KGE Algorithms
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:' An updated method for link prediction that uses a regularization factor that models relation argument typesAbstract (Kotnis and Nastase, 2017): Learning relations based on evidence from knowledge repositoriesrelies on processing the available relation instances. Knowledgerepositories are not balanced in terms of relations or entities – thereare relations with less than 10 but also thousands of instances, andentities involved in less than 10 but also thousands of relations.Many relations, however, have clear domain and range, which wehypothesize could help learn a better, more generalizing, model.We include such information in the RESCAL model in the formof a regularization factor added to the loss function that takes intoaccount the types (categories) of the entities that appear as a' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["graph embedding","knowledge graphs","link prediction"]} <|eot_id|>
 Here is the Description of a dataset. Description:' An updated method for link prediction that uses a regularization factor that models relation argument typesAbstract (Kotnis and Nastase, 2017): Learning relations based on evidence from knowledge repositoriesrelies on processing the available relation instances. Knowledgerepositories are not balanced in terms of relations or entities – thereare relations with less than 10 but also thousands of instances, andentities involved in less than 10 but also thousands of relations.Many relations, however, have clear domain and range, which wehypothesize could help learn a better, more generalizing, model.We include such information in the RESCAL model in the formof a regularization factor added to the loss function that takes intoaccount the types (categories) of the entities that appear as a' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
 An updated method for link prediction that uses a regularization factor that models relation argument typesAbstract (Kotnis and Nastase, 2017): Learning relations based on evidence from knowledge repositoriesrelies on processing the available relation instances. Knowledgerepositories are not balanced in terms of relations or entities – thereare relations with less than 10 but also thousands of instances, andentities involved in less than 10 but also thousands of relations.Many relations, however, have clear domain and range, which wehypothesize could help learn a better, more generalizing, model.We include such information in the RESCAL model in the formof a regularization factor added to the loss function that takes intoaccount the types (categories) of the entities that appear as a
graph embedding; knowledge graphs; link prediction