# Charting Past, Present, and Future Research in the Semantic Web and Interoperability
## 2 Methodology
### Network Analysis  
Following an analysis of descriptive statistics, the intellectual landscape of research at the intersection of interoperability and the semantic web was represented by networks of various entities, including countries, academic institutions, and authors. VOSviewer was used to create these networks from the extracted bibliographic sources. Additionally, we conducted an in-depth analysis of the trends and relationships between the publications in our sample. We performed network analysis on bibliometric data and visualized the network structure of interoperability and the semantic web using the visual tool Gephi. The strength and significance of a network connection between two publications can be explained by the co-occurrence of keywords in the same publication or the co-citation of the same publication [58]. The visualization of co-citation networks is an example of exploratory data analysis (EDA) that utilizes graph theory to examine the data structure [59]. A co-citation network comprises a set of nodes that represent publications and a set of edges that represent the co-occurrence of publications in the network's reference list [60]. Co-cited publications are those that appear in the reference lists of other publications. As a result, publications A and B are co-cited if publication C cites both publications A and B. It has been demonstrated that publications that are frequently cited together by other publications are more likely to be related and thus to contain similar content or subject matter [61]. This approach was used to map and categorize the literature on interoperability and the semantic web. Since a highly cited paper might not be highly prestigious, prestige is a critical impact indicator [62]. In this regard, PageRank was introduced to measure both the citability and prestige of a paper [63]. In this study, we identified the leading ten papers from each cluster by their PageRank measure to ensure the analysis of high-quality papers in each cluster. Gephi is used to generate the co-citation network from the BibExcel data. We used a procedure to generate the co-citation network cluster. For example, prior to using Gephi, bibliometric data were pre-processed, and a citation frequency threshold of 50 was established. Caution was exercised in this step, as a low threshold value may produce an excessive number of clusters, while a high threshold value may result in over-filtering. Additionally, to create a meaningful visualization, a simple and interpretable map was generated using the force atlas layout in Gephi, which is used to reposition the nodes in the network in a more simplistic and readable manner. Each node in the network represents a publication, and each connection between two nodes represents a co-citation relationship. We manually adjusted the hierarchies, node sizes, and other settings to generate the co-citation network (e.g., node color). We conducted a keyword co-occurrence network analysis to better understand the interoperability and semantic web research. Like a co-citation network, a keyword co-occurrence network illustrates the co-occurrence of keywords and their relationships [64]. According to [65], analyzing this network enables researchers to identify research topics and document the transition of research frontiers within a particular field of study. If two keywords appear more frequently in the same publications, they have a stronger relationship. By creating the keyword co-occurrence network, we hoped to analyze the core content of the keywords used and better understand the research structure at the intersection of interoperability and the semantic web. VOSviewer was used to visualize the co-word network because it is highly recommended and compatible with the BiBExcel software package. The node's size corresponds to the frequency of each keyword, while the thickness of the edges indicates the frequency with which each pair of keywords appears in publications.