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The purpose of this study is to provide a systematic literature review on taxonomy alignment methods in information science to explore the common research pipeline and characteristics.

The authors implement a five-step systematic literature review process relating to taxonomy alignment. They take on a knowledge organization system (KOS) perspective, and specifically examining the level of KOS on “taxonomies.”

They synthesize the matching dimensions of 28 taxonomy alignment studies in terms of the taxonomy input, approach and output. In the input dimension, they develop three characteristics: tree shapes, variable names and symmetry; for approach: methodology, unit of matching, comparison type and relation type; for output: the number of merged solutions and whether original taxonomies are preserved in the solutions.

The main research implications of this study are threefold: (1) to enhance the understanding of the characteristics of a taxonomy alignment work; (2) to provide a novel categorization of taxonomy alignment approaches into natural language processing approach, logic-based approach and heuristic-based approach; (3) to provide a methodological guideline on the must-include characteristics for future taxonomy alignment research.

There is no existing comprehensive review on the alignment of “taxonomies”. Further, no other mapping survey research has discussed the comparison from a KOS perspective. Using a KOS lens is critical in understanding the broader picture of what other similar systems of organizations are, and enables us to define taxonomies more precisely.

The purpose of this study is to determine which metadata schemas are used in the museums and university collections of the main universities in Spain and other European countries. Although libraries and archives are also university memory institutions (according to a Galleries, Libraries, Archives and Museums perspective), their collections are not included in this study because their metadata systems are highly standardized and their inclusion would, therefore, skew our understanding of the diverse realities that the study aims to capture.

The analysis has three components. The first is a bibliographic review based on Web of Science. The second is a direct survey of the individuals responsible for university collections to understand their internal work and documentation systems. Finally, the results obtained are complemented by an analysis of collective university heritage portals in Europe.

The results of this study confirmed the hypothesis that isolation and a lack of resources are still major issues in many cases. Increasing digitalization and the desire to participate in content aggregation systems are forcing change, although the responsibility for that change at universities is still vague.

Universities, particularly those with a long history, have an important heritage whose parts are often scattered or hidden. Although many contemporary academic publications have focused on the dissemination of university collections, this study focuses on the representation of information based on the conviction that good metadata are essential for dissemination.

This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.

This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.

The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.

This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.