BootEA

Source code and datasets for IJCAI-2018 paper "Bootstrapping Entity Alignment with Knowledge Graph Embedding".

Dataset

We use two datasets, namely DBP15K and DWY100K. DBP15K can be found here while DWY100K is as follows.

Id files of DWY100K

Folder "dataset/DWY100K/" contains the id files of DWY100K.

The subfolder "mapping/0_3" contains the id files used in BootEA and MTransE while the subfolder "sharing/0_3" is for JAPE and IPTransE. The two datasets use 30% reference entity alignment as seeds. Id files in "sharing/0_3" are generated following the idea of parameter sharing that lets the two aligned enitites in seed alignment share the same id, while "mapping/0_3" does not.

The subfolder "mapping/0_3" inculdes the following files:

• ent_ids_1: entity ids in the source KG;
• ent_ids_2: entity ids in the target KG;
• ref_ent_ids: entity alignment for testing, list of pairs like (e_s \t e_t);
• sup_ent_ids: seed entity alignment (training data);
• rel_ids_1: relation ids in the source KG;
• rel_ids_2: relation ids in the target KG;
• triples_1: relation triples in the source KG;
• triples_2: relation triples in the target KG;

The subfolder "sharing/0_3" inculdes the following additional files:

• attr_ids_1: attribute ids in the source KG;
• attr_ids_2: attribute ids in the target KG;
• attr_range_type_1: attribute ranges in the source KG, list of pairs like (attribute id \t range code);
• attr_range_type_2: attribute ranges in the target KG;
• ent_attrs_1: entity attributes in the source KG;
• ent_attrs_2: entity attributes in the target KG;
• ref_ents: seed entity alignment denoted by URIs (training data);

Raw data of DWY100K

File "dataset/DWY100K_raw_data.zip" is the raw data of DWY100K, where each entity, relation or attribute is represented by a URI. Each dataset has the following files:

• ent_links: all the entity links without traning/test splits;
• triples_1: relation triples in the source KG, list of triples like (h \t r \t t);
• triples_2: relation triples in the target KG;
• attr_triples_1: attribute triples in the source KG;
• attr_triples_2: attribute triples in the target KG;
• uri_attr_range_type_1: attribute ranges in the source KG, list of pairs like (attribute uri \t range code);
• uri_attr_range_type_2: attribute ranges in the target KG;
• attrs_1: entity attributes in the source KG;
• attrs_2: entity attributes in the target KG;

Code

Folder "code" contains all codes of BootEA, in which:

• "AlignE.py" is the implementation of AlignE;
• "BootEA.py" is the implementation of BootEA;
• "param.py" is the config file.

Dependencies

• Python 3
• Tensorflow 1.x
• Scipy
• Numpy
• Graph-tool or igraph or NetworkX

If you fail to install Graph-tool, we suggest you to set "self.heuristic = False" in param.py, which allows BootEA to run using igraph rather than Graph-tool. If you have trouble installing igraph, you can use NetworkX by modifying the code of line 186-189 in train_bp.py and replacing "mwgm_graph_tool" and "mwgm_igraph" with "mwgm_networkx". Note that, igraph and NetworkX are much slower than Graph-tool!

If you have any difficulty or question in running code and reproducing experiment results, please email to zqsun.nju@gmail.com and whu@nju.edu.cn.

Citation

If you use this model or code, please cite it as follows:

@inproceedings{BootEA,
  author    = {Zequn Sun and Wei Hu and Qingheng Zhang and Yuzhong Qu},
  title     = {Bootstrapping Entity Alignment with Knowledge Graph Embedding},
  booktitle = {IJCAI},
  pages     = {4396--4402},
  year      = {2018}
}

Links

The following links point to some recent work that uses our datasets:

• Yixin Cao, et al. Multi-Channel Graph Neural Network for Entity Alignment. In: ACL 2019.
• Qiannan Zhu, et al. Neighborhood-Aware Attentional Representation for Multilingual Knowledge Graphs. In: IJCAI 2019.
• Qingheng Zhang, et al. Multi-view Knowledge Graph Embedding for Entity Alignment. In: IJCAI. 2019.
• Tingting Jiang, et al. Two-Stage Entity Alignment: Combining Hybrid Knowledge Graph Embedding with Similarity-Based Relation Alignment. In: PRICAI 2019.