Representation Learning of Knowledge Graphs with Entity Descriptions (AAAI'16)
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New: Add entity type information


Description-Embodied Knowledge Representation Learning (DKRL)

Representation Learning of Knowledge Graphs with Entity Descriptions (AAAI'16)

Ruobing Xie


Just type make in the folder ./


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.


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]


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).