Abstract Purpose Formal concept analysis (FCA) and concept lattice theory (CLT) are introduced for constructing a network of IDR topics and for evaluating their effectiveness for knowledge structure exploration. Design/methodology/approach We introduced the theory and applications of FCA and CLT, and then proposed a method for interdisciplinary knowledge discovery based on CLT. As an example of empirical analysis, interdisciplinary research (IDR) topics in Information & Library Science (LIS) and Medical Informatics, and in LIS and Geography-Physical, were utilized as empirical fields. Subsequently, we carried out a comparative analysis with two other IDR topic recognition methods. Findings The CLT approach is suitable for IDR topic identification and predictions. Research limitations IDR topic recognition based on the CLT is not sensitive to the interdisciplinarity of topic terms, since the data can only reflect whether there is a relationship between the discipline and the topic terms. Moreover, the CLT cannot clearly represent a large amounts of concepts. Practical implications A deeper understanding of the IDR topics was obtained as the structural and hierarchical relationships between them were identified, which can help to get more precise identification and prediction to IDR topics. Originality/value IDR topics identification based on CLT have performed well and this theory has several advantages for identifying and predicting IDR topics. First, in a concept lattice, there is a partial order relation between interconnected nodes, and consequently, a complete concept lattice can present hierarchical properties. Second, clustering analysis of IDR topics based on concept lattices can yield clusters that highlight the essential knowledge features and help display the semantic relationship between different IDR topics. Furthermore, the Hasse diagram automatically displays all the IDR topics associated with the different disciplines, thus forming clusters of specific concepts and visually retaining and presenting the associations of IDR topics through multiple inheritance relationships between the concepts.

中文翻译：

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跨学科研究主题的识别与预测：基于概念格理论的研究

摘要目的介绍形式概念分析（FCA）和概念格理论（CLT），以构建IDR主题网络并评估其在知识结构探索中的有效性。设计/方法/方法我们介绍了FCA和CLT的理论和应用，然后提出了一种基于CLT的跨学科知识发现的方法。作为经验分析的一个例子，信息与图书馆科学（LIS）和医学信息学以及LIS和地理物理的跨学科研究（IDR）主题被用作经验领域。随后，我们与其他两种IDR主题识别方法进行了比较分析。结果CLT方法适用于IDR主题识别和预测。研究限制基于CLT的IDR主题识别对主题词的跨学科性不敏感，因为数据只能反映学科和主题词之间是否存在关联。此外，CLT无法清楚地代表大量概念。实际意义识别IDR主题之间的结构和层次关系后，他们对IDR主题有了更深入的了解，这有助于对IDR主题进行更精确的识别和预测。基于CLT的创新性/价值IDR主题识别表现良好，该理论在识别和预测IDR主题方面具有多个优势。首先，在概念格中，互连的节点之间存在偏序关系，因此，一个完整的概念晶格可以表现出层次特性。第二，基于概念格的IDR主题的聚类分析可以产生突出基本知识特征并有助于显示不同IDR主题之间的语义关系的聚类。此外，Hasse图自动显示与不同学科相关的所有IDR主题，从而形成特定概念的集群，并通过概念之间的多重继承关系在视觉上保留和呈现IDR主题的关联。