Improving the visibility of library resources via mapping library subject headings to Wikipedia articles

Abstract

Purpose-Linking libraries and Wikipedia can significantly improve the quality of services provided by these two major silos of knowledge. Such linkage would enrich the quality of Wikipedia articles and at the same time increase the visibility of library resources. To this end, this work describes the design and development of a software system for automatic mapping of FAST subject headings, used to index library materials, to their corresponding articles in Wikipedia. Design/methodology/approach-The proposed system works by first detecting all the candidate Wikipedia concepts (articles) occurring in the titles of the books and other library materials which are indexed with a given FAST subject heading. This is then followed by training and deploying a Machine Learning (ML) algorithm designed to automatically identify those concepts that correspond to the FAST heading. The ML algorithm used is a binary classifier which classifies the candidate concepts into either "corresponding" or "non-corresponding" categories. The classifier is trained to learn the characteristics of those candidates which have the highest probability of belonging to the "corresponding" category based on a set of fourteen positional, statistical, and semantic features. Findings-We have assessed the performance of the developed system using standard information retrieval measures of precision, recall, and F-score on a dataset containing 200 FAST subject headings manually mapped to their corresponding Wikipedia articles. The evaluation results show that the developed system is capable of achieving Fscores as high as 0.65 and 0.99 in the corresponding and non-corresponding categories respectively. Research limitations/implications-The size of the dataset used to evaluate the performance of the system is rather small. However, we believe the developed dataset is large enough to demonstrate the feasibility and scalability of the proposed approach. Practical implications-The sheer size of English Wikipedia makes the manual mapping of Wikipedia articles to library subject headings a very labour-intensive and time consuming task. Therefore, our aim is to reduce the cost of such mapping and integration. Social Implications-The proposed mapping paves the way for connecting libraries and Wikipedia as two major silos of knowledge, and enables the bi-directional movement of users between the two. Originality/value-To the best of our knowledge, the current work is the first attempt at automatic mapping of Wikipedia to a library controlled vocabulary.

Enriching the library subject headings with folksonomy

“…For example, Joorabchi et al (2015) investigated the mapping of user tags from a Q&A website, StackOverflow, to their corresponding concepts in Wikipedia through a machine learning-based method. Joorabchi and Mahdi (2018) also adopted the machine-learning method to map Wikipedia concepts to FAST subject headings. Though testing on a small data set, they emphasized that the mapping system can improve the visibility of library resources.…”