Mapping knowledge domains is of importance to understand how a discipline develops and how publications, authors, journals and/or affiliations relate to each other in the discipline. Yet, maps without rigorous assessment cannot offer convincing evidence to research policy and decision making. This brief communication discusses the elements to be assessed in a typical assessment task and potential quantitative and qualitative strategies for the assessment of mapping knowledge domains. At the end of this communication, I also point out potential future outlooks in mapping knowledge domains and their assessments.
BuYLiuTYHuangWB. MACA: a modified author co-citation analysis method combined with general descriptive metadata of citations. Scientometrics2016; 108(1): 143–166.
BuYWangBChinchilla-RodríguezZ, et al. Considering author sequence in all-author co-citation analysis. Inf Process Manag2020; 57(6): 102300.
4.
XieQWaltmanL. A comparison of citation-based clustering and topic modeling for science mapping [Internet]. arXiv [preprint]2023. DOI: 10.48550/arXiv.2309.06160.
5.
YanEDingY. Scholarly network similarities: how bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other. J Am Soc Inf Sci Technol2012; 63(7): 1313–1326.
6.
BoyackKWKlavansR. Co-citation analysis, bibliographic coupling, and direct citation: which citation approach represents the research front most accurately?J Am Soc Inf Sci Technol2010; 61(12): 2389–2404.
7.
SmallH. Co-citation in the scientific literature: a new measure of the relationship between two documents. J Am Soc Inf Sci1973; 24(4): 265–269.
8.
KesslerMM. Bibliographic coupling between scientific papers. Am Doc1963; 14(1): 10–25.
9.
WhiteHDGriffithBC. Author cocitation: a literature measure of intellectual structure. J Am Soc Inf Sci1981; 32(3): 163–171.
10.
ZhaoDStrotmannA. Author bibliographic coupling: another approach to citation-based author knowledge network analysis. Proc Am Soc Inf Sci Technol2008; 45(1): 1–10.
11.
NewmanME. Coauthorship networks and patterns of scientific collaboration. Proc Natl Acad Sci2004; 101(Suppl. 1): 5200–5205.
12.
McCainKW. Mapping economics through the journal literature: an experiment in journal cocitation analysis. J Am Soc Inf Sci1991; 42(4): 290–296.
13.
ZhouQLeydesdorffL. The normalization of occurrence and co-occurrence matrices in bibliometrics using cosine similarities and Ochiai coefficients. J Assoc Inf Sci Technol2016; 67(11): 2805–2814.
14.
BoyackKWKlavansR. A comparison of large-scale science models based on textual, direct citation and hybrid relatedness. Quant Sci Stud2020; 1(4): 1570–1585.
15.
BuY. Understanding the citation-based impact of scientific publications through ego-centered citation networks. PhD Thesis, Indiana University, Bloomington, IN, 2020.
16.
BuYNiSHuangWB. Combining multiple scholarly relationships with author cocitation analysis: a preliminary exploration on improving knowledge domain mappings. J Informetr2017; 11(3): 810–822.
17.
AnegónFDMContrerasEJCorrochanoMDLM. Research fronts in library and information science in Spain (1985–1994). Scientometrics1998; 42(2): 229–246.
18.
DingYChowdhuryGGFooS. Bibliometric cartography of information retrieval research by using co-word analysis. Inf Process Manag2001; 37(6): 817–842.
19.
EstabrooksCADerksenLWintherC, et al. The intellectual structure and substance of the knowledge utilization field: a longitudinal author co-citation analysis, 1945 to 2004. Implement Sci2008; 3(1): 49.
20.
MaRNiC. Author coupling analysis: an exploratory study on a new approach to discover intellectual structure of a discipline. J Libr Sci China2012; 38(2): 4–11.
21.
ZhaoRChenB. Applying author co-citation analysis to user interaction analysis: a case study on instant messaging groups. Scientometrics2014; 101(2): 985–997.
22.
EomSB. Relationships between the decision support system subspecialities and reference disciplines: an empirical investigation. Eur J Oper Res1998; 104(1): 31–45.
23.
EomSB. The intellectual development and structure of decision support systems (1991–1995). Omega1998; 26(5): 639–657.
24.
EomSB. Decision support systems research: current state and trends. Ind Manag Data Syst1999; 99(5): 213–221.
25.
BruerJ. Can we talk? How the cognitive neuroscience of attention emerged from neurobiology and psychology, 1980–2005. Scientometrics2009; 83(3): 751–764.
26.
ChuKCLiuWITsaiMY. The study of co-citation analysis and knowledge structure on healthcare domain. In: American institute of physics conference series, Las Vegas, Nevada, November 2012, pp. 247–253. New York: Melville.
27.
TsayMY. The subject structure of randomized controlled trials: an author co-citation analysis. In: Proceedings of ISSI 2011–The 13th international conference on scientometrics and informetrics, Durban, South Africa, 4–7 July 2011, pp. 1067–1069. New York: ACM.
28.
YeJ. On the system of all-round evaluation of research. 1st ed.Beijing, China: Social Sciences Academic Press of China, 2021.
29.
WaltmanLVan EckNJ. A new methodology for constructing a publication-level classification system of science. J Am Soc Inf Sci Technol2012; 63(12): 2378–2392.
30.
ConstantinoIKojakuSFortunatoS, et al. Representing the disciplinary structure of physics: a comparative evaluation of graph and text embedding methods [Internet]. arXiv [preprint]2023. DOI: 10.48550/arXiv.2308.15706.
LiangGLouYHouH. Revisiting the disruptive index: evidence from the Nobel prize-winning articles. Scientometrics2022; 127(10): 5721–5730.
33.
JinCMaYUzziB. Scientific prizes and the extraordinary growth of scientific topics. Nat Commun2021; 12(1): 1–11.
34.
BornmannLDevarakondaSTeklesA, et al. Are disruption index indicators convergently valid? The comparison of several indicator variants with assessments by peers. Quant Sci Stud2020; 1: 1242–1259.
35.
BornmannLTeklesA. Convergent validity of several indicators measuring disruptiveness with milestone assignments to physics papers by experts. J Informetr2021; 15(3): 101159.
36.
KlavansRBoyackKW. Which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge?J Assoc Inf Sci Technol2017; 68(4): 984–998.
37.
BuYWangBHuangW, et al. Using the appearance of citations in full text on author co-citation analysis. Scientometrics2018; 116(1): 275–289.
38.
WangBBuYHuangW, et al. Document-and keyword-based author co-citation analysis. Data Inf Manag2018; 2(2): 70–82.
39.
KwonOHCrnovrsaninTMaKL. What would a graph look like in this layout? A machine learning approach to large graph visualization. IEEE Trans Vis Comput Graph2018; 24(1): 478–488.
FunkRJOwen-SmithJ. A dynamic network measure of technological change. Manag Sci2016; 63(3): 791–817.
43.
UzziBMukherjeeSStringerM, et al. Atypical combinations and scientific impact. Science2013; 342(6157): 468–472.
44.
WuLWangDEvansJA. Large teams develop and small teams disrupt science and technology. Nature2019; 566(7744): 378.
45.
SunXChengHLiJ, et al. All in one: multi-task prompting for graph neural networks. In: KDD ‘23: proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining, Long Beach, CA, 6–10 August 2023, pp. 2120–2131. New York: ACM.
46.
WhiteHDMcCainKW. Visualizing a discipline: an author co-citation analysis of information science, 1972–1995. J Am Soc Inf Sci1998; 49(4): 327–355.
