946 research outputs found
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
We propose a distance supervised relation extraction approach for
long-tailed, imbalanced data which is prevalent in real-world settings. Here,
the challenge is to learn accurate "few-shot" models for classes existing at
the tail of the class distribution, for which little data is available.
Inspired by the rich semantic correlations between classes at the long tail and
those at the head, we take advantage of the knowledge from data-rich classes at
the head of the distribution to boost the performance of the data-poor classes
at the tail. First, we propose to leverage implicit relational knowledge among
class labels from knowledge graph embeddings and learn explicit relational
knowledge using graph convolution networks. Second, we integrate that
relational knowledge into relation extraction model by coarse-to-fine
knowledge-aware attention mechanism. We demonstrate our results for a
large-scale benchmark dataset which show that our approach significantly
outperforms other baselines, especially for long-tail relations.Comment: To be published in NAACL 201
Graph Neural Networks with Generated Parameters for Relation Extraction
Recently, progress has been made towards improving relational reasoning in
machine learning field. Among existing models, graph neural networks (GNNs) is
one of the most effective approaches for multi-hop relational reasoning. In
fact, multi-hop relational reasoning is indispensable in many natural language
processing tasks such as relation extraction. In this paper, we propose to
generate the parameters of graph neural networks (GP-GNNs) according to natural
language sentences, which enables GNNs to process relational reasoning on
unstructured text inputs. We verify GP-GNNs in relation extraction from text.
Experimental results on a human-annotated dataset and two distantly supervised
datasets show that our model achieves significant improvements compared to
baselines. We also perform a qualitative analysis to demonstrate that our model
could discover more accurate relations by multi-hop relational reasoning
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation
We present a Few-Shot Relation Classification Dataset (FewRel), consisting of
70, 000 sentences on 100 relations derived from Wikipedia and annotated by
crowdworkers. The relation of each sentence is first recognized by distant
supervision methods, and then filtered by crowdworkers. We adapt the most
recent state-of-the-art few-shot learning methods for relation classification
and conduct a thorough evaluation of these methods. Empirical results show that
even the most competitive few-shot learning models struggle on this task,
especially as compared with humans. We also show that a range of different
reasoning skills are needed to solve our task. These results indicate that
few-shot relation classification remains an open problem and still requires
further research. Our detailed analysis points multiple directions for future
research. All details and resources about the dataset and baselines are
released on http://zhuhao.me/fewrel.Comment: EMNLP 2018. The first four authors contribute equally. The order is
determined by dice rolling. Visit our website http://zhuhao.me/fewre
ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion
Knowledge graphs generally suffer from incompleteness, which can be
alleviated by completing the missing information. Deep knowledge convolutional
embedding models based on neural networks are currently popular methods for
knowledge graph completion. However, most existing methods use external
convolution kernels and traditional plain convolution processes, which limits
the feature interaction capability of the model. In this paper, we propose a
novel dynamic convolutional embedding model ConvD for knowledge graph
completion, which directly reshapes the relation embeddings into multiple
internal convolution kernels to improve the external convolution kernels of the
traditional convolutional embedding model. The internal convolution kernels can
effectively augment the feature interaction between the relation embeddings and
entity embeddings, thus enhancing the model embedding performance. Moreover, we
design a priori knowledge-optimized attention mechanism, which can assign
different contribution weight coefficients to multiple relation convolution
kernels for dynamic convolution to improve the expressiveness of the model
further. Extensive experiments on various datasets show that our proposed model
consistently outperforms the state-of-the-art baseline methods, with average
improvements ranging from 11.30\% to 16.92\% across all model evaluation
metrics. Ablation experiments verify the effectiveness of each component module
of the ConvD model
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
Human knowledge provides a formal understanding of the world. Knowledge
graphs that represent structural relations between entities have become an
increasingly popular research direction towards cognition and human-level
intelligence. In this survey, we provide a comprehensive review of knowledge
graph covering overall research topics about 1) knowledge graph representation
learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph,
and 4) knowledge-aware applications, and summarize recent breakthroughs and
perspective directions to facilitate future research. We propose a full-view
categorization and new taxonomies on these topics. Knowledge graph embedding is
organized from four aspects of representation space, scoring function, encoding
models, and auxiliary information. For knowledge acquisition, especially
knowledge graph completion, embedding methods, path inference, and logical rule
reasoning, are reviewed. We further explore several emerging topics, including
meta relational learning, commonsense reasoning, and temporal knowledge graphs.
To facilitate future research on knowledge graphs, we also provide a curated
collection of datasets and open-source libraries on different tasks. In the
end, we have a thorough outlook on several promising research directions
Joint embedding in Hierarchical distance and semantic representation learning for link prediction
The link prediction task aims to predict missing entities or relations in the
knowledge graph and is essential for the downstream application. Existing
well-known models deal with this task by mainly focusing on representing
knowledge graph triplets in the distance space or semantic space. However, they
can not fully capture the information of head and tail entities, nor even make
good use of hierarchical level information. Thus, in this paper, we propose a
novel knowledge graph embedding model for the link prediction task, namely,
HIE, which models each triplet (\textit{h}, \textit{r}, \textit{t}) into
distance measurement space and semantic measurement space, simultaneously.
Moreover, HIE is introduced into hierarchical-aware space to leverage rich
hierarchical information of entities and relations for better representation
learning. Specifically, we apply distance transformation operation on the head
entity in distance space to obtain the tail entity instead of translation-based
or rotation-based approaches. Experimental results of HIE on four real-world
datasets show that HIE outperforms several existing state-of-the-art knowledge
graph embedding methods on the link prediction task and deals with complex
relations accurately.Comment: Submitted to Big Data research one year ag
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