Abstract
Knowledge Graph (KG) embedding has emerged as an active area of research resulting in the development of several KG embedding methods. Relational facts in KG often show temporal dynamics, e.g., the fact (Cristiano_Ronaldo, playsFor, Manchester_United) is valid only from 2003 to 2009. Most of the existing KG embedding methods ignore this temporal dimension while learning embeddings of the KG elements. In this paper, we propose HyTE, a temporally aware KG embedding method which explicitly incorporates time in the entity-relation space by associating each timestamp with a corresponding hyperplane. HyTE not only performs KG inference using temporal guidance, but also predicts temporal scopes for relational facts with missing time annotations. Through extensive experimentation on temporal datasets extracted from real-world KGs, we demonstrate the effectiveness of our model over both traditional as well as temporal KG embedding methods.
Highlights
Knowledge Graphs (KGs) are large multirelational graphs where nodes correspond to entities, and typed edges represent relationships among them
In order to overcome this challenge, in this paper, we propose Hyperplane-based Temporally aware KG Embedding (HyTE), a novel KG em
Taking inspiration from the objective of TransH, we propose a hyperplane based method for learning KG representation distributed in time
Summary
Knowledge Graphs (KGs) are large multirelational graphs where nodes correspond to entities, and typed edges represent relationships among them. KG embedding has emerged as a very active area of research over the last few years, resulting in the development of several techniques (Bordes et al, 2013; Nickel et al, 2016b; Yang et al, 2014; Lin et al, 2015; Trouillon et al, 2016; Dettmers et al, 2018; Guo et al, 2018) These methods learn high-dimensional vectorial representations for nodes and relations in the KG, while preserving various graph and knowledge constraints. In contrast to previous time-sensitive KG embedding methods, HyTE encodes temporal information directly in the learned embeddings This enables us to predict temporal scopes for previously unscoped KG beliefs. We have made HyTE’s source code and datasets used in the paper available at https://github.com/malllabiisc/HyTE
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