Knowledge Graphs
937 papers with code • 3 benchmarks • 41 datasets
Libraries
Use these libraries to find Knowledge Graphs models and implementationsDatasets
Subtasks
Most implemented papers
Embedding Logical Queries on Knowledge Graphs
Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities.
TuckER: Tensor Factorization for Knowledge Graph Completion
Knowledge graphs are structured representations of real world facts.
MMKG: Multi-Modal Knowledge Graphs
We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs.
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
Here we propose Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to provide better recommendations.
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.
Multi-Relational Embedding for Knowledge Graph Representation and Analysis
The goal of this thesis is first to study multi-relational embedding on knowledge graphs to propose a new embedding model that explains and improves previous methods, then to study the applications of multi-relational embedding in representation and analysis of knowledge graphs.
Logic Embeddings for Complex Query Answering
Answering logical queries over incomplete knowledge bases is challenging because: 1) it calls for implicit link prediction, and 2) brute force answering of existential first-order logic queries is exponential in the number of existential variables.
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG.
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs
To this end, we propose NodePiece, an anchor-based approach to learn a fixed-size entity vocabulary.
MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction
Knowledge graph embedding aims to predict the missing relations between entities in knowledge graphs.