Hi Amit,
I look forward to your kernel! The things we tried for approximating human
judgment are so far all somewhat basic (property frequency, regression
trained on entities that have one but not the other property, topic
similarity between entities and properties), so any ideas that can improve
the approximations would be great contributions.
Let me know if you have technical questions about the dataset.
Cheers,
Simon
On 26 August 2017 at 15:37, AMIT KUMAR JAISWAL <amitkumarj441(a)gmail.com>
wrote:
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 <+91%2080811%2087743> | 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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