Comments (1)
kosugi11037
commented on September 7, 2024
Thanks for your interest in our work! On DWY100K, we use the basic bert unit to encode entity names (not entity descriptions) to obtain entity embeddings. The entity names can be found on https://github.com/nju-websoft/MultiKE
About entity descriptions, maybe you can find them on the original knowledge graphs. For example, the descriptions of English DBpedia can be downloaded from http://downloads.dbpedia.org/2016-10/core/long_abstracts_en.ttl.bz2. (but I'm not sure if every knowledge graph has entity descriptions)
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Related Issues (17)
- Basic_Bert_Unit_model 训练问题 HOT 2
- Description data是如何提取的 HOT 3
- 关于DWY100K数据集 HOT 5
- Why aggregation operation by RBF? HOT 1
- result = self.forward(*input, **kwargs) File "/data/my_project/nlp_text/nlp_models/bert-int-master/basic_bert_unit/Basic_Bert_Unit_model.py", line 20, in forward cls_vec = sequence_output[:,0] HOT 1
- Neighboring relation mask matrix的计算问题
- 运行basic_bert_unit时遇到的问题
- 运行interaction_model.py遇到问题，没有"../data/dbp15k/ja_en/attribute_similarity_feature.pkl"文件
- Basic_Bert_Unit_model train-test相似度以及epoch-loss HOT 3
- Attribute-view Interactions的问题 HOT 3
- Upload pre-trained BERT unit models HOT 1
- What are hardware requirements for training bert-int from scratch with/without cuda? HOT 2
- Results aren't matching the paper results HOT 1
- Why aren't names considered in the descriptions as part of Mbert input? HOT 2
- Could you share data conversion scripts / rules you used? HOT 3
- 提供的预训练模型无法正常使用。 HOT 3
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