Chinese Library Classification

Abstract: Representing classification systems on the web for publication and exchange continues to be a challenge within the SKOS framework. This paper focuses on the differences between classification schemes and other families of KOS (knowledge organization systems) that make it difficult to express classifications without sacrificing a large amount of their semantic richness. Issues resulting from the specific set of relationships between classes and topics that defines the basic nature of any classification system are discussed. Where possible, different solutions within the frameworks of SKOS and OWL are proposed and examined.

Abstract: The invention discloses a method for automatically classifying academic documents, which comprises the following steps: inputting training documents into a database, wherein the training documents comprise document classification numbers; selecting unitary characteristic words and binary characteristic words, and generating binary word pairs for the training documents; reading the training documents in the database, and respectively calculating the probability relations between the unitary and binary characteristic words and the document classification numbers, thereby forming a unitary classification dictionary and a binary classification dictionary; reading a document to be labelled, calculating the Chinese library classification number corresponding to the document according to the weight of the unitary and binary classification dictionaries, and the unitary and binary characteristic words in the document to be labelled, and automatically labelling; and dividing the unitary and binary classification result into a high-accuracy result set and a low-accuracy result set according to the degree of confidence, and outputting the classification result.

Abstract: Disciplinary classification of science is essential to bibliometric analyses. Given the conceptual and technical difficulties in classifying individual papers into disciplines and specialties, most classifications systems are implemented at the journal level, which affects the classification of papers published in multidisciplinary journals. In order to investigate the effect of the different classification systems on bibliometric evaluations, this study compares the rankings of the most productive institutions and most productive authors using the two types of classifications. Results show that the classification of papers has less influence on rankings at the institutional level than at the individual level. Implications for bibliometric evaluations are discussed.

Abstract: As the framework of scientific research, subject-classification plays an important role in the development of science. In order to combine the development of science with the current expert subject-classification system and further give a more appropriate description of scientific output analysis from subject level, We study the relationship between the natural science related sub-categories of Chinese library classification using objective computerized scientometrics, and give some modification to the first two level subjects of the existing Chinese library classification system. Taking Chinese Science Citation Database as our data source, this article studies the similarity of subjects based on journal coupling strength. Then we try to set up an improved subject-classification system whose top categories are relied on Chinese library classification system and sub-categories are the ensemble clustering result based on journal coupling measure. Further, in order to help identifying and interpreting the rationality of this improved classification system, we make use of some text mining methods, such as key words recognition and topic detection, to explain the cause of similarity between some subjects from the perspective of semantic. Our study shows that the improved subject-classification system constructed in this article not only conforms to previous experience and cognitive but also combines subject development knowledge.