Identification and Prediction of Interdisciplinary Research Topics: A Study Based on the Concept Lattice Theory

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 Citation: 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. This paper is organized as follows. First, various methods and existing problems of IDR topic identification research are reviewed, and the characteristics of different methods used for IDR topic analysis are clarified. Then, the theory and application of FCA and CLT are introduced, and the method for identifying and predicting IDR topics based on the CLT is described. Subsequently, IDR topics in LIS & Medical Informatics, and in LIS & Geography-Physical, were utilized as empirical fields. A comparative analysis with two other IDR topic recognition methods is performed. Finally, the advantages and limitations of this study are discussed. 65Haiyun Xu et al.

Efficient processing of top-k frequent spatial keyword queries

“…It maintains a poset of concepts and can be represented by a Hasse graph, in which each node is a concept, to reveal the relationship between objects and attributes. It has been widely used in information retrieval 40 , software engineering 41 , recommendation system 42 , and knowledge discovery 43 , etc. A concept in concept lattice is the explicit results of data aggregation, and can be represented as a set of spatial objects with several common keywords.…”

Efficient Processing of Top-k Frequent Spatial Keyword Queries

“…It maintains a poset of concepts and can be represented by a Hasse graph, in which each node is a concept, to reveal the relationship between objects and attributes. It has been widely used in information retrieval 36 , software engineering 37 , recommendation system 38 , and knowledge discovery 39 , etc. A concept in concept lattice is the explicit results of data aggregation, and can be represented as a set of spatial objects with several common keywords.…”

Non-redundant implicational base of formal context with constraints using SAT

“…Formal concept analysis (FCA) studies how to extract knowledge from a formal context and has been applied to many areas of data since a formal context is capable to represent any kinds of data. Some research has been conducted to extract knowledge from any data which is formulated in a formal context ( Moulahi, 2021 ; Xu et al, 2019 ; Marín et al, 2021 ; Gély et al, 2022 ; Yan & Li, 2022 ; Zou et al, 2020 ; Janostik & Konecny, 2020 ; Atencia et al, 2020 ; Kötters & Eklund, 2020 ; Rocco, Hernandez-Perdomo & Mun, 2020 ; Kumar Mishra, Joshi & Mathur, 2020 ; Albahli & Melton, 2016 ). Therefore, formal concept analysis has been considered to be a method in knowledge discovery ( Kumar, 2011 ).…”