"Semantic Similarity & Taxonomic Distance: Using Structured Metadata in Data Science Models", Andrew Clegg, Data Scientist at Etsy Watch videos from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo Visit the conference website to learn more: www.datanatives.io Follow Data Natives: https://www.facebook.com/DataNatives https://twitter.com/DataNativesConf https://www.youtube.com/c/DataNatives Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS About the Author: Andrew joined Etsy in 2014, and lives in London, making him their first data scientist outside the USA. Since then he has worked on a variety of challenges including localized recommendations, image similarity search, and anomaly detection. Prior to Etsy, he spent almost 15 years designing machine learning workflows, and building search and analytics services, in academia, startups and enterprises, and in an ever-growing list of research areas including biomedical informatics, computational linguistics, social media analytics, and educational gaming. These days he’s interested in probabilistic algorithms and data structures, online learning, deep learning, data visualization, and the convergence of search and recommender systems. He can count to over 1000 on his fingers but doesn’t know how to drive a car.

1. Andrew Clegg
Data Natives, Berlin, 2016

2. Semantic Similarity and
Taxonomic Distance:
Using Structured Metadata in
Data Science Models

7. Semantic
Similarity:
Some Uses
7

8. digital_prints music_and_movie_posters (0.84)
digital_prints digital_prints (1.00)
digital_prints lithographs (0.79)
lens_cases lens_cases (1.00)
lens_cases camera_cases (0.92)
lens_cases laptop_bags (0.77)
True Label Prediction / Score
8

9. Before After

10. Measuring
Semantic
Similarity
10

11. All Items
Shoes
Boots Sneakers & Athletic Shoes
Hi Tops
Sandals
SkatesTie Sneakers
Path Length
sim(node1, node2) = 1
len(node1, node2)
2 ⇥ max depth

12. home_and_living 1
kitchen_and_dining 2
cookware 3
pots_and_pans 4
pans 5
skillets 6
Node Depth
12

13. All Items
Shoes
Boots Sneakers & Athletic Shoes
Hi Tops
Sandals
SkatesTie Sneakers
Wu & Palmer 1994
sim(node1, node2) =
2 ⇥ depth(ancestor)
len(node1, node2) + 2 ⇥ depth(ancestor)

14. All Items
Shoes
Boots Sneakers & Athletic Shoes
Hi Tops
Sandals
SkatesTie Sneakers
Sussna 1993
0.17
0.17
0.11
dist(parent, child) =
1 1 ÷ num children(parent)
2 ⇥ depth(child)

15. Information-
Based Methods
15

16. How frequent is this node or any
of its descendants in your data?
Information Content
16
I(node) = log P(node)
| {z }

17. All Items
Shoes
Boots Sneakers & Athletic Shoes
Hi Tops
Sandals
SkatesTie Sneakers
Resnik 1995
sim(node1, node2) = log P(ancestor)
P(shoes) = 0.14
-log P(shoes) = 2.83

18. All Items
Shoes
Boots Sneakers & Athletic Shoes
Hi Tops
Sandals
SkatesTie Sneakers
Lin 1998
sim(node1, node2) =
2 ⇥ log P(ancestor)
log P(node1) + log P(node2)
P(shoes) = 0.14
-log P(shoes) = 2.83
P(boots) = 0.04
-log P(boots) = 4.64
P(tie sneakers) = 0.03
-log P(tie sneakers) = 5.06

19. Which Method
Wins?
19

20. Thanks!
See this paper for all the references: Budanitsky &
Hearst, Computational Linguistics 32 (1), 2006.
Find me on Twitter: @andrew_clegg
PS We’re hiring! https://www.etsy.com/careers/