2018
DOI: 10.1007/s11192-018-2861-0
|View full text |Cite
|
Sign up to set email alerts
|

Bibliometric-enhanced information retrieval: preface

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
9
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
6
3

Relationship

3
6

Authors

Journals

citations
Cited by 18 publications
(9 citation statements)
references
References 14 publications
0
9
0
Order By: Relevance
“…The first one is the special issue on “Bibliometric-enhanced information retrieval and natural language processing for digital libraries (BIRNDL)” in the International Journal on Digital Libraries (Mayr et al, 2018 ). The second one is “Bibliometric-enhanced Information retrieval and Scientometrics” in Scientometrics (Cabanac et al, 2018 ).…”
Section: Discussionmentioning
confidence: 99%
“…The first one is the special issue on “Bibliometric-enhanced information retrieval and natural language processing for digital libraries (BIRNDL)” in the International Journal on Digital Libraries (Mayr et al, 2018 ). The second one is “Bibliometric-enhanced Information retrieval and Scientometrics” in Scientometrics (Cabanac et al, 2018 ).…”
Section: Discussionmentioning
confidence: 99%
“…The third key direction of LIS would seek to provide better searching capabilities to users by coupling textual and non-textual meta-data from full-text digital archives (Cabanac et al, 2018…”
Section: Unfolding the Challenges For Smart Librariesmentioning
confidence: 99%
“…What is more, the quality of these techniques is considered imperfect in some cases, requiring more time to achieve the same results as specific well-defined algorithms (Turki et al, 2021b). Here, Bibliometric-Enhanced Information Retrieval (BIR) has evolved as a novel field that utilizes bibliographic metadata to efficiently drive the extraction and refinement of semantic data from scholarly publications (Cabanac et al, 2018). This field contributed to the development of many intuitive and explainable algorithms for knowledge engineering.…”
Section: Introductionmentioning
confidence: 99%