The Ineffectiveness of TKGE Models in Encoding Real-World Knowledge Graphs

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 available. Instead, real-world TKGs are usually more sparse and filled with noisy and less popular entities, which makes it very challenging to use to train an accurate TKGE model. In this paper, we ran five different TKGE models on the TKGQA mergers and acquisitions (M&A) dataset to assess the effectiveness of TKGE models in encoding real-world TKGs. Specifically, we selected M&As because it's common for a well-known company to merge/acquire a less popular/unknown company and as such we can evaluate the effectiveness of TKGE models in encoding the less well-known companies. The results show that TKGE models are ineffective in encoding less popular/unknown entities in sparse KGs; given the lack of information on the entities, the TKGE models find distinguishing them in the embedding space challenging.