Dataset: https://www.kaggle.com/Paper: http://www.simonrazniewski.We then show that state-of-the-art techniques (Wikidata Property Suggestor, Google search) achieve 61% precision on predicting the winner in high-agreement records, which can be lifted to 74% by using linguistic similarity, but remains still significantly below human performance (87.5% precision).In the paper we develop a dataset of 350 random (entity, property1, property2) records, and use human judgments to determine the more interesting property in each record.Hello,I wanted to make you aware of our new paper "Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties", which deals with the problem of determining the interestingness of Wikidata properties for individual entities.com/2017_ADMA.pdf (to appear at ADMA 2017).srazniewski/ wikidatapropertyranking Best wishes,Simon Razniewski
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