Woo, Ahn, Jae-Min, & Kim, Hyoung-Joo (2011). Improving tag search based on user activeness scores. Journal of KIISE: Computing Practices and Letters, 17(3), 150-158.)
  7. Baeza-Yates, R., & Riberiro-Neto, B. (1999). Modern information retrieval. New York: Addison-Wesley Longman.
  8. Bao, S., Xue, G., Wu, X., Yu, Y., Fei, B., & Su, Z. (2007). Optimizing web search using social annotations. Proceedings of the 16th international conference on World Wide Web (WWW '07), 501-510. http://dx.doi.org/10.1145/1242572.1242640
  9. Begelman, G, Keller, P., & Smadja, F. (2005.2.22). Automated tag clustering: Improving search and exploration in the tag space. Paper presented at the Collaborative Web Tagging Workshop at WWW 06, Edinburgh, UK. Retrieved from http://www.semanticmetadata.net/hosted/taggingws-www2006-files/20.pdf
  10. Bookstein, A. (1980). Fussy requests: An approach to weighted boolean searches. Journal of the American Society for Information Science, 31(4), 275-279.
  11. Carmagnola, F., Cena, F., Cortassa, O., Gena, C., & Torre, I. (2007). Towards a tag-based user model: How can user model benefit from tags? Lecture Notes in Computer Science, 4511, 445-449. http://dx.doi.org/10.1007/978-3-540-73078-1_62
  12. Cattuto, C., Benz, D., Hotho, A., & Stummen, G. (2008). Semantic grounding of tag relatedness in social bookmarking systems. Lecture Notes in Computer Science, 5318, 615-631. http://dx.doi.org/10.1007/978-3-540-88564-1_39
  13. Chen, H., Tim, T., & Fye, D. (1995). Automatic thesaurus generation for an electronic community system. Journal of the American Society for Information Science, 46(3), 175-193. https://doi.org/10.1002/(SICI)1097-4571(199504)46:3<175::AID-ASI3>3.0.CO;2-U
  14. Choi, Yun-Seon (2010). Implications of social tagging for digital libraries. Journal of the Korean Society for Information Management, 27(2), 225-239. http://dx.doi.org/10.3743/KOSIM.2010.27.2.225
  15. Heymann, P., Koutrika, G., & Garcia-Molina, H. (2008). Can social bookmarking improve web search? Proceedings of the 2008 International Conference on Web Search and Data Mining (WSDM '08), 195-206. http://dx.doi.org/10.1145/1341531.1341558
  16. Jeh, G., & Widom, J. (2002). SimRank: A measure of structural-context similarity. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '02), 538-543. http://dx.doi.org/10.1145/775047.775126
  17. Jing, Y., & Croft, W. B. (1994). An association thesaurus for information retrieval. Proceedings of the RIAO '94 Conference, 146-160.
  18. Kent, A., Berry, M. M., Luehrs Jr., F. U., & Perry, J. W. (1955). Machine literature searching VIII: Operational criteria for designing information retrieval systems. American Documentation, 6(2), 93-101. http://dx.doi.org/10.1002/asi.5090060209
  19. Lee, Bog-Gi (1999). A new document ranking algorithm in boolean retrieval system. Journal of Kyungwon College, 21, 159-165.
  20. Moffat, A., & Zobel, J. (2008). Rank-biased precision for measurement of retrieval effectiveness. ACM Transactions on Information Systems, 27(1), 1-26. http://dx.doi.org/10.1145/1416950.1416952
  21. Nakamoto, R. Y., Nakajima, S., Miyazaki, J., Uemura, S., Kato, H., & Inagaki, Y. (2008). Reasonable tag-based collaborative filtering for social tagging systems. Proceedings of the 2nd ACM Workshop on Information Credibility on the Web (WICOW '08), 11-18. http://dx.doi.org/10.1145/1458527.1458533
  22. National Discovery for Science Leaders (NDSL). Retrieved from http://www.ndsl.kr
  23. National R&D Outcome Service. Retrieved from http://roots.ntis.go.kr
  24. National Science and Technology Information Service (NTIS). Retrieved from http://www.ntis.go.kr
  25. NDSL Trend Service. Retrieved from http://radar.ndsl.kr
  26. Page, L., Brin, S., Motwani, R., & Winograd, T. (1998). The PageRank citation ranking: Bringing order to the web. Proceedings of the 7th International World Wide Web Conference Brisbane, 161-172.
  27. Salton, G., & McGill, M. J. (1983). Introduction to modern information retrieval. New York: McGraw Hill.
  28. Salton, G., Fox, E. A., & Wu, H. (1983). Extended boolean information retrieval. Communications of the ACM, 36(11), 1022-1036.
  29. Schmitz, P. (2006.5.22). Inducing ontology from flickr tags. Paper presented at the Collaborative Web Tagging Workshop at WWW 06, Edinburgh, UK. Retrieved from http://www.semanticmetadata.net/hosted/taggingws-www2006-files/22.pdf
  30. TREC (Text Retrieval Conference). Retrieved from http://trec.nist.gov/
  31. Waller, W. G., & Kraft, D. H. (1979). A mathematical model for a weighted boolean retrieval system. Information Processing and Management, 15(5), 235-245. https://doi.org/10.1016/0306-4573(79)90030-X
  32. Xu, Z., Fu, Y., Mao, J., & Su, D. (2006.5.22). Towards the semantic web: collaborative tag suggestions. Paper presented at the Collaborative Web Tagging Workshop at WWW 06, Edinburgh, UK. Retrieved from http://www.semanticmetadata.net/hosted/taggingws-www2006-files/13.pdf
  33. Yanbe, Y., Jatowt, A., Nakamura, S., & Tanaka, K. (2007). Can social bookmarking enhance search in the web? Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL '07), 107-116. http://dx.doi.org/10.1145/1255175.1255198
  34. Yi, K., & Chan, L. M. (2009). Linking folksonomy to library of congress subject headings: An exploratory study. Journal of Documentation, 65(6), 872-900. http://dx.doi.org/10.1108/00220410910998906