Measuring Patent-Citations of LIS Literature: An analytical study of the Journal Scientometrics

The purpose of this study is to analyse the utility and application of Library and Information Science (LIS) research in patents representing innovations, inventions and new knowledge. With this research, we have tried to bridge a gap between LIS research and patents, which is unavailable to date in the literature. To conduct the study, various patent search databases were used. Data in the form of DOIs were extracted from the Scopus database for the journal Scientometrics and were processed and analysed in visualisation software and spreadsheet software. The findings reveal how industries filing patents derive valuable inputs from LIS research in terms of its utilization, recognition and acceptance. This research paper will enhance the understanding regarding Library and information science, what is its value in Research and Development (R&D). Normally, it is believed that only STEM (Science, Technology, Engineering and Medical) research is fruitful for patents and innovations. The study breaks the glass ceiling as it provides an evidence-based approach to justify the LIS research does play a crucial role in the growth, development and progress of the society through its existence and proven integration with the patents. The findings reveal that LIS research is influencing Patents as they are being cited regularly with the growth in this discipline

A new approach to journal co-citation matrix construction based on the number of co-cited articles in journals

Co-citation analysis is one of the most important methods in information science. Journal co-citation analysis has been widely used to analyze the relevance, relationship and structure of underlying articles between journals. Accurate construction of co-citation matrix is a key to accurate journal co-citation analysis. However, the traditional co-citation matrix construction based on co-citation frequency of journals does not accurately reflect the similarity between journals. This paper proposes a new construction method of co-citation matrix based on the number of co-citation articles in journals. The experimental validation has been conducted with real datasets from Chinese Social Science Citation Index and National Knowledge Infrastructure. The results show that the proposed method can accurately capture the similarity between journals and outperform the existing approaches (i.e. co-citation frequency and co-citation ratio approaches). In addition, the proposed method does not need the full-text index of a paper, which provides added value in the field

A Correlation Study of Co-opinion and Co-citation Similarity Measures

Co-citation forms a relational document network. Co-citation-based measures are found to be effective in retrieving relevant documents. However, they are far from ideal and need further enhancements. Co-opinion concept was proposed and tested in previous research and found to be effective in retrieving relevant documents. The present study endeavors to explore the correlation between opinion (dis)similarity measures and the traditional co-citation-based ones including Citation Proximity Index (CPI), co-citedness and co-citation context similarity. The results show significant, though weak to medium, correlations between the variables. The correlations are direct for co-opinion measure, while being inverse for the opinion distance. Accordingly, the two groups of measures are revealed to represent some similar aspects of the document relation. Moreover, the weakness of the correlations implies that there are different dimensions represented by the two group

CITREC: An Evaluation Framework for Citation-Based Similarity Measures based on TREC Genomics and PubMed Central

Citation-based similarity measures such as Bibliographic Coupling and Co-Citation are an integral component of many information retrieval systems. However, comparisons of the strengths and weaknesses of measures are challenging due to the lack of suitable test collections. This paper presents CITREC, an open evaluation framework for citation-based and text-based similarity measures. CITREC prepares the data from the PubMed Central Open Access Subset and the TREC Genomics collection for a citation-based analysis and provides tools necessary for performing evaluations of similarity measures. To account for different evaluation purposes, CITREC implements 35 citation-based and text-based similarity measures, and features two gold standards. The first gold standard uses the Medical Subject Headings (MeSH) thesaurus and the second uses the expert relevance feedback that is part of the TREC Genomics collection to gauge similarity. CITREC additionally offers a system that allows creating user defined gold standards to adapt the evaluation framework to individual information needs and evaluation purposes.ye