[Purpose/Significance] This study aims to mitigate data conflicts caused by time lags, enhance semantic complementarity and accuracy of multi-source data, and improve the synergistic effect of knowledge fusion. [Method/Process] Focusing on topic time lag, this study incorporates the data source as a covariate in the structural topic model (STM). The direction and duration of the lag are measured by analyzing topic content, evolutionary trends, and similarity. By combining time lag and semantic similarity, the knowledge element semantics are separated into three categories: non-lagging semantics, lagging-similar semantics, and lagging-dissimilar semantics. And semantic separation and fusion strategies are proposed. Furthermore, an implementation framework for a multi-source data integration knowledge fusion model is constructed. This framework is applied and validated using artificial intelligence domain data from NSF and WoS. [Result/Conclusion] The proposed approach of local time lag processing mitigates their negative impact on data fusion while maintaining the characteristics of the data source. It is the basis for designing topic time lag measurement and knowledge element similarity fusion strategies. The calculation reveals that topic time lags of NSF and WoS data manifest as both leads and lags, and exhibit different lag directions and durations on different topics. The fusion of knowledge element semantics significantly reduces semantic redundancy and noise, achieves alignment and deduplication of divergent semantics in multi-source data, while preserving the integrity of semantic information. The proposed workflow of “Time Lag Measurement→Semantic Separation→Semantic Fusion” can effectively support the implementation of multi-source data integration knowledge fusion model.
Jin Jialin
,
Wang Qi
,
Wang Yuefen
. Research on the Implementation of Multi-Source Data Integration Knowledge Fusion Model Considering Topic Time Lag[J]. Library and Information Service, 2026
, 70(10)
: 32
-42
.
DOI: 10.13266/j.issn.0252-3116.2026.10.003
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