TL-Rank: A Blend of Text and Link Information for Measuring Similarity in Scientific Literature Databases
Summary :
This paper presents a novel similarity measure that computes similarity scores among scientific research papers. The text of a given paper in online scientific literature is often found to be incomplete in terms of its potential to be compared with others, which likely leads to inaccurate results. Our solution to this problem makes use of both text and link information of a paper in question for similarity scores in that the comparison text of the paper is strengthened by adding that of papers related to it. More accurate similarity scores can be computed by reinforcing the input with the citations of the paper as well as the citations included within the paper. The efficacy of the proposed measure is validated through our extensive performance evaluation study which demonstrates a substantial gain.
- Publication
- IEICE TRANSACTIONS on Information Vol.E95-D No.10 pp.2556-2559
- Publication Date
- 2012/10/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E95.D.2556
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Seok-Ho YOON, Ji-Su KIM, Sang-Wook KIM, Choonhwa LEE, "TL-Rank: A Blend of Text and Link Information for Measuring Similarity in Scientific Literature Databases" in IEICE TRANSACTIONS on Information,
vol. E95-D, no. 10, pp. 2556-2559, October 2012, doi: 10.1587/transinf.E95.D.2556.
Abstract: This paper presents a novel similarity measure that computes similarity scores among scientific research papers. The text of a given paper in online scientific literature is often found to be incomplete in terms of its potential to be compared with others, which likely leads to inaccurate results. Our solution to this problem makes use of both text and link information of a paper in question for similarity scores in that the comparison text of the paper is strengthened by adding that of papers related to it. More accurate similarity scores can be computed by reinforcing the input with the citations of the paper as well as the citations included within the paper. The efficacy of the proposed measure is validated through our extensive performance evaluation study which demonstrates a substantial gain.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E95.D.2556/_p
Copy
@ARTICLE{e95-d_10_2556,
author={Seok-Ho YOON, Ji-Su KIM, Sang-Wook KIM, Choonhwa LEE, },
journal={IEICE TRANSACTIONS on Information},
title={TL-Rank: A Blend of Text and Link Information for Measuring Similarity in Scientific Literature Databases},
year={2012},
volume={E95-D},
number={10},
pages={2556-2559},
abstract={This paper presents a novel similarity measure that computes similarity scores among scientific research papers. The text of a given paper in online scientific literature is often found to be incomplete in terms of its potential to be compared with others, which likely leads to inaccurate results. Our solution to this problem makes use of both text and link information of a paper in question for similarity scores in that the comparison text of the paper is strengthened by adding that of papers related to it. More accurate similarity scores can be computed by reinforcing the input with the citations of the paper as well as the citations included within the paper. The efficacy of the proposed measure is validated through our extensive performance evaluation study which demonstrates a substantial gain.},
keywords={},
doi={10.1587/transinf.E95.D.2556},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - TL-Rank: A Blend of Text and Link Information for Measuring Similarity in Scientific Literature Databases
T2 - IEICE TRANSACTIONS on Information
SP - 2556
EP - 2559
AU - Seok-Ho YOON
AU - Ji-Su KIM
AU - Sang-Wook KIM
AU - Choonhwa LEE
PY - 2012
DO - 10.1587/transinf.E95.D.2556
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E95-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2012
AB - This paper presents a novel similarity measure that computes similarity scores among scientific research papers. The text of a given paper in online scientific literature is often found to be incomplete in terms of its potential to be compared with others, which likely leads to inaccurate results. Our solution to this problem makes use of both text and link information of a paper in question for similarity scores in that the comparison text of the paper is strengthened by adding that of papers related to it. More accurate similarity scores can be computed by reinforcing the input with the citations of the paper as well as the citations included within the paper. The efficacy of the proposed measure is validated through our extensive performance evaluation study which demonstrates a substantial gain.
ER -
English
