TY - GEN
T1 - Resolving Conflict of Interests in Recommending Reviewers for Academic Publications Using Link Prediction Techniques
AU - Al-Zboon, Sa'Ad A.
AU - Tawalbeh, Saja Khaled
AU - Ai-Jarrah, Heba
AU - Al-Asa'D, Muntaha
AU - Hammad, Mahmoud
AU - Al-Smadi, Mohammad
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - An honest peer-review process is a key for producing high quality scientific research. However, this process depends on two main factors: (1) the expertise of reviewers in the topic of a submitted paper and (2) the relationships between reviewers and authors. To satisfy the first factor, editors and conferences chairs manually select reviewers. Whereas to prevent any conflict of interest (CoI) between reviewers and authors to satisfy the second factor, reviewers and authors are asked to declare any CoI manually. Such a solution is tedious to all actors and error-prone. To solve this problem and satisfy those two factors, we have developed a novel framework that (1) recommend expert reviewers and (2) resolve the CoIproblem. To develop our framework, we have represented the DBLP citation network dataset as a graph database using Neo4J. A Cypher queries used to select expert reviewers. Various link prediction algorithms, especially the Adamic Adar and the Common Neighbors algorithms, have been utilized to resolve any notential conflict of interest.
AB - An honest peer-review process is a key for producing high quality scientific research. However, this process depends on two main factors: (1) the expertise of reviewers in the topic of a submitted paper and (2) the relationships between reviewers and authors. To satisfy the first factor, editors and conferences chairs manually select reviewers. Whereas to prevent any conflict of interest (CoI) between reviewers and authors to satisfy the second factor, reviewers and authors are asked to declare any CoI manually. Such a solution is tedious to all actors and error-prone. To solve this problem and satisfy those two factors, we have developed a novel framework that (1) recommend expert reviewers and (2) resolve the CoIproblem. To develop our framework, we have represented the DBLP citation network dataset as a graph database using Neo4J. A Cypher queries used to select expert reviewers. Various link prediction algorithms, especially the Adamic Adar and the Common Neighbors algorithms, have been utilized to resolve any notential conflict of interest.
KW - Adamic Adar
KW - Common Neighbors
KW - Conflict of Interests (CoIs)
KW - DBLP
KW - Link Prediction
UR - https://www.scopus.com/pages/publications/85077193600
U2 - 10.1109/ICTCS.2019.8923033
DO - 10.1109/ICTCS.2019.8923033
M3 - Conference contribution
AN - SCOPUS:85077193600
T3 - 2019 2nd International Conference on New Trends in Computing Sciences, ICTCS 2019 - Proceedings
BT - 2019 2nd International Conference on New Trends in Computing Sciences, ICTCS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on New Trends in Computing Sciences, ICTCS 2019
Y2 - 9 October 2019 through 11 October 2019
ER -