Abstract
Continuously discovering novel entities in news and Web data is important for Knowledge Base (KB) maintenance. One of the key challenges is to decide whether an entity mention refers to an in-KB or out-of-KB entity. We propose a principled approach that learns a novel entity classifier by modeling mention and entity representation into multiple feature spaces, including contextual, topical, lexical, neural embedding and query spaces. Different from most previous studies that address novel entity discovery as a submodule of entity linking systems, our model is more a generalized approach and can be applied as a pre-filtering step of novel entities for any entity linking systems. Experiments on three real-world datasets show that our method significantly outperforms existing methods on identifying novel entities.
| Original language | English (US) |
|---|---|
| Title of host publication | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 |
| Publisher | AAAI press |
| Pages | 3073-3079 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781577357605 |
| State | Published - Jan 1 2016 |
| Event | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States Duration: Feb 12 2016 → Feb 17 2016 |
Publication series
| Name | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 |
|---|
Other
| Other | 30th AAAI Conference on Artificial Intelligence, AAAI 2016 |
|---|---|
| Country | United States |
| City | Phoenix |
| Period | 2/12/16 → 2/17/16 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
Cite this
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Exploring multiple feature spaces for novel entity discovery. / Wu, Zhaohui; Song, Yang; Giles, C. Lee.
30th AAAI Conference on Artificial Intelligence, AAAI 2016. AAAI press, 2016. p. 3073-3079 (30th AAAI Conference on Artificial Intelligence, AAAI 2016).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Exploring multiple feature spaces for novel entity discovery
AU - Wu, Zhaohui
AU - Song, Yang
AU - Giles, C. Lee
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Continuously discovering novel entities in news and Web data is important for Knowledge Base (KB) maintenance. One of the key challenges is to decide whether an entity mention refers to an in-KB or out-of-KB entity. We propose a principled approach that learns a novel entity classifier by modeling mention and entity representation into multiple feature spaces, including contextual, topical, lexical, neural embedding and query spaces. Different from most previous studies that address novel entity discovery as a submodule of entity linking systems, our model is more a generalized approach and can be applied as a pre-filtering step of novel entities for any entity linking systems. Experiments on three real-world datasets show that our method significantly outperforms existing methods on identifying novel entities.
AB - Continuously discovering novel entities in news and Web data is important for Knowledge Base (KB) maintenance. One of the key challenges is to decide whether an entity mention refers to an in-KB or out-of-KB entity. We propose a principled approach that learns a novel entity classifier by modeling mention and entity representation into multiple feature spaces, including contextual, topical, lexical, neural embedding and query spaces. Different from most previous studies that address novel entity discovery as a submodule of entity linking systems, our model is more a generalized approach and can be applied as a pre-filtering step of novel entities for any entity linking systems. Experiments on three real-world datasets show that our method significantly outperforms existing methods on identifying novel entities.
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UR - http://www.scopus.com/inward/citedby.url?scp=84997564622&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84997564622
T3 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
SP - 3073
EP - 3079
BT - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
PB - AAAI press
ER -