Presentation given at NLP&DBpedia workshop on 18 October 2016. The presentation accompanies the work described in: https://nlpdbpedia2016.files.wordpress.com/2016/09/nlpdbpedia2016_paper_9.pdf

1. Entity Typing Using
Distributional Semantics and DBpedia
Marieke van Erp and Piek Vossen

2. Conclusions
• Finegrained entity typing is necessary for semantic
queries over text
• Search space for word2vec is large, topics help
• Combining Distributional Semantics with DBpedia can
help overcome NIL and Dark Entities
https://github.com/MvanErp/entity-typing/

3. Dark entities: little or no information available in KB
https://github.com/MvanErp/entity-typing/

4. Dark entities: little or no information available in KB
https://github.com/MvanErp/entity-typing/

5. Distributional Semantics
• Similar concepts (denoted by words) occur in similar
contexts
• Word2Vec (Mikolov et al., 2013) explores this notion in a
popular implementation
Sushi
Teriyaki
Udon
Okonomiyaki
Soba
Sashimi
Kimono
Yukata
Nemaki
KFC
Steak
Hamburger
McDonald’s
Jeans
T-shirt
Skirt

6. Research Question:
• Can we predict the type of the concept ‘Sushi’ by
modelling it in a distributional semantics space and
comparing its vector to the vectors of concepts for which
we do know the type?
Sushi
Teriyaki
Udon
Okonomiyaki
Soba
Sashimi
Kimono
Yukata
Nemaki
KFC
Steak
Hamburger
McDonald’s
Jeans
T-shirt
Skirt

7. Setup
• 7 Named Entity Linking Benchmark datasets (AIDA-YAGO,
2014 NEEL, 2015 NEEL, OKE2015, RSS500, WES2015,
Wikinews)
• 3 Word2Vec models: GoogleNews, English Wikipedia,
Reuters RCV1*
• Compare all entities within datasets to each other and return
highest ranking type (as taken from DBpedia)
* AIDA-YAGO is part of Reuters RCV1
https://github.com/MvanErp/entity-typing/

8. Initial results
• Not so great?
https://github.com/MvanErp/entity-typing/

9. Initial results (some footnotes)
• Ranking approach favours fine-grained entity types
• The Word2Vec corpus matters! NEEL2014&2015 are derived
from Tweets, typically low coverage when querying news
• Smaller datasets (Wikinews, WES2015, OKE2015) do better?
https://github.com/MvanErp/entity-typing/

10. Let’s zoom in
on topics
• Initially, all entities
within a benchmark
dataset were
compared to all other
entities.
• What if we only
compare entities from
sports documents to
other entities from
sports documents?
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AIDA−YAGO Coarsegrained Categories GoogleNews Fine
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AIDA−YAGO Coarsegrained Categories RCV1 Fine
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AIDA−YAGO Coarsegrained Categories Wikipedia Fine
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AIDA−YAGO Finegrained Categories GoogleNews Fine
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AIDA−YAGO Finegrained Categories RCV1 Fine
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AIDA−YAGO Finegrained Categories Wikipedia Fine
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https://github.com/MvanErp/entity-typing/

11. Conclusions and Future Work
• Difficult task, but topics help
• Ranking needs to be improved
• Multi-class classification (KFC: food & organisation,
Arnold Schwarzenegger: Actor & Politician)
• What else can we discover beyond type?
https://github.com/MvanErp/entity-typing/

12. Thank you!
https://github.com/MvanErp/entity-typing/

13. This research was made possible by the CLARIAH-CORE project
financed by NWO.
http://www.clariah.nl