NELLIE: Never-Ending Linking for Linked Open Data

Abstract

Knowledge graphs (KGs) that follow the Linked Data principles are created daily. However, there are no holistic models for the Linked Open Data (LOD). Building these models(i.e., engineering a pipeline system) is still a big challenge in order to make the LOD vision comes true. In this paper, we address this challenge by presenting NELLIE, a pipeline architecture to build a chain of modules, in which each of our modules addresses one data augmentation challenge. The ultimate goal of the proposed architecture is to build a single fused knowledge graph out of the LOD. NELLIE starts by crawling the available knowledge graphs in the LOD cloud. It then finds a set of matching KG pairs. NELLIE uses a two-phase linking approach for each pair (first an ontology matching phase, then an instance matching phase). Based on the ontology and instance matching, NELLIE fuses each pair of knowledge graphs into a single knowledge graph. The resulting fused KG is then an ideal data source for knowledge-driven applications such as search engines, question answering, digital assistants and drug discovery. Our evaluation shows an improved $Hit \text{@} 1$ score of the link prediction task on the resulting fused knowledge graph by NELLIE in up to 94.44% of the cases. Our evaluation also shows a runtime improvement by several orders of magnitude when comparing our two-phases linking approach with the estimated runtime of linking using a naïve approach.

Keywords

• Knowledge graphs
• linked data
• semantic web
• data augmentation
• link discovery
• data fusion