Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

CiteULike is a free service for managing and discovering scholarly references - click here to get started.

Sign In to gain access to subscriptions and/or personal tools.
Journal of Information Science
This Article
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
0165551509105195v1
35/5/563    most recent
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Adefowoke Ojokoh, B.
Right arrow Articles by Oluwole Falaki, S.
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Automated document metadata extraction

Bolanle Adefowoke Ojokoh

Department of Computer Science, Federal University of Technology, Nigeria, bolanleojokoh{at}yahoo.com

Olumide Sunday Adewale

Department of Computer Science, Federal University of Technology, Nigeria

Samuel Oluwole Falaki

Department of Computer Science, Federal University of Technology, Nigeria

Web documents are available in various forms, most of which do not carry additional semantics. This paper presents a model for general document metadata extraction. The model, which combines segmentation by keywords and pattern matching techniques, was implemented using PHP, MySQL, JavaScript and HTML. The system was tested with 40 randomly selected PDF documents (mainly theses). An evaluation of the system was done using standard criteria measures namely precision, recall, accuracy and F-measure. The results show that the model is relatively effective for the task of metadata extraction, especially for theses and dissertations. A combination of machine learning with these rule-based methods will be explored in the future for better results.

Key Words: keywords • metadata • rules • segmentation • theses

This version was published on October 1, 2009

Journal of Information Science, Vol. 35, No. 5, 563-570 (2009)
DOI: 10.1177/0165551509105195
Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?