LOD-a-lot democratizes access to the Linked Open Data (LOD) Cloud by serving more than 28 billion unique triples from 650K datasets from a single self-indexed file. This corpus can be queried online with a sustainable Linked Data Fragments interface, or it can be downloaded and consumed locally: LOD-a-lot is easy to deploy and only requires limited resources (524 GB of disk space and 15.7 GB of RAM), enabling web-scale repeatable experimentation and research from a high-end laptop.
With the Web serving as a huge worldwide data repository, issues related to data semantics (familiar to database modelers since the 1970s) have again become of paramount importance. As Web data comes from heterogeneous, possibly ...
A. Hotho, R. Jaeschke, and K. Lerman. Semantic Web8 (5):
623--624(April 2017)2017 IOS Press and the authors. This is an author produced version of a paper subsequently published in Semantic Web. Uploaded in accordance with the publisher's self-archiving policy..
N. Nakashole, G. Weikum, and F. Suchanek. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, page 1135--1145. Stroudsburg, PA, USA, Association for Computational Linguistics, (2012)