Wikiref: Wikilinks as a Route to Recommending Appropriate References for Scientific Wikipedia Pages
Authors | Abhik Jana Pranjal Kanojiya Pawan Goyal Animesh Mukherjee |
|---|---|
Publication date | 2018 |
Links | Original Preprint |
Wikiref: Wikilinks as a Route to Recommending Appropriate References for Scientific Wikipedia Pages - scientific work related to Wikipedia quality published in 2018, written by Abhik Jana, Pranjal Kanojiya, Pawan Goyal and Animesh Mukherjee.
Overview
The exponential increase in the usage of Wikipedia as a key source of scientific knowledge among the researchers is making it absolutely necessary to metamorphose this knowledge repository into an integral and self-contained source of information for direct utilization. Unfortunately, the references which support the content of each Wikipedia entity page, are far from complete. Why are the reference section ill-formed for most Wikipedia pages? Is this section edited as frequently as the other sections of a page? Can there be appropriate surrogates that can automatically enhance the reference section? In this paper, authors propose a novel two step approach -- WikiRef -- that (i) leverages the wikilinks present in a scientific Wikipedia target page and, thereby, (ii) recommends highly relevant references to be included in that target page appropriately and automatically borrowed from the reference section of the wikilinks. In the first step, authors build a classifier to ascertain whether a wikilink is a potential source of reference or not. In the following step, authors recommend references to the target page from the reference section of the wikilinks that are classified as potential sources of references in the first step. Authors perform an extensive evaluation of approach on datasets from two different domains -- Computer Science and Physics. For Computer Science authors achieve a notably good performance with a precision@1 of 0.44 for reference recommendation as opposed to 0.38 obtained from the most competitive baseline. For the Physics dataset, authors obtain a similar performance boost of 10% with respect to the most competitive baseline.
