Knowledge graph reasoning (KGR), aiming to deduce new facts from existing
facts based on mined logic rules underlying knowledge graphs (KGs), has become
a fast-growing research direction. It has been proven to significantly benefit
the usage of KGs in many AI applications, such as question answering,
recommendation systems, and etc. According to the graph types, existing KGR
models can be roughly divided into three categories, i.e., static models,
temporal models, and multi-modal models. Early works in this domain mainly
focus on static KGR, and recent works try to leverage the temporal and
multi-modal information, which are more practical and closer to real-world.
However, no survey papers and open-source repositories comprehensively
summarize and discuss models in this important direction. To fill the gap, we
conduct a first survey for knowledge graph reasoning tracing from static to
temporal and then to multi-modal KGs. Concretely, the models are reviewed based
on bi-level taxonomy, i.e., top-level (graph types) and base-level (techniques
and scenarios). Besides, the performances, as well as datasets, are summarized
and presented. Moreover, we point out the challenges and potential
opportunities to enlighten the readers. The corresponding open-source
repository is shared on GitHub
https://github.com/LIANGKE23/Awesome-Knowledge-Graph-Reasoning.Comment: This work has been submitted to the IEEE for possible publication.
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