SERIMI - Resource Description Similarity, RDF Instance Matching and Interlinking

Abstract

The interlinking of datasets published in the Linked Data Cloud is a challenging problem and a key factor for the success of the Semantic Web. Manual rule-based methods are the most effective solution for the problem, but they require skilled human data publishers going through a laborious, error prone and time-consuming process for manually describing rules mapping instances between two datasets. Thus, an automatic approach for solving this problem is more than welcome. In this paper, we propose a novel interlinking method, SERIMI, for solving this problem automatically. SERIMI matches instances between a source and a target datasets, without prior knowledge of the data, domain or schema of these datasets. Experiments conducted with benchmark collections demonstrate that our approach considerably outperforms state-of-the-art automatic approaches for solving the interlinking problem on the Linked Data Cloud.

Cited by in corpus (5)

• TheSoz: A SKOS Representation of the Thesaurus for the Social Sciences
• Generate FAIR Literature Surveys with Scholarly Knowledge Graphs
• Knowledge Graph Curation: A Practical Framework
• Duplication Detection in Knowledge Graphs: Literature and Tools
• A Proposal for the Characterization of Multi-Dimensional Inter-relationships of RDF Graphs Based on Set Theoretic Approach