3 research outputs found
WikiLinkGraphs: A Complete, Longitudinal and Multi-Language Dataset of the Wikipedia Link Networks
Wikipedia articles contain multiple links connecting a subject to other pages
of the encyclopedia. In Wikipedia parlance, these links are called internal
links or wikilinks. We present a complete dataset of the network of internal
Wikipedia links for the largest language editions. The dataset contains
yearly snapshots of the network and spans years, from the creation of
Wikipedia in 2001 to March 1st, 2018. While previous work has mostly focused on
the complete hyperlink graph which includes also links automatically generated
by templates, we parsed each revision of each article to track links appearing
in the main text. In this way we obtained a cleaner network, discarding more
than half of the links and representing all and only the links intentionally
added by editors. We describe in detail how the Wikipedia dumps have been
processed and the challenges we have encountered, including the need to handle
special pages such as redirects, i.e., alternative article titles. We present
descriptive statistics of several snapshots of this network. Finally, we
propose several research opportunities that can be explored using this new
dataset.Comment: 10 pages, 3 figures, 7 tables, LaTeX. Final camera-ready version
accepted at the 13TH International AAAI Conference on Web and Social Media
(ICWSM 2019) - Munich (Germany), 11-14 June 201
Semantic Unlink Prediction in Evolving Social Networks through Probabilistic Description Logic
Recently, prediction of new links between two individuals in social networks has gained a lot of attention. However, to fully understand and predict how the network evolves through time, ending relationships also need to be predicted. Although most approaches use graph-based methods for link prediction, these may not be suited for the unlink prediction task. In this paper, we propose an approach for unlink prediction that uses information about the domain of discourse through a probabilistic ontology, specified in the probabilistic description logic CRALC. We empirically evaluated our approach comparing it with standard graph-based and some state of the art unlink methods. The results shows significant improvement on detecting unlinks when considering our proposal. 漏 2014 IEEE.Trabajo de investigaci贸