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 | LinkedIn | Portfolio
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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