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  • title: The Ineffectiveness of Temporal Knowledge Graph Embedding Models in Encoding Real-World Knowledge Graphs
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            The Ineffectiveness of Temporal Knowledge Graph Embedding Models in Encoding Real-World Knowledge Graphs
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            The Ineffectiveness of Temporal Knowledge Graph Embedding Models in Encoding Real-World Knowledge Graphs

            Dec 2, 2022

            Speakers

            RO

            Ryan Ong

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            JS

            Jiahao Sun

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            OS

            Ovidiu Serban

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            About

            Temporal knowledge graphs (TKGs) have been rising in popularity in many industrial applications. However, for TKG-based applications to perform accurately, we need to have a reliable temporal knowledge graph embedding (TKGE) model to capture the semantic meanings of entities and the relationship between entities. This is possible when we have many standardised academic TKGs that are well-connected with popular entities. However, in real-world settings, these well-connected TKGs are hardly avail…

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            NeurIPS 2022

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