Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Knowledge Graphs (KGs) are a widely used formalism for representing knowledge in the b of Data.  focus on the problem of link prediction, i.e. predicting missing links in large knowledge graphs, so to discover new facts about the world. Representation learning models that embed entities and relation types in continuous vector spaces recently re used to achieve new state-of-the-art link prediction results. A limiting factor in these models is that the process of learning the optimal embedding vectors can be really time-consuming, and might even require days of computations for large KGs. In this work,  propose a principled method for sensibly reducing the learning time, while converging to more accurate link prediction models. Furthermore,  employ the proposed method for training and evaluating a set of novel and scalable models. Our extensive evaluations show significant improvements over state-of-the-art link prediction methods on several datasets.
['Pasquale Minervini', 'Nicola Fanizzi', "Claudia d'Amato", 'Floriana Esposito']
I am writing a research paper on a idea Scalable Learning of Entity and Predicate Embeddings for Knowledge Graph Completion
[{"from": "human", "value": "I am writing a research paper on a idea Scalable Learning of Entity and Predicate Embeddings for Knowledge Graph Completion"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Knowledge Graphs (KGs) are a widely used formalism for representing knowledge in the b of Data.  focus on the problem of link prediction, i.e. predicting missing links in large knowledge graphs, so to discover new facts about the world. Representation learning models that embed entities and relation types in continuous vector spaces recently re used to achieve new state-of-the-art link prediction results. A limiting factor in these models is that the process of learning the optimal embedding vectors can be really time-consuming, and might even require days of computations for large KGs. In this work,  propose a principled method for sensibly reducing the learning time, while converging to more accurate link prediction models. Furthermore,  employ the proposed method for training and evaluating a set of novel and scalable models. Our extensive evaluations show significant improvements over state-of-the-art link prediction methods on several datasets."}]