RAVEN: Towards Zero-Configuration Link Discovery

With the growth of the Linked Data Web, time-efficient approaches for computing links between data sources have become indispensable. Yet, in many cases, determining the right specification for a link discovery problem is a tedious task that must still be carried out manually. In this article we present RAVEN, an approach for the semiautomatic determination of link specifications. Our approach is based on the combination of stable solutions of matching problems and active learning leveraging the time-efficient link discovery framework LIMES. RAVEN is designed to require a small number of interactions with the user in order to generate classifiers of high accuracy. We focus with RAVEN on the computation and configuration of Boolean and weighted classifiers, which we evaluate in three experiments against link specifications created manually. Our evaluation shows that we can compute linking configurations that achieve more than 90% F-score by asking the user to verify at most twelve potential links.