Abstract

The advent of Linked Open Data (LOD) gave birth to a plethora of open datasets freely available to everyone. Accompanied with LOD, a new research field arises focusing on how to handle and to take advantage of this huge amount of data. In this paper, we introduce a novel approach utilizing and aggregating open datasets to compute the most-related entities for a set of weighted input entities. We optimize different algorithms for large semantic datasets enabling combining data from different semantic open sources and providing high quality results even if only limited resources are available. We evaluate our approach on a large encyclopedic dataset. The evaluation results show that our approach efficiently supports different semantic edge types. Application build on our framework provide highly relevant results and explanations for results explaining the semantic relationship between the computed entities in detail.

@inproceedings{semapro_2011_5_10_50027,
author = {Lommatzsch, Andreas and Plumbaum, Till and Albayrak, Sahin},
title = {A linked dataverse knows better: Boosting recommendation quality using semantic knowledge},
booktitle = {Proc. of the 5th Intl. Conf. on Advances in Semantic Processing},
year = {2011},
pages = {97 -- 103},
isbn = {978-1-61208-175-5},
location = {Lisbon, Portugal},
publisher = {IARIA},
address = {Wilmington, DE, USA},
}