Minervini, P; Fanizzi, N; D'Amato, C; Esposito, F; (2016) Scalable Learning of Entity and Predicate Embeddings for Knowledge Graph Completion. In: Proceedings of the IEEE - 14th International Conference on Machine Learning and Applications (ICMLA) 2015. (pp. pp. 162-167). IEEE: Danvers (MA), USA.

Preview

Text Minervini_Scalable learning of entity and predicate embeddings for knowledge graph completion_AAM.pdf - Accepted Version Download (618kB) | Preview

Abstract

Knowledge Graphs (KGs) are a widely used formalism for representing knowledge in the Web of Data. We 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 were 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, we propose a principled method for sensibly reducing the learning time, while converging to more accurate link prediction models. Furthermore, we 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.

Download activity - last month

Export as JSONCSVXML

Download activity - last 12 months

Export as JSONXMLCSV

Downloads by country - last 12 months

Export as JSONCSVXML

Archive Staff Only

View Item