FusE: Entity-Centric Data Fusion on Linked Data

Steffen Thoma, Andreas Thalhammer, Andreas Harth, Rudi Studer

Abstract: Many current web pages include structured data which can directly be processed and used. Search engines, in particular, gather that structured data and provide question answering capabilities over the integrated data with an entity-centric presentation of the results. Due to the decentralized nature of the web, multiple structured data sources can provide similar information about an entity. But data from different sources may involve different vocabularies and modeling granularities, which makes integration difficult. We present FusE, an approach that identifies similar entity-specific data across sources, independent of the vocabulary and data modeling choices. We apply our method along the scenario of a trustable knowledge panel, conduct experiments in which we identify and process entity data from web sources, and compare the output to a competing system. The results underline the advantages of the presented entity-centric data fusion approach.