TY - JOUR
T1 - A paper recommendation system with readerbench
T2 - the graphical visualization of semantically related papers and concepts
AU - Paraschiv, Ionut Cristian
AU - Dascalu, Mihai
AU - Dessus, Philippe
AU - Trausan-Matu, Stefan
AU - McNamara, Danielle
N1 - Publisher Copyright:
© 2016, Springer Science+Business Media Singapore.
PY - 2016
Y1 - 2016
N2 - The task of tagging papers with semantic metadata in order to analyze their relatedness represents a good foundation for a paper recommender system. The analysis from this paper extends from previous research in order to create a graph of papers from a specific domain with the purpose of determining each article’s importance within the considered corpus of papers. Moreover, as non-latent representations are powerful when used in conjunction with latent ones, our system retrieves semantically close words, not present in the paper, in order to improve the retrieval of papers. Our previous analyses used the semantic representation of papers in different semantic models with the purpose of creating visual graphs based on the semantic relatedness links between the abstracts. The current analysis takes a step forward by proposing a model that can suggest which papers are of the highest relevance, share similar concepts, and are semantically related with the initial query. Our study is performed using paper abstracts in the field of information technology extracted from the Web of Science citation index. The research includes a use case and its corresponding results by using interactive and exploratory network graph representations.
AB - The task of tagging papers with semantic metadata in order to analyze their relatedness represents a good foundation for a paper recommender system. The analysis from this paper extends from previous research in order to create a graph of papers from a specific domain with the purpose of determining each article’s importance within the considered corpus of papers. Moreover, as non-latent representations are powerful when used in conjunction with latent ones, our system retrieves semantically close words, not present in the paper, in order to improve the retrieval of papers. Our previous analyses used the semantic representation of papers in different semantic models with the purpose of creating visual graphs based on the semantic relatedness links between the abstracts. The current analysis takes a step forward by proposing a model that can suggest which papers are of the highest relevance, share similar concepts, and are semantically related with the initial query. Our study is performed using paper abstracts in the field of information technology extracted from the Web of Science citation index. The research includes a use case and its corresponding results by using interactive and exploratory network graph representations.
KW - Discourse analysis
KW - Paper recommendation system
KW - Scientometrics
KW - Semantic similarity
UR - http://www.scopus.com/inward/record.url?scp=84986555473&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84986555473&partnerID=8YFLogxK
U2 - 10.1007/978-981-287-868-7_53
DO - 10.1007/978-981-287-868-7_53
M3 - Article
AN - SCOPUS:84986555473
SN - 2196-4963
SP - 445
EP - 451
JO - Lecture Notes in Educational Technology
JF - Lecture Notes in Educational Technology
IS - 9789812878663
ER -