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
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
Holographic Embeddings of Knowledge Graphs
Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs.
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
ACL 2019
• 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 Relation Extraction on FewRel
ENTITY TYPING KNOWLEDGE GRAPHS NATURAL LANGUAGE INFERENCE SENTIMENT ANALYSIS
Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs
CVPR 2018
• JudyYe/zero-shot-gcn
•
Given a learned knowledge graph (KG), our approach takes as input semantic embeddings for each node (representing visual category).
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
PaperRobot: Incremental Draft Generation of Scientific Ideas
ACL 2019
• EagleW/PaperRobot
•
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.
Explainable Knowledge Graph-based Recommendation via Deep Reinforcement Learning
• DeepGraphLearning/RecommenderSystems
Recently, a variety of methods have been developed for this problem, which generally try to learn effective representations of users and items and then match items to users according to their representations.
KNOWLEDGE GRAPHS POLICY GRADIENT METHODS RECOMMENDATION SYSTEMS