5,264 research outputs found
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
Graph representation learning (GRL) has emerged as a pivotal field that has
contributed significantly to breakthroughs in various fields, including
biomedicine. The objective of this survey is to review the latest advancements
in GRL methods and their applications in the biomedical field. We also
highlight key challenges currently faced by GRL and outline potential
directions for future research.Comment: Accepted by 2023 IMIA Yearbook of Medical Informatic
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal
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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