Some papers on Temporal Knowledge Graph Embedding and Reasoning

Halal or Not is a novel Attributed Knowledge Graph Completion Model developed by NS Lab @ CUK based on pure PyTorch backend. This model aims at analyzing ingredients of everyday products to predict their cultural availability, e.g. predicting whether food or cosmetics meet halal or vegan criteria.

Some papers on knowledge graph embedding

我们的论文“基于知识图谱嵌入的阿尔茨海默病药物重定位研究”的代码。

🏘️ Hubness reduced nearest neighbor search for entity alignment with knowledge graph embeddings

A Python library for knowledge graph representation learning (graph embedding).

A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs, VLDB 2020

LiteralKG is a novel Attributed Knowledge Graph Embedding Model developed by NS Lab @ CUK based on pure PyTorch backend.

This repository provides code to replicate the experiments conducted in the research paper [Evaluation of Sampling Methods for Discovering Facts from Knowledge Graph Embeddings].

Along the Time: Timeline-traced Embedding for Temporal Knowledge Graph Completion

High-performance implementations of W2V, DistMult, CP, SimplE, ComplEx, RotatE, Quaternion, and MEI. Paper: Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion (ECAI 2020).

We extend the idea of reducing false negatives by adopting a Tucker decomposition representation to enhance the semantic soundness of latent relations among entities by introducing a relation feature space.

Experiments on Knowledge Graph Embeddings models for link prediction and deletion

Knowledge Graph Embedding with interactive live visualization.

Including W2V, DistMult, CP, SimplE, ComplEx, RotatE, Quaternion, MEI, MEIM. Paper: MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction (IJCAI 2022).

Analyzing knowledge graph embedding methods, including TransE, DistMult, CP, SimplE, ComplEx, Quaternion

Implementation of TransE model in PyTorch.

Reproduceing the models of Knowledge Representation Learning (KRL), such as TransE, TransH etc.

Code and data accompanying the paper: "Model-Agnostic Bias Measurement in Link Prediction" published in the EACL Findings 2023

Pipeline for Knowledge Graph Embedding and Embedding Classification