Towards linking libraries and Wikipedia: Aautomatic subject indexing of library records with Wikipedia concepts
|Towards linking libraries and Wikipedia: Aautomatic subject indexing of library records with Wikipedia concepts|
|Author(s)||Joorabchi A., Mahdi A.E.|
|Published in||Journal of Information Science|
|Keyword(s)||bibliographic records, library metadata, metadata generation, subject metadata, text mining, Wikipedia (Extra: Bibliographic records, Metadata generation, Subject metadatas, Text mining, Wikipedia, Data mining, Indexing (of information), Learning algorithms, Libraries, Metadata, Bibliographic retrieval systems)|
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Towards linking libraries and Wikipedia: Aautomatic subject indexing of library records with Wikipedia concepts is a 2014 journal article written in English by Joorabchi A., Mahdi A.E. and published in Journal of Information Science.
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 concepts 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.
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