We are very pleased to announce that our group got one paper accepted for presentation at the 14th Extended Semantic Web Conference (ESWC 2017) research track, held in Portoroz, Slovenia from 28th of May to the 1st of June. The ESWC is an important international forum for the Semantic Web / Linked Data Community.
Abstract. A significant portion of the evolution of Linked Data datasets lies in updating the links to other datasets. An important challenge when aiming to update these links automatically under the open-world assumption is the fact that usually only positive examples for the links exist. We address this challenge by presenting and evaluating WOMBAT , a novel approach for the discovery of links between knowledge bases that relies exclusively on positive examples. WOMBAT is based on generalisation via an upward refinement operator to traverse the space of link specification. We study the theoretical characteristics of WOMBAT and evaluate it on 8 different benchmark datasets. Our evaluation suggests that WOMBAT outperforms state-of-the-art supervised approaches while relying on less information. Moreover, our evaluation suggests that WOMBAT’s pruning algorithm allows it to scale well even on large datasets.
This work is supported by the European Union’s H2020 research and innovation action HOBBIT (GA no. 688227), the European Union’s H2020 research and innovation action SLIPO (GA no. 731581) and the BMWI Project GEISER (project no. 01MD16014).