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# The Normalized Freebase Distance (NFD)

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Poster on the Normalized Freebase Distance, presented at ESWC 2014.
Code can be found at https://github.com/FredericGodin/TheNormalizedFreebaseDistance.

Published in: Data & Analytics
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### The Normalized Freebase Distance (NFD)

1. 1. http://multimedialab.elis.ugent.be Ghent University – iMinds -- Multimedia Lab, Belgium1 http://dme.rwth-aachen.de/ RWTH Aachen University – Data Management and Exploration Group, Germany2 Fréderic Godin1, Tom De Nies1, Christian Beecks2, Laurens De Vocht1, Wesley De Neve1, Erik Mannens1, Thomas Seidl2 and Rik Van de Walle1 Extended Semantic Web Conference (ESWC) The Normalized Freebase Distance May 25th – May 29th 2014 What if a bass would not be similar to a bass guitar and Apple would not be fruit? Use of Normalized Google/Bing Distance for measuring similarity The Normalized Freebase Distance Makes use of structured linked data instead of search engines Simple, easy to calculate distance metric for semantic concepts Concept 1 Concept 2 Concept 1 Concept 2 What is it? How does it work? Examples C1 C2 C1,C2 Another concept in Freebase NFD(C1,C2) = f( , , ) C1 C2 C1,C2 NBD NFD salmon – trout 0.072 0.070 salmon – bass 0.133 0.087 salmon – bass guitar 0.283 0.274 bass – bass guitar 0.086 0.276 NBD NFD fruit – banana 0.073 0.086 fruit – apple 0.065 0.072 fruit – Apple 0.065 0.166 apple – Apple 0 0.164 Problem Solution Fails on ambigious concepts (The above numbers are distances . Close to 0 means a very short distance and therefore very similar)