219,931 research outputs found
One-Shot Relational Learning for Knowledge Graphs
Knowledge graphs (KGs) are the key components of various natural language
processing applications. To further expand KGs' coverage, previous studies on
knowledge graph completion usually require a large number of training instances
for each relation. However, we observe that long-tail relations are actually
more common in KGs and those newly added relations often do not have many known
triples for training. In this work, we aim at predicting new facts under a
challenging setting where only one training instance is available. We propose a
one-shot relational learning framework, which utilizes the knowledge extracted
by embedding models and learns a matching metric by considering both the
learned embeddings and one-hop graph structures. Empirically, our model yields
considerable performance improvements over existing embedding models, and also
eliminates the need of re-training the embedding models when dealing with newly
added relations.Comment: EMNLP 201
Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach
Knowledge base completion (KBC) aims to predict missing information in a
knowledge base.In this paper, we address the out-of-knowledge-base (OOKB)
entity problem in KBC:how to answer queries concerning test entities not
observed at training time. Existing embedding-based KBC models assume that all
test entities are available at training time, making it unclear how to obtain
embeddings for new entities without costly retraining. To solve the OOKB entity
problem without retraining, we use graph neural networks (Graph-NNs) to compute
the embeddings of OOKB entities, exploiting the limited auxiliary knowledge
provided at test time.The experimental results show the effectiveness of our
proposed model in the OOKB setting.Additionally, in the standard KBC setting in
which OOKB entities are not involved, our model achieves state-of-the-art
performance on the WordNet dataset. The code and dataset are available at
https://github.com/takuo-h/GNN-for-OOKBComment: This paper has been accepted by IJCAI1
Graph-based Neural Multi-Document Summarization
We propose a neural multi-document summarization (MDS) system that
incorporates sentence relation graphs. We employ a Graph Convolutional Network
(GCN) on the relation graphs, with sentence embeddings obtained from Recurrent
Neural Networks as input node features. Through multiple layer-wise
propagation, the GCN generates high-level hidden sentence features for salience
estimation. We then use a greedy heuristic to extract salient sentences while
avoiding redundancy. In our experiments on DUC 2004, we consider three types of
sentence relation graphs and demonstrate the advantage of combining sentence
relations in graphs with the representation power of deep neural networks. Our
model improves upon traditional graph-based extractive approaches and the
vanilla GRU sequence model with no graph, and it achieves competitive results
against other state-of-the-art multi-document summarization systems.Comment: In CoNLL 201
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