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 srazniew@gmail.com 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
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