The Linked Open Data cloud consists of more than 26 billion triples, of which less than 3% are links between knowledge bases. However, such links play a central role in key tasks such as cross-ontology question answering, large-scale inferencing and link-based traversal query execution models. The mere size of the Linked Data Cloud makes manual linking impossible. Consequently, Link Discovery Frameworks have been developed over the last years with the aim of providing means to detect links between knowledge bases automatically. Yet, even the current runtime-optimized frameworks for linking lead to unacceptable runtimes when presented with very large datasets. This paper addresses the time complexity of Link Discovery on very large datasets by presenting and evaluating the parallelization of the time-optimized LIMES framework by means of the MapReduce paradigm.
['Stanley Hillner', 'Axel-Cyrille Ngonga Ngomo']
Parallelizing LIMES for large-scale link discovery