@inproceedings{ec453025851c46b893525b8d331b2fee,
title = "Quantifying the bias in data links",
abstract = "The main idea behind Linked Data is to connect data from different sources together, in order to develop a hub of shared and publicly accessible knowledge. While the benefit of sharing knowledge is universally recognised, what is less visible is how much results can be affected when the knowledge in one dataset and in the connected ones are not equally distributed. This lack of balance in information, or bias, generally assumed a priori, can actually be quantified to improve the quality of the results of applications and analytics relying on such linked data. In this paper, we propose a process to measure how much bias one dataset contains when compared to another one, by identifying the most affected RDF properties and values within the set of entities that those datasets have in common (defined as the linkset). This process was ran on a wide range of linksets from Linked Data, and in the experiment section we present the results as well as measures of its performance.",
keywords = "Datasets bias, Linked Data, Linksets Analysis",
author = "Ilaria Tiddi and Mathieu D{\textquoteright}aquin and Enrico Motta",
year = "2014",
month = jan,
day = "1",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "531--546",
editor = "Stefan Schlobach and Krzysztof Janowicz and Eero Hyv{\"o}nen and Patrick Lambrix",
booktitle = "Knowledge Engineering and Knowledge Management - 19th International Conference, EKAW 2014, Proceedings",
address = "Germany",
note = "19th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2014 ; Conference date: 24-11-2014 Through 28-11-2014",
}