Garbage In, Garbage Out? An Empirical Look at Information Richness of LBD Input Types
Pages 369 - 372
Abstract
Literature-Based Discovery (LBD) which is a sub-discipline of text mining, aims to detect meaningful implicit knowledge linkages in digital libraries that have the potential in generating novel research hypotheses. The input can be considered as one of the most critical components of the LBD process as the entire knowledge discovery is solely dependent on the content and quality of input. However, there is no uniform selection of the input since different LBD studies have picked different input types (e.g., titles, abstracts, keywords). This emphasises the need for assessing the information richness of inputs to decide the most suited input type for LBD workflow. Therefore, this study focuses on a large-scale assessment of the information richness of different variants of popular LBD input types. Our observations are consistent with all of the five golden test cases in the discipline.
References
[1]
DC. Corrales, A. Ledezma, and JC. Corrales. 2015. A conceptual framework for data quality in knowledge discovery tasks (FDQ-KDT): A Proposal. JCP, Vol. 10, 6 (2015), 396--405.
[2]
K. Jha, G. Xun, Y. Wang, V. Gopalakrishnan, and A. Zhang. 2018. Concepts-bridges: Uncovering conceptual bridges based on biomedical concept evolution. In SIGKDD. ACM, 1599--1607.
[3]
K. Jha, G. Xun, Y. Wang, and A. Zhang. 2019. Hypothesis Generation From Text Based On Co-Evolution Of Biomedical Concepts. In SIGKDD. ACM, 843--851.
[4]
RN. Kostoff, JA. Block, JA. Stump, and KM. Pfeil. 2004. Information content in Medline record fields. Int. J. Med. Inform, Vol. 73, 6 (2004), 515--527.
[5]
Q. Le and T. Mikolov. 2014. Distributed representations of sentences and documents. In ICML. JMLR, 1188--1196.
[6]
P. Pirolli. 2007. Information foraging theory: Adaptive interaction with information. Oxford University Press.
[7]
Y. Sebastian, EG. Siew, and SO. Orimaye. 2017. Learning the heterogeneous bibliographic information network for literature-based discovery. Knowledge-Based Systems, Vol. 115 (2017), 66--79.
[8]
D. Stephens and J. Krebs. 1986. Foraging theory. Princeton University Press.
[9]
M. Thilakaratne, K. Falkner, and T. Atapattu. 2019. A systematic review on literature-based discovery workflow. PeerJ-CS, Vol. 5 (2019), e235.
[10]
G. Xun, K. Jha, V. Gopalakrishnan, Y. Li, and A. Zhang. 2017. Generating medical hypotheses based on evolutionary medical concepts. In ICDM. IEEE, 535--544.
Index Terms
- Garbage In, Garbage Out? An Empirical Look at Information Richness of LBD Input Types
Recommendations
A Systematic Review on Literature-based Discovery: General Overview, Methodology, & Statistical Analysis
The vast nature of scientific publications brings out the importance of Literature-Based Discovery (LBD) research that is highly beneficial to accelerate knowledge acquisition and the research development process. LBD is a knowledge discovery workflow ...
Bisociative Literature-Based Discovery: Lessons Learned and New Word Embedding Approach
AbstractThe field of bisociative literature-based discovery aims at mining scientific literature to reveal yet uncovered connections between different fields of specialization. This paper outlines several outlier-based literature mining approaches to ...
Literature-based discovery on the World Wide Web
Previous research has shown that researchers can generate medical hypotheses by using computers to analyze several, seemingly unrelated, medical literatures. In this work we suggest broader application for the ideas of literature-based discovery. ...
Comments
Information & Contributors
Information
Published In
August 2020
611 pages
ISBN:9781450375856
DOI:10.1145/3383583
- General Chairs:
- Ruhua Huang,
- Dan Wu,
- Gary Marchionini,
- Program Chairs:
- Daqing He,
- Sally Jo Cunningham,
- Preben Hansen
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Sponsors
- SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
- SIGIR: ACM Special Interest Group on Information Retrieval
- IEEE: Institute of Electrical and Electronics Engineers
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 01 August 2020
Check for updates
Author Tags
Qualifiers
- Short-paper
Conference
JCDL '20: The ACM/IEEE Joint Conference on Digital Libraries in 2020
August 1 - 5, 2020
Virtual Event, China
Acceptance Rates
Overall Acceptance Rate 415 of 1,482 submissions, 28%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 105Total Downloads
- Downloads (Last 12 months)7
- Downloads (Last 6 weeks)0
Reflects downloads up to 27 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in