Strategic diagrams and co-word analysis are widely employed to examine the conceptual structure of scientific domains and their development over time. Yet a structural inconsistency characterises dominant longitudinal implementations: themes are detected through relational clustering in weighted networks, whereas their inter-temporal connections are commonly inferred from set-theoretic overlap among keywords or core documents. This study introduces a structurally integrated framework in which lineage reconstruction is embedded within the same weighted relational architecture that underpins cross-sectional detection. The approach models thematic continuity through graded document affiliation and a lineage-strength measure that combines directional coverage with centrality-weighted structural relevance, thereby conceptualising evolution as the reconfiguration of relational structures rather than simple lexical persistence. By aligning thematic detection and temporal modelling within a unified relational paradigm, the framework enhances the methodological coherence and interpretive robustness of longitudinal science mapping.

翻译：战略图与共词分析被广泛用于考察科学领域的概念结构及其随时间的发展。然而，主流的纵向实施方法存在结构性不一致：主题是通过加权网络中的关系聚类检测到的，而主题间的跨时间连接通常基于关键词或核心文档的集合论重叠来推断。本研究引入了一个结构整合的框架，其中谱系重建内嵌于支持横截面检测的同一加权关系架构中。该方法通过分级文档隶属度和结合方向覆盖度与中心性加权结构相关性的谱系强度度量来建模主题连续性，从而将演化概念化为关系结构的重组，而非简单的词汇持续性。通过在统一的关系范式内对齐主题检测与时间建模，该框架增强了纵向科学图谱的方法论一致性与解释稳健性。