Constructing Scholarly Knowledge Graphs From Sociology Research Papers: Incremental Changes in Cause-Effect Statements
Построение научных графов знаний на основе исследовательских статей по социологии: инкрементальные изменения в причинно-следственных высказываниях
2025-11-30
SCID: 54.1/xrd32x8a
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argument flowcausal information framescause-effect statementsscholarly knowledge graphssociology research papers
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Abstract (AI)
Researchers have been working on different aspects of representing scientific research papers as machine-processable knowledge graphs. This study extends that work to the domain of social science research papers—specifically aiming to construct scholarly knowledge graphs that represent research results together with their supporting arguments, showing how research claims are incrementally constructed following the discourse structure of the paper. The study analyzed 30 sociology research papers that aimed to find or confirm cause-effect relationships between concepts. It analyzed the causal structure of the research result statements in the papers and investigated what additional information is provided by other statements in the papers performing the argument/rhetorical functions of general statement, topic centrality, literature review, research gap, research objective, research method, research result, and research contribution. A cause-effect information frame that lists all possible roles in a causal information structure was used to guide the analysis. We show that a scholarly knowledge graph representing the research results in a social science research paper must include information extracted from multiple statements in the paper, to provide a comprehensive view of the constructed knowledge. Together, these statements, taken in sequence, form the argument flow of the paper, showing how knowledge is constructed. The study also implemented a prototype graph visualization application to help users examine the causal structure of the research result statements and how that structure is incrementally constructed when the information content of the other statements is viewed in sequence. This tool enables readers to explore conflicting or unexpected results, trace how cause-effect relationships are generalized or specialized, and understand the overall causal information structure accumulated across the paper.
Key Findings
1
A comprehensive scholarly knowledge graph must integrate information from multiple statements, not only research-result statements, to represent social science findings fully.
2
A prototype visualization application enables readers to inspect causal structures, trace generalization or specialization of relationships, and explore conflicting or unexpected results.
3
Statements serving different rhetorical functions incrementally construct the causal knowledge structure and argument flow of a paper.
4
The study analyzed 30 sociology papers focused on identifying or confirming cause-effect relationships between concepts.
5
The study introduced a cause-effect information frame covering all possible roles in causal information structures to guide analysis.
Research Object
30 sociology research papers reporting or examining cause-effect relationships between concepts
Research Subject
The incremental construction of causal research-result statements and their supporting argument structure for scholarly knowledge graphs
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2025-11-30
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