Hi Simon, This is amazing. Congratulations and Kudos to your team.
I just liked your Kaggle Dataset and would love to experiment with it by developing a new kernel. Please let me know if I can be of any help. Have a nice day. Regards Amit Kumar Jaiswal ᐧ Amit Kumar Jaiswal Mozilla Representative <http://reps.mozilla.org/u/amitkumarj441> | LinkedIn <http://in.linkedin.com/in/amitkumarjaiswal1> | Portfolio <http://amitkumarj441.github.io> New Delhi, India M : +91-8081187743 | T : @AMIT_GKP | PGP : EBE7 39F0 0427 4A2C On Sat, Aug 26, 2017 at 6:18 PM, Simon Razniewski <[email protected]> wrote: > 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. > > 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. > 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). > > Paper: http://www.simonrazniewski.com/2017_ADMA.pdf (to appear at ADMA > 2017). > Dataset: https://www.kaggle.com/srazniewski/wikidatapropertyranking > > Best wishes, > Simon Razniewski > > > _______________________________________________ > Wikidata mailing list > [email protected] > https://lists.wikimedia.org/mailman/listinfo/wikidata > >
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