Computer security

Theoretical computer science

Astrophysics

Dimension (graph theory)

Data mining

Computer science

Embedding

Relation (database)

Variation (astronomy)

Hyperplane

Physics

Graph

Mathematics

Relational database

Inference

Timestamp

Artificial intelligence

Combinatorics

Knowledge graph

Machine learning

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.

HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding