Besides finding trends and unveiling typical patterns, modern information retrieval is increasingly more interested in the discovery of surprising information in textual datasets. In this work we focus on finding "unexpected links" in hyperlinked document corpora when documents are assigned to categories. To achieve this goal, we model the hyperlinks graph through node categories: the presence of an arc is fostered or discouraged by the categories of the head and the tail of the arc. Specifically, we determine a latent category matrix that explains common links. The matrix is built using a margin-based online learning algorithm (Passive-Aggressive), which makes us able to process graphs with @math links in less than @math minutes. We show that our method provides better accuracy than most existing text-based techniques, with higher efficiency and relying on a much smaller amount of information. It also provides higher precision than standard link prediction, especially at low recall levels; the two methods are in fact shown to be orthogonal to each other and can therefore be fruitfully combined.
Some authors approach the strictly related problem of determining lacking content (called in @cite_38 ) rather than unexpected information, using Wikipedia as knowledge base. A similar task is undertaken by @cite_3 , this time assuming the dual approach of finding content holes in Wikipedia using the web as a source of information.
@cite_38 @cite_3
1983361085 2071018679 1970396504 1755289444
With the huge amount of data on the Web, looking for desired information can be a time consuming task. Wikipedia is a very helpful tool as it is the largest most popular general reference site on the internet. Most search engines rank Wikipedia pages among the top listed results. However, because many articles on Wikipedia are manually updated by users, there are several articles that lack information and need to be upgraded. Those lacking information can sometimes be found on the web. Uprooting this information from the web will involve a time consuming process of reading, analyzing and summarizing the information for the user. In order to support the user search process and help Wikipedia contributors in the updating process of articles, we propose a method of finding valuable complementary information on the web. Experiments showed that our method was quite effective in retrieving important complementary information from the web pages. In this paper we address the problem of discovering missing hypertext links in Wikipedia. The method we propose consists of two steps: first, we compute a cluster of highly similar pages around a given page, and then we identify candidate links from those similar pages that might be missing on the given page. The main innovation is in the algorithm that we use for identifying similar pages, LTRank, which ranks pages using co-citation and page title information. Both LTRank and the link discovery method are manually evaluated and show acceptable results, especially given the simplicity of the methods and conservativeness of the evaluation criteria. Wikipedia is the largest monolithic repository of human knowledge. In addition to its sheer size, it represents a new encyclopedic paradigm by interconnecting articles through hyperlinks. However, since these links are created by human authors, links one would expect to see are often missing. The goal of this work is to detect such gaps automatically. In this paper, we propose a novel method for augmenting the structure of hyperlinked document collections such as Wikipedia. It does not require the extraction of any manually defined features from the article to be augmented. Instead, it is based on principal component analysis, a well-founded mathematical generalization technique, and predicts new links purely based on the statistical structure of the graph formed by the existing links. Our method does not rely on the textual content of articles; we are exploiting only hyperlinks. A user evaluation of our technique shows that it improves the quality of top link suggestions over the state of the art and that the best predicted links are significantly more valuable than the 'average' link already present in Wikipedia. Beyond link prediction, our algorithm can potentially be used to point out topics an article misses to cover and to cluster articles semantically. Hyperlinks are an essential feature of the World Wide Web. They are especially important for online encyclopedias such as Wikipedia: an article can often only be understood in the context of related articles, and hyperlinks make it easy to explore this context. But important links are often missing, and several methods have been proposed to alleviate this problem by learning a linking model based on the structure of the existing links. Here we propose a novel approach to identifying missing links in Wikipedia. We build on the fact that the ultimate purpose of Wikipedia links is to aid navigation. Rather than merely suggesting new links that are in tune with the structure of existing links, our method finds missing links that would immediately enhance Wikipedia's navigability. We leverage data sets of navigation paths collected through a Wikipedia-based human-computation game in which users must find a short path from a start to a target article by only clicking links encountered along the way. We harness human navigational traces to identify a set of candidates for missing links and then rank these candidates. Experiments show that our procedure identifies missing links of high quality.