Paper Info
Title
HEER: Heterogeneous graph embedding for emerging relation detection from news
Abstract
Real-world knowledge is growing rapidly nowadays. New entities arise with time, resulting in large volumes of relations that do not exist in current knowledge graphs (KGs). These relations containing at least one new entity are called emerging relations. They often appear in news, and hence the latest information about new entities and relations can be learned from news timely. In this paper, we focus on the problem of discovering emerging relations from news. However, there are several challenges for this task: (1) at the beginning, there is little information for emerging relations, causing problems for traditional sentence-based models; (2) no negative relations exist in KGs, creating difficulties in utilizing only positive cases for emerging relation detection from news; and (3) new relations emerge rapidly, making it necessary to keep KGs up to date with the latest emerging relations. In order to address these issues, we start from a global graph perspective and propose a novel Heterogeneous graph Embedding framework for Emerging Relation detection (HEER) that learns a classifier from positive and unlabeled instances by utilizing information from both news and KGs. Furthermore, we implement HEER in an incremental manner to timely update KGs with the latest detected emerging relations. Extensive experiments on real-world news datasets demonstrate the effectiveness of the proposed HEER model.
Year
DOI
Venue
2016
10.1109/BigData.2016.7840673
2016 IEEE International Conference on Big Data (Big Data)
Keywords
Field
DocType
Heterogeneous Networks,Emerging Relations,Embedding
Data science,Data mining,Computer science,Artificial intelligence,Classifier (linguistics),Graph,Embedding,Graph embedding,Feature extraction,Heterogeneous network,Big data,Sentence,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4673-9006-4
1
0.35
References 
Authors
28
6
Name
Order
Citations
PageRank
Jingyuan Zhang165360.53
Chun-Ta Lu218315.10
Mianwei Zhou31438.28
Sihong Xie448333.80
Yi Chang5146386.17
Philip S. Yu6306703474.16