On Link Discovery using a Hybrid Approach

Abstract

With the growth of the Linked Data Web, time-efficient link discovery frameworks have become indispensable for implementing the fourth Linked Data principle, i.e., the provision of links between data sources. Due to the sheer size of the Data Web, detecting links even when using trivial link specifications based on a single property can be time-demanding. Moreover, non-trivial link discovery tasks require complex link specifications and are consequently even more challenging to optimize with respect to runtime. In this paper, we present a hybrid approach to link discovery that allows combining time-efficient algorithms specialized on specific data types. Especially, we present the HYPPO algorithm, which can process numeric data efficiently. These algorithms are combined by using original insights on the translation of complex link specifications to combinations of atomic specifications via a series of operations on sets and filters. We show in nine experiments that our approach outperforms SILK 2.5.1 with respect to runtime by up to four orders of magnitude.