A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal
Abstract
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.
Cite
@article{arxiv.2212.05767,
title = {A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal},
author = {Ke Liang and Lingyuan Meng and Meng Liu and Yue Liu and Wenxuan Tu and Siwei Wang and Sihang Zhou and Xinwang Liu and Fuchun Sun},
journal= {arXiv preprint arXiv:2212.05767},
year = {2024}
}
Comments
This work has been submitted to the IEEE for possible publication