Inductive future time prediction on temporal knowledge graphs with interval time

Temporal Knowledge Graphs (TKGs) are an extension of Knowledge Graphs where facts are temporally scoped. They have recently received increasing attention in knowledge management, mirroring an increased interest in temporal graph learning within the graph learning community. While there have been man...

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Bibliographic Details
Main Authors: Pop, Roxana, Kostylev, Egor V
Format: Conference Proceeding
Language:eng ; nor
Online Access:Request full text
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Summary:Temporal Knowledge Graphs (TKGs) are an extension of Knowledge Graphs where facts are temporally scoped. They have recently received increasing attention in knowledge management, mirroring an increased interest in temporal graph learning within the graph learning community. While there have been many systems proposed for TKG learning, there are many settings to be considered, and not all of them are yet fully explored. In this position paper we identify a problem not yet approached, inductive future time prediction on interval-based TKGs, and formalise it as a machine learning task. We then outline several promising approaches for solving it, focusing on a neurosymbolic framework connecting TKG learning with the temporal reasoning formalism DatalogMTL.
ISSN:1613-0073
1613-0073