TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation
- URL: http://arxiv.org/abs/2010.01029v2
- Date: Sat, 24 Oct 2020 22:42:26 GMT
- Title: TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation
- Authors: Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi,
Jens Lehmann
- Abstract summary: We present a new approach of TKG embedding, TeRo, which defines the temporal evolution of entity embedding.
We show our proposed model overcomes the limitations of the existing KG embedding models and TKG embedding models.
Experimental results on four different TKGs show that TeRo significantly outperforms existing state-of-the-art models for link prediction.
- Score: 12.138550487430807
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the last few years, there has been a surge of interest in learning
representations of entitiesand relations in knowledge graph (KG). However, the
recent availability of temporal knowledgegraphs (TKGs) that contain time
information for each fact created the need for reasoning overtime in such TKGs.
In this regard, we present a new approach of TKG embedding, TeRo, which defines
the temporal evolution of entity embedding as a rotation from the initial time
to the currenttime in the complex vector space. Specially, for facts involving
time intervals, each relation isrepresented as a pair of dual complex
embeddings to handle the beginning and the end of therelation, respectively. We
show our proposed model overcomes the limitations of the existing KG embedding
models and TKG embedding models and has the ability of learning and
inferringvarious relation patterns over time. Experimental results on four
different TKGs show that TeRo significantly outperforms existing
state-of-the-art models for link prediction. In addition, we analyze the effect
of time granularity on link prediction over TKGs, which as far as we know
hasnot been investigated in previous literature.
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