Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The models developed to date for knowledge base embedding are all based on the assumption that the relations contained in knowledge bases are binary. For the training and testing of these embedding models, multi-fold (or n-ary) relational data are converted to triples (e.g., in FB15K dataset) and interpreted as instances of binary relations.  presents a canonical representation of knowledge bases containing multi-fold relations.  show that the existing embedding models on the popular FB15K datasets correspond to a sub-optimal modelling framework, resulting in a loss of structural information.  advocate a novel modelling framework, which models multi-fold relations directly using this canonical representation. Using this framework, the existing TransH model is generalized to a new model, m-TransH.  demonstrate experimentally that m-TransH outperforms TransH by a large margin, thereby establishing a new state of the art.
['Jianfeng Wen', 'Jianxin Li', 'Yongyi Mao', 'Shini Chen', 'Richong Zhang']
I am writing a research paper on a idea On the representation and embedding of knowledge bases beyond binary relations
[{"from": "human", "value": "I am writing a research paper on a idea On the representation and embedding of knowledge bases beyond binary relations"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The models developed to date for knowledge base embedding are all based on the assumption that the relations contained in knowledge bases are binary. For the training and testing of these embedding models, multi-fold (or n-ary) relational data are converted to triples (e.g., in FB15K dataset) and interpreted as instances of binary relations.  presents a canonical representation of knowledge bases containing multi-fold relations.  show that the existing embedding models on the popular FB15K datasets correspond to a sub-optimal modelling framework, resulting in a loss of structural information.  advocate a novel modelling framework, which models multi-fold relations directly using this canonical representation. Using this framework, the existing TransH model is generalized to a new model, m-TransH.  demonstrate experimentally that m-TransH outperforms TransH by a large margin, thereby establishing a new state of the art."}]