Bibliometric Analysis and Co-word Mapping of the Knowledge Graph Field: A Review-Style Study Charts the Domain's Knowledge Base and Thematic Structure

The article frames the knowledge graph research field as an object of bibliometric analysis and co-word mapping, positioning itself as a review-style study of the field's research output and thematic structure. Unlike technical surveys of knowledge graphs, it treats the knowledge graph field itself as a scientometric object and uses co-word mapping to characterize its thematic structure. Based on the article title and its reference list; the text available here is the reference list only and does not include the search strategy, data sources, or bibliometric results from the body.

The article's knowledge base spans knowledge graph concepts and surveys, embedding and representation, completion and refinement, domain-specific graphs, and application areas such as education. The references combine conceptual works (such as discussions of what a knowledge graph is) with technical works (such as surveys of embedding, completion, and refinement), indicating both a conceptual and a methodological strand in the field. Based on the reference entries themselves, such as those on knowledge graph definitions, embedding surveys, completion reviews, and refinement surveys; no counts or shares per topic are provided.

Methodologically, the article draws on co-word analysis and scientometric tooling, with references including work on improving co-word analysis, applications of co-word analysis, and the VOSviewer manual. Including co-word methodology and tool documentation places the study within the scientometric and knowledge-visualization tradition. Based on reference entries on semantic-distance improvement of co-word analysis, co-word analysis for mapping research trends, and the VOSviewer manual; no parameters, thresholds, or clustering results are provided.

The reference list includes both Persian-language and English-language works, covering Iranian scholarship in scientometrics and knowledge graphs. The multilingual references indicate coverage of both local and international sources. Based on reference entries marked as Persian or published in Iranian journals; no language-selection criteria or database coverage are stated.

The study is aimed at researchers, research managers, and information analysts who want a quick view of the thematic distribution and knowledge base of the knowledge graph field, and it suits settings that require a bibliometric grasp of a field, such as positioning a research topic, identifying research frontiers, and informing science policy. Its methodological frame (co-word analysis and knowledge visualization) can also be transferred to thematic-structure analysis in other disciplines.

Because the text available here is an incomplete reference list, the databases searched, time window, number of documents, co-word clustering, and visualization results in the body cannot be confirmed, so the specific conclusions about the field's thematic structure cannot be judged. Readers interested in thematic evolution and frontier identification in knowledge graphs may watch for whether the article reports cluster labels, topic strength, and time slices, and how those results compare with existing bibliometric reviews of knowledge graphs.