LOTUS: Adaptive text search for big linked data

Abstract

Finding relevant resources on the Semantic Web today is a dirty job: no centralized query service exists and the support for natural language access is limited. We present LOTUS: Linked Open Text UnleaShed, a text-based entry point to a massive subset of today’s Linked Open Data Cloud. Recognizing the use case dependency of resource retrieval, LOTUS provides an adaptive framework in which a set of matching and ranking algorithms are made available. Researchers and developers are able to tune their own LOTUS index by choosing and combining the matching and ranking algorithms that suit their use case best. In this paper, we explain the LOTUS approach, its implementation and the functionality it provides. We demonstrate the ease with which LOTUS enables text-based resource retrieval at an unprecedented scale in concrete and domain-specific scenarios. Finally, we provide evidence for the scalability of LOTUS with respect to the LOD Laundromat, the largest collection of easily accessible Linked Open Data currently available.