Knowledge Graphs
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OpenKE: An Open Toolkit for Knowledge Embedding
We release an open toolkit for knowledge embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space.
INFORMATION RETRIEVAL KNOWLEDGE GRAPHS QUESTION ANSWERING REPRESENTATION LEARNING
Modeling Relational Data with Graph Convolutional Networks
• tkipf/gae
• 
We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.
INFORMATION RETRIEVAL KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS LINK PREDICTION
ERNIE: Enhanced Language Representation with Informative Entities
• thunlp/ERNIE
• 
Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks.
SOTA for Entity Typing on Open Entity
ENTITY TYPING KNOWLEDGE GRAPHS NATURAL LANGUAGE INFERENCE RELATION CLASSIFICATION SENTIMENT ANALYSIS
Convolutional 2D Knowledge Graph Embeddings
In this work, we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets.
SOTA for Link Prediction on WN18
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
We study the problem of learning to reason in large scale knowledge graphs (KGs).
KNOWLEDGE GRAPH EMBEDDING KNOWLEDGE GRAPH EMBEDDINGS KNOWLEDGE GRAPHS
Learning Deep Generative Models of Graphs
ICLR 2018
• snap-stanford/GraphRNN
•
Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry.
PaperRobot: Incremental Draft Generation of Scientific Ideas
We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper.
Knowledge Graph Completion via Complex Tensor Factorization
In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predicting missing relationships---labeled edges.
KNOWLEDGE GRAPH COMPLETION LINK PREDICTION RELATIONAL REASONING
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
HLT 2018
• cai-lw/KBGAN
•
This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks.
KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPH EMBEDDING KNOWLEDGE GRAPH EMBEDDINGS KNOWLEDGE GRAPHS LINK PREDICTION
Position-aware Attention and Supervised Data Improve Slot Filling
EMNLP 2017
• yuhaozhang/tacred-relation
•
The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance.
#4 best model for
Relation Extraction
on TACRED
KNOWLEDGE BASE POPULATION KNOWLEDGE GRAPHS RELATION EXTRACTION SLOT FILLING