| Abstrakt: |
Knowledge graph completion is a research hotspot in recent years, and it has broad application prospects in downstream applications, such as knowledge question answering, recommended system and intelligent search, etc. However, most of the completion methods ignore the dynamic characteristics of the knowledge graphs, many of which the facts will change over time. The new temporal knowledge graph completion methods take into account the limitations of the previous by incorporating time information, enabling the dynamic changes of the knowledge graph over time to be well captured. In response to the big potential of temporal knowledge graph completion methods in research fields such as social networks, transportation, finance and trade with complex time dependent characteristics, this paper summarizes temporal knowledge graph completion techniques. Based on the main different principle of model usage, completion methods based on logical rules, tensor decomposition, translation model, neural networks, deep reinforcement learning, and language model are summarized. The commonly used evaluation indicators, datasets, core ideas, advantages and disadvantages, applicable scenarios, and improvements on corresponding static models of existing methods are summarized. Finally, it looks forward to the future research directions. [ABSTRACT FROM AUTHOR] |
