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kglib - Grakn Knowledge Graph Library (ML R&D)

  •    Python

To respond to these scenarios, KGLIB is the centre of all research projects conducted at Grakn Labs. In particular, its focus is on the integration of machine learning with the Grakn Knowledge Graph. More on this below, in Knowledge Graph Tasks. At present this repo contains one project: Knowledge Graph Convolutional Networks (KGCNs). Go there for more info on getting started with a working example.

ConMask - ConMask model described in paper Open-world Knowledge Graph Completion.

  •    Python

Code for AAAI'18 paper: Open-world Knowledge Graph Completion. Warning: Current implementation needs a machine with four GPUs, this could be reduced to 1 GPU but needs code modification.

ProjE - Embedding Projection for Knowledge Graph Completion

  •    Python

Accepted at AAAI'2017. First, please download the data file from https://github.com/thunlp/KB2E and unzip the data.zip file. You will get two folders FB15k and WN18.

Fast-TransX - An Efficient implementation of TransE and its extended models for Knowledge Representation Learning

  •    C++

An extremely fast implementation of TransE [1], TransH [2], TransR [3], TransD [4], TranSparse [5] for knowledge representation learning (KRL) based on our previous pakcage KB2E ("https://github.com/thunlp/KB2E") for KRL. The overall framework is similar to KB2E, with some underlying design changes for acceleration. This implementation also supports multi-threaded training to save time. These codes will be gradually integrated into the new framework [OpenKE].

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