The feasibility of investing in manual correction of metadata for a large-scale digital library

Chen, Hung-Hsuan; Khabsa, Madian; Giles, C. Lee

doi:10.1109/jcdl.2014.6970172

IEEE/ACM Joint Conference on Digital Libraries 2014

DOI: 10.1109/jcdl.2014.6970172

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The feasibility of investing in manual correction of metadata for a large-scale digital library

Hung-Hsuan Chen¹,

Madian Khabsa²,

C. Lee Giles³

Abstract: Given a large-scale digital library that automatically crawls and parses PDF files to generate metadata for documents and authors, we estimate the number of person-hours required to correct a small portion of the metadata, in the hope that a large portion of users can benefit from these corrections. We obtain users requests by analyzing CiteSeerX's log files from September 2009 to March 2013. We found that the distribution of users requests for search is highly imbalanced: most document search queries and auth… Show more

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Cited by 2 publications

References 13 publications

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Toward Building an Academic Search Engine Understanding the Purposes of the Matched Sentences in an Abstract

et al. 2021

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This paper introduces an automatic approach to understand the purposes of each sentence in the abstract of an academic document. Specifically, computers can label each sentence in the abstract as being related to one or several of six aspects -"BACKGROUND", "OBJECTIVES", "METHODS", "RESULTS", "CONCLUSIONS", and "OTHERS". Experimental results obtained on a real dataset show that the labeling methodology outperforms baseline methods. We also build a prototype academic search engine to demonstrate the use of this new design. Users may search for articles containing keywords related to any of these six aspects to better meet their search goals.

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Toward Building an Academic Search Engine Understanding the Purposes of the Matched Sentences in an Abstract

et al. 2021

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Experience: Analyzing Missing Web Page Visits and Unintentional Web Page Visits from the Client-side Web Logs

Hsu

Chen

2022

J. Data and Information Quality

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Web logs have been widely used to represent the web page visits of online users. However, we found that web logs in Chrome’s browsing history only record 57\% of users’ visited websites, i.e., nearly half of a user’s website visits are not recorded. Additionally, 5.1\% of the visits recorded in the web log occur because of unconscious user actions, i.e., these page visits are not initiated from users. We created a Google Chrome plugin and recruited users to install the plugin to collect and analyze the conscious URL visits, unconscious URL visits, and “missing” URL visits (i.e., the visits unrecorded in the traditional web log). We reported the statistics of these behaviors. We showed that sorting popular website categories based on traditional web logs differs from the rankings obtained when including missing visits or excluding unintentional visits. We predicted users’ future behaviors based on three types of training data – all the visits in modern web logs, the intentional visits in web logs, and the intentional visits plus missing visits in web logs. The experimental results indicate that missing visits in web logs may contain additional information, and unintentional visits in web logs may contain more noise than information for user modeling. Consequently, we need to be careful of the observations and conclusions derived from web log analyses because the web log data could be an incomplete and noisy dataset of a user’s visited web pages.

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The feasibility of investing in manual correction of metadata for a large-scale digital library

Cited by 2 publications

References 13 publications

Toward Building an Academic Search Engine Understanding the Purposes of the Matched Sentences in an Abstract

Toward Building an Academic Search Engine Understanding the Purposes of the Matched Sentences in an Abstract

Experience: Analyzing Missing Web Page Visits and Unintentional Web Page Visits from the Client-side Web Logs

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