In this article, we first argue the importance and timely need of linking libraries and Wikipedia for improving the quality of their services to information consumers, as such linkage will enrich the quality of Wikipedia articles and at the same time increase the visibility of library resources which are currently overlooked to a large degree. We then describe the development of an automatic system for subject indexing of library metadata records with Wikipedia concepts as an important step towards Library-Wikipedia integration. The proposed system is based on first identifying all Wikipedia concepts occurring in the metadata elements of library records. This is then followed by training and deploying generic machine learning algorithms to automatically select those co ncepts which most accurately reflect the core subjects of the library materials whose records are being indexed. We have assessed the performance of the developed system using standard information retrieval measures of precision, recall, and F-score on a dataset consisting of 100 library metadata records manually indexed with a total of 469 Wikipedia concepts. The evaluation results show that the developed system is capable of achieving an averaged F-score as high as 0.92.
Joorabchi and Mahdi REF present a methodology for indexing library metadata records with Wikipedia concepts, where the Wikipedia concepts appearing in the library metadata are identified and classified as ''key'' or ''non-key'' based on 15 statistical, positional, and semantic features.
Towards linking libraries and Wikipedia: automatic subject indexing of library records with Wikipedia concepts
J. Inf. Sci.
J. Inf. Sci.
Computer Science