Paper Info
Title
Answer-enhanced Path-aware Relation Detection over Knowledge Base
Abstract
Knowledge Based Question Answering (KBQA) is one of the most promising approaches to provide suitable answers for the queries posted by users. Relation detection that aims to take full advantage of the substantial knowledge contained in knowledge base (KB) becomes increasingly important. Significant progress has been made in performing relation detection over KB. However, recent deep neural networks that achieve the state of the art on KB-based relation detection task only consider the context information of question sentences rather than the relatedness between question and answer candidates, and exclusively extract the relation from KB triple rather than learn informative relational path. In this paper, we propose a Knowledge-driven Relation Detection network (KRD) to interactively learn answer-enhanced question representations and path-aware relation representations for relation detection. A Siamese LSTM is employed into a similarity matching process between the question representation and relation representation. Experimental results on the SimpleQuestions and WebQSP datasets demonstrate that KRD outperforms the state-of-the-art methods. In addition, a series of ablation test show the robust superiority of the proposed method.
Year
DOI
Venue
2019
10.1145/3331184.3331328
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
Keywords
Field
DocType
knowledge base, relation detection, relational path inference, representation learning
Monad (category theory),Question answering,Information retrieval,Computer science,Knowledge base,Similarity matching,Deep neural networks,Feature learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-6172-9
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Daoyuan Chen100.68
Min Yang27720.41
Zheng Hai-Tao314224.39
yaliang li462950.87
Shen Ying57323.48