Abstract
This paper proposes a novel entitymetrics approach by exclusively focusing on citation sentences. Since citation sentences offer authors’ research interest, knowledge entities that appear in such sentences can be considered as key entities. To characterize such key entities, we focus on citation sentences that were extracted from full-text research articles collected from PubMed Central. We used “opioid” as our search query since it is an actively studied domain, which indicates that rigorous amounts of knowledge entities and entity pairs are available for examination. After which we construct two novel citation sentence-based networks, namely the Direct Citation Sentence (DCS) network and the Indirect Citation Sentence (ICS) network. The DCS network is built upon direct entity pairs that are captured within citation sentences. The ICS network, on the other hand, utilized indirect entity cooccurrences based on cited author information and section information. To do this, we propose a multi-anchor bipartite network that uses cited author information and section headings as a multi-anchor that is related to bio-entity nodes, namely the [author/section]-entity bipartite network. To demonstrate the usefulness of the DCS and ICS network, a conventional full-text network is formed for comparison analysis. In addition, during this process, MeSH tree structure is used to examine the bio-entity level characteristics. The results show that DCS and ICS network demonstrate distinct network characteristics and provide unobserved top-ranked bio-entity pairs when compared to traditional method. This indicates that our method can expand the base of entitymetrics and provide new insights for entity level bibliometrics analysis.
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https://www.springer.com/journal/11192/submission-guidelines#Instructions%20for%20Authors_Title%20Page, last accessed 15 January 2023.
https://www.nlm.nih.gov/mesh/intro_trees.html last accessed 15 January 2023.
MeSH tree structure is a forest rather (composed of 16 single category trees) than a single tree. To calculate the distance (branch count) between MeSH descriptors that were included in different categories, we assumed that the 16 root tree nodes were all connected with each other. We acknowledge that such approach can have limits since some root tree nodes are not closely related with one another (e.g., “Information Science” and “Diseases”). However, it was shown that all the MeSH terms that matched our top-20 bio-entity pair list were included in eight root tree nodes (i.e., “Anatomy”, “Organisms”, “Diseases”, “Chemicals and Drugs”, “Analytical, Diagnostic and Therapeutic Techniques, and Equipment”, “Psychiatry and Psychology”, “Phenomena and Processes”, “Health Care”) that were closely related to each other.
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Acknowledgements
This paper was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2022R1A2B5B02002359). The earlier version of this paper (Nam et al., 2022) was accepted at the 3rd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2022) at the ACM/IEEE Joint Conference on Digital Libraries 2022 (JCDL2022), Cologne, Germany and Online.
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Appendices
Appendix 1: Top-50 section heading based on frequencies
Section heading | Freq. | % | Section heading | Freq. | % |
|---|---|---|---|---|---|
Discussion | 550,001 | 22.36 | Introduction and background | 1114 | 0.05 |
Introduction | 420,851 | 17.15 | Limitations | 1013 | 0.04 |
Results | 141,030 | 5.75 | Diagnosis | 963 | 0.04 |
Methods | 113,141 | 4.43 | Concluding remarks | 855 | 0.03 |
Materials and methods | 101,053 | 3.75 | Main body | 850 | 0.03 |
Background | 79,623 | 3.24 | Mechanism of action | 834 | 0.03 |
Results and discussion | 31,899 | 1.30 | Pharmacological activities | 823 | 0.03 |
Conclusions | 12,597 | 0.52 | Mapping and ablation | 821 | 0.03 |
Review | 11,080 | 0.45 | Recommendations | 753 | 0.03 |
Methodsdesign | 4814 | 0.20 | Subjects and methods | 698 | 0.03 |
Discussion and conclusions | 3613 | 0.14 | Pathogenesis | 689 | 0.03 |
Main text | 3137 | 0.13 | Rationale | 675 | 0.03 |
Patients and methods | 3091 | 0.13 | Clinical studies | 674 | 0.03 |
Treatment | 3021 | 0.12 | General discussion | 665 | 0.03 |
Methods and analysis | 2643 | 0.11 | Starethods | 658 | 0.03 |
Methodology | 2323 | 0.09 | Pharmacokinetics | 638 | 0.03 |
Management | 1785 | 0.07 | Pharmacology | 636 | 0.03 |
Experimental section | 1780 | 0.07 | Case presentation | 631 | 0.03 |
Pathophysiology | 1558 | 0.06 | Online methods | 599 | 0.02 |
Findings | 1514 | 0.06 | Serotonin | 556 | 0.02 |
Epidemiology | 1420 | 0.06 | Pharmacological activity | 529 | 0.02 |
Literature review | 1338 | 0.05 | Opioids | 528 | 0.02 |
Future directions | 1282 | 0.05 | Overview | 522 | 0.02 |
Experimental procedures | 1210 | 0.05 | Conclusions and future directions | 517 | 0.02 |
Experimental | 1198 | 0.05 | Context | 511 | 0.02 |
Appendix 2: Excluded entity list
Entity | Freq. | Entity | Freq. |
|---|---|---|---|
Treatment | 526,686 | Lead | 57,961 |
Pain | 456,305 | Oral | 57,447 |
Drug | 211,165 | Procedure | 57,276 |
FIG | 206,186 | Procedures | 56,578 |
Care | 201,449 | Affect | 56,073 |
Response | 174,147 | Neuronal | 54,537 |
Drugs | 168,170 | Severity | 54,409 |
Surgery | 161,686 | Protocol | 54,118 |
Brain | 157,926 | Condition | 54,087 |
Dose | 137,096 | Like | 53,988 |
Reduced | 127,899 | Key | 53,656 |
Function | 127,052 | Medications | 52,639 |
Therapy | 117,437 | End | 51,958 |
Human | 117,366 | Sensitivity | 51,654 |
Blood | 106,818 | Interest | 50,794 |
Disease | 106,162 | Secondary | 49,189 |
Opioids | 101,146 | Rat | 48,710 |
Symptoms | 96,081 | Distribution | 48,064 |
Support | 89,745 | Strategies | 46,310 |
Chronic | 82,604 | Adult | 44,728 |
Stimulation | 82,076 | Disorders | 43,925 |
Severe | 80,434 | Delivery | 43,609 |
Exposure | 80,109 | Line | 42,817 |
Impact | 77,239 | Side effects | 42,242 |
General | 76,767 | Right | 41,062 |
Expressed | 72,793 | Injury | 41,590 |
Acute | 71,777 | Understanding | 40,342 |
Normal | 71,392 | Moderate | 39,323 |
Management | 68,786 | Focus | 37,051 |
Medication | 67,976 | Diseases | 36,224 |
Measures | 67,668 | Light | 35,968 |
Association | 67,400 | Onset | 35,871 |
Concentrations | 64,023 | Finding | 35,413 |
Central | 63,070 | Strategy | 35,201 |
Food | 62,410 | Nervous | 34,967 |
Block | 62,157 | Activated | 34,227 |
Set | 61,710 | Content | 33,724 |
Intensity | 59,397 |
Appendix 3: Visualization map of conventional full-text cooccurrence network
Appendix 4: Visualization map of DCS cooccurrence network
Appendix 5: Visualization map of ICS cooccurrence network
Appendix 6: Conventional full-text network cooccurrence information
Entity 1 | Entity 2 | Freq | Distance |
|---|---|---|---|
Anesthesia | Propofol | 5902 | 10 |
Methadone | Buprenorphine | 5708 | 6.75 |
Morphine | Fentanyl | 5326 | 7.25 |
Mental health | Substance use disorder | 5240 | 5 |
Substance use disorder | Addiction | 4292 | 4 |
Anterior | Posterior | 3697 | – |
Morphine | Oxycodone | 3463 | 8.375 |
Naloxone | Overdose | 3447 | 10.75 |
Heroin | Cocaine | 3410 | 8 |
Kit | RNA | 3329 | – |
Glucose | Insulin | 3225 | 11 |
Hip | Fracture | 3199 | – |
Propofol | Remifentanil | 3182 | 9.5 |
Anesthesia | Isoflurane | 3152 | 7 |
Postoperative pain | Pain management | 3116 | 8.5 |
Anesthesia | Sevoflurane | 3093 | 7.5 |
Propofol | Sedation | 3081 | 10 |
Hypotension | Bradycardia | 3057 | 5 |
CBD | THC | 3040 | 1 |
Withdrawal | Morphine | 3035 | 9.75 |
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Nam, D., Kim, J., Yoon, J. et al. Examining knowledge entities and its relationships based on citation sentences using a multi-anchor bipartite network. Scientometrics 129, 7197–7228 (2024). https://doi.org/10.1007/s11192-023-04824-0
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DOI: https://doi.org/10.1007/s11192-023-04824-0