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(a)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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