Knowledge Retrieval and Semantic Systems

Abstract Introduction
In recent decades, evaluating the impact of scientific outputs has become a central concern in the fields of scientometrics and knowledge management. Traditional citation-based indicators, such as citation counts, have long been recognized as reliable measures of scholarly impact. However, these indicators suffer from limitations, including time delays in citation accumulation and insufficient coverage of online scholarly interactions. With the rapid development of web technologies and the widespread use of academic social networks, new approaches known as altmetrics have emerged. These metrics provide a more immediate and broader reflection of the attention and usage of scholarly outputs by capturing data from various platforms such as social media, reference management tools, and online repositories. Despite their growing popularity, a key question remains regarding the extent to which altmetrics correlate with and predict traditional citation-based indicators, particularly in specialized fields such as particle physics, where publication and citation behaviors may differ significantly.
Literature Review
A review of previous studies indicates that the relationship between altmetrics and traditional scientometric indicators has attracted considerable scholarly attention in recent years. Many studies report a positive correlation between certain altmetric indicators—such as Mendeley readership—and citation counts. Additionally, evidence suggests that indicators reflecting scholarly engagement, such as saving and downloading, tend to have stronger predictive power than those based on social media activity. However, findings across disciplines are not consistent. In fields such as medical and life sciences, strong correlations between altmetrics and citations have been observed, whereas in other disciplines, these relationships appear weak or statistically insignificant. This inconsistency highlights the influence of contextual factors, including disciplinary norms, communication practices, and the level of adoption of digital tools among researchers. Consequently, domain-specific studies are essential for a more accurate understanding of these relationships.
Methodology
This applied study adopts a descriptive-analytical approach within the framework of altmetrics. The statistical population consists of 5,704 documents in the field of particle physics indexed in the Scopus database between 2000 and 2019. From this population, a sample of 103 highly cited documents was selected based on Scopus classifications. Altmetric data for these documents were extracted from the PlumX platform, covering five main categories: citations, usage, captures, mentions, and social media interactions. In this study, the dependent variable is the number of citations received in Scopus, while the independent variables are the various PlumX metrics. Data analysis was conducted using descriptive statistics, Pearson correlation coefficients to examine relationships between variables, and multiple regression analysis to assess the predictive power of altmetric indicators. All analyses were performed using SPSS software.
Results
The findings reveal that the total number of citations received by the sampled documents in Scopus is 56,861, with an average of approximately 552 citations per document. In PlumX, the total citation count for these documents is 43,278. Among the altmetric dimensions, “captures” (particularly Mendeley readership) and “usage” (such as abstract views in databases) show substantial levels of engagement. Pearson correlation analysis indicates a strong and statistically significant positive relationship between PlumX citations and Scopus citations. Additionally, the “captures” and “mentions” indicators demonstrate significant positive correlations with Scopus citation counts. In contrast, “usage” and “social media” indicators do not exhibit statistically significant relationships with citation counts. The results of multiple regression analysis show that the model has a high explanatory power (R² ≈ 0.93). Within this model, PlumX citations emerge as the strongest predictor of Scopus citations, followed by captures and mentions, while other variables do not significantly contribute to the prediction.
Discussion
The results of this study suggest that not all dimensions of altmetrics equally contribute to predicting scholarly impact. Indicators that reflect actual scholarly engagement such as reading, saving, and citing demonstrate stronger predictive capabilities compared to those based on general social media activity. This pattern may be attributed to the specialized nature of particle physics, where scholarly communication predominantly occurs through formal and discipline-specific channels rather than general social media platforms. Furthermore, the findings are consistent with some previous studies while diverging from others, reinforcing the importance of considering disciplinary characteristics when interpreting altmetric data. Overall, altmetrics appear to function more effectively as complementary tools rather than replacements for traditional citation-based indicators.
Conclusion
In conclusion, the findings indicate that altmetric indicators particularly those related to scholarly usage and citation can play a meaningful role in predicting citation counts in traditional databases such as Scopus. However, social media-based indicators alone are insufficient for predicting scholarly impact and should be used with caution. Therefore, it is recommended that research evaluation frameworks incorporate a combination of traditional and altmetric indicators to achieve a more comprehensive assessment. Additionally, enhancing researchers’ awareness and skills in using altmetric tools may contribute to increasing the visibility and impact of their scholarly work.

Abstract Introduction

Authors often publish scientific articles in reputable journals to achieve a good professional status and recognition. One of the methods of identifying reputable journals is to examine the ranking indicators that are used by publishing houses. This study aims to identify the impact of SJR, SNIP, and Citescore on the number of citations received in Semantic Web publications. It examines the status of Semantic Web scientific productions in Scopus, the relationship between SJR, SNIP, and Citescore, and the number of citations received. The results of this study help authors identify factors that affect the increase in the number of citations received in Scopus, and examine the current status of Semantic Web publications for submitting their research results.

Literature Review

Rahman et al. (2024) studied a range of bibliometric indicators including impact factor, special factor score, citation score, and H-index of 105 healthcare services and science journals from Scopus and Web of Science databases and acknowledged that there is a strong statistical correlation between JIF and Citescore as well as between JIF and SJR. While there is a moderate correlation between JIF and SNIP. Elmi, Mohammad and Bari (2024) while pointing out the impact of journal scientometric analyses on drawing the landscape of knowledge areas, believe that journals with Citescore, SNIP, SJR rankings are key platforms for publishing influential research and play a significant role in shaping the scientific path of journals and knowledge areas.

Methodology

The present study is a fundamental type that was conducted in a descriptive manner and with a scientometric approach. The statistical population of this study includes 42,270 documents produced in the field of semantic web that were published by journals indexed in the Scopus database in the period 1960-2021. After an initial review, 10 titles of high-production journals in the field of semantic web that published 15,182 titles of scientific productions in this field were selected as a statistical sample. In order to examine the relationships between the research variables, Pearson correlation coefficient test and multiple linear regression were used with the help of Excel and SPSS software.

Results

Retrieving information related to scientific productions in the field of semantic web from the Scopus database showed that in the period 1960-2021, 42,270 records were indexed in this database. 29,189 titles (69%) were conference papers. This number of scientific productions has been cited 267,384 times so far. The trend of scientific productions in the field of semantic web has been almost uniform from 1970 (the time of indexing the first record) to 2000. The number of citations received in this period was 267,384. The largest number of productions (3,304 titles) was in 2010. The largest number of citations received (13,446 citations) was related to the article titled "Fully convolutional networks for semantic segmentation" in 2015. Lecture Notes in Computer Science (LNCS) published 471,217 articles during the study period, of which 7,418 were in the semantic web field. "Lecture Notes in Computer Science" has the highest number of publications (7,418 records), equivalent to 17.5% of all publications in the semantic web field. This source is also ranked first in terms of the total number of publications from different fields. In terms of Site Score, "Journal Of Web Semantics" has the highest score (1.9). This score is the average of the Site Score scores of the last 10 years of this journal. Given that the Citescore is the result of dividing the number of citations by the number of documents published in a three-year period, in this respect the journal has the best performance compared to other journals studied. Among the 10 selected journals, the Journal of Web Semantics has the highest Site Score (9.1) and the highest SJR score (8.1). The highest SNIP score (12.6) is assigned to the Journal of Web Semantics. According to (r=.860; p=.812), there is no correlation between the number of citations and the SNIP index. There is also no significant relationship between the number of citations and SJR (r=.580; p=.874) and the number of citations and Site Score (r=.480; p=.896). The results of multiple regression showed that none of the three Site Score, SNIP, and SJR indices can predict the number of citations received in the Scopus database.

Discussion

Examining the relationship between Citescore, SJR, and SNIP indicators shows that there are positive and significant relationships between these indicators in the scientific productions of the semantic web. These indicators are affected by the number of citations and there is a very high correlation between them. However, there is no significant relationship between these indicators and the total number of citations received in the semantic web in the publications under study. The significance level obtained from the variance test between the indicators under study and the number of citations received in the Scopus database indicates the absence of a significant relationship in this case. On the other hand, the regression coefficients and the significance level obtained from running the model also indicate the inability to predict the number of citations received through the desired indicators. In other words, none of the Citescore, SJR, and SNIP indicators are able to predict the number of citations received in the scientific productions of the semantic web.

Conclusion

The lack of a significant relationship between Citescore, SNIP, and SJR indices and the number of citations received by high-producing Semantic Web publications indicates the insignificant share of the number of citations received from Semantic Web publications compared to the total number of citations received by these publications.

Abstract The aim of this study was to identify the effect of Altmetrics indexes of ResearchGate social network on scientometric indicators of Scopus, Web of Science, and Google Scholar citation databases. The present study is an applied research and descriptive-survey research that has been done by the scientometric method with the Altmetrics approach. The statistical population of the study includes Iranian researchers and writers in the field of nursing who in the period 2000-2019 and on the date of research (20/03/2020), had an indexed document in the Scopus citation database and were also a member of the scientific social network ResearchGate. The statistical sample of the study includes 158 high-profile authors introduced by Scopus. In addition to descriptive statistics, in the inferential statistics section, the correlation test between the studied indices has been used with the help of SPSS and Excel software.The research findings show that there is a significant relationship between most of the ResearchGate indices and the indices studied in Scopus, Web of Science, and Google Scholar. But there is no significant relationship between the index of followers with H-index indexes and citations received in Scopus and Web of Science databases. Examining the relationship between the number of citations received in Google Scholar, Scopus, and Web of Science databases shows that there is a significant relationship between the number of citations received in Scopus, Web of Science, and Google Scholar databases.

Abstract This study investigates the relationship between the measurements obtained from the social networks of ResearchGate and Academia with the scientometric indicators of Scopus and Web of Science databases. The present study is of a fundamental type that has been done in a descriptive manner and with an algorithmic approach. The statistical population of the study includes the most prolific authors in the field of Iranian Business Management who have uploaded their documents in the Scopus database between 2000-2019. 160 of these authors, who had the most documents indexed in Scopus, respectively, have been introduced as a statistical sample of the research. In order to investigate the relationship between the metrics obtained by the authors' activities in the research networks of ResearchGate and Academia with the scientometric indicators of Scopus and Web of Science databases, the statistical tests of correlation with the help of Excel and SPSS has been used. The presence and activity of Business Management writers on the ResearchGate social network is much higher than that of the academia. The relationship between the number of records, citations, H-index and co-authorship in Scopus with the metrics of citation number, citations, index, readers, RJ score and followers in ResearchGate is positive and significant. However, there is no significant relationship between Scopus scientometrics and the criteria of followers, viewers, and co-authors in the academia.