Paper Info
Title
MetaP: Meta Pattern Learning for One-Shot Knowledge Graph Completion
Abstract
ABSTRACTKnowledge Graphs (KGs) are widely used in various applications of information retrieval. Despite the large scale of KGs, they are still facing incomplete problems. Conventional approaches on Knowledge Graph Completion (KGC) require a large number of training instances for each relation. However, long-tail relations which only have a few related triples are ubiquitous in KGs. Therefore, it is very difficult to complete the long-tail relations. In this paper, we propose a meta pattern learning framework (MetaP) to predict new facts of relations under a challenging setting where there is only one reference for each relation. Patterns in data are representative regularities to classify data. Triples in KGs also conform to relation-specific patterns which can be used to measure the validity of triples. Our model extracts the patterns effectively through a convolutional pattern learner and measures the validity of triples accurately by matching query patterns with reference patterns. Extensive experiments demonstrate the effectiveness of our method. Besides, we build a few-shot KGC dataset of COVID-19 to assist the research process of the new coronavirus.
Year
DOI
Venue
2021
10.1145/3404835.3463086
Research and Development in Information Retrieval
Keywords
DocType
Citations 
Knowledge Graph Completion, One-Shot, Pattern
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Zhiyi Jiang100.68
Jianliang Gao210620.98
Xinqi Lv300.34