# DKRL **Repository Path**: xugus/DKRL ## Basic Information - **Project Name**: DKRL - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2022-01-04 - **Last Updated**: 2022-01-04 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # DKRL New: Add Evaluation code for DKRL(CNN)+TransE, additional TransE results are needed to run this evaluation. "../transE_res/entity2vec."+version "../transE_res/relation2vec."+version with the same dimension and unif/bern. # INTRODUCTION Description-Embodied Knowledge Representation Learning (DKRL) Representation Learning of Knowledge Graphs with Entity Descriptions (AAAI'16) Ruobing Xie # COMPILE Just type make in the folder ./ # NOTE Pre-trained embeddings for entity/relation/word are optional. We update both Structure-based Representations and Description-based Representations in this version. We can also fix Structure-based Representations pre-trained by other models and only update Description-based Representations. For DKRL, we learn structure-based representations (SBR) and description-based representations (DBR) simultaneously in training. However, Test_cnn.cpp only use description-based representations for prediction. You can load in both entity representations for joint prediction. # DATA FB15k is published by the author of the paper "Translating Embeddings for Modeling Multi-relational Data (2013)." [download] You can also get FB15k from here: [download] Entity list and descriptions of FB15k used in this work [download] FB20k is based on FB15k and used for zero-shot scenario [download] Entity type information for entity classification [download] All these datasets are also in data.rar. Entity name file [download] # CITE If the codes or datasets help you, please cite the following paper: Ruobing Xie, Zhiyuan Liu, Jia Jia, Huanbo Luan, Maosong Sun. Representation Learning of Knowledge Graphs with Entity Descriptions. The 30th AAAI Conference on Artificial Intelligence (AAAI'16).