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 …
represent structural relations between entities have become an increasingly popular …
More data, more relations, more context and more openness: A review and outlook for relation extraction
Relational facts are an important component of human knowledge, which are hidden in vast
amounts of text. In order to extract these facts from text, people have been working on …
amounts of text. In order to extract these facts from text, people have been working on …
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 …
sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The …
Connecting the dots: Document-level neural relation extraction with edge-oriented graphs
Document-level relation extraction is a complex human process that requires logical
inference to extract relationships between named entities in text. Existing approaches use …
inference to extract relationships between named entities in text. Existing approaches use …
Neural logic machines
We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both
inductive learning and logic reasoning. NLMs exploit the power of both neural networks---as …
inductive learning and logic reasoning. NLMs exploit the power of both neural networks---as …
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 …
data which is prevalent in real-world settings. Here, the challenge is to learn accurate" few …
Chains of reasoning over entities, relations, and text using recurrent neural networks
Our goal is to combine the rich multistep inference of symbolic logical reasoning with the
generalization capabilities of neural networks. We are particularly interested in complex …
generalization capabilities of neural networks. We are particularly interested in complex …
Hierarchical relation extraction with coarse-to-fine grained attention
Distantly supervised relation extraction employs existing knowledge graphs to automatically
collect training data. While distant supervision is effective to scale relation extraction up to …
collect training data. While distant supervision is effective to scale relation extraction up to …
Modeling relation paths for knowledge graph completion
Knowledge graphs (KG) often encounter knowledge incompleteness. The path reasoning
that predicts the unknown path relation between pairwise entities based on existing facts is …
that predicts the unknown path relation between pairwise entities based on existing facts is …
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 …
learning field. Among existing models, graph neural networks (GNNs) is one of the most …