Link Prediction of Artificial Intelligence Concepts using Low Computational Power
원문 초록
This paper presents an approach proposed for the Science4cast 2021 competition, organized by the Institute of Advanced Research in Artificial Intelligence, whose main goal was to predict the likelihood of future associations between machine learning concepts in a semantic network. The developed methodology corresponds to a solution for a scenario of availability of low computational power only, exploiting the extraction of low order topological features and its incorporation in an optimized classifier to estimate the degree of future connections between the nodes. The reasons that motivated the developed methodologies will be discussed, as well as some results, limitations and suggestions of improvements.
함께 살펴볼 논문
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Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs
Demystifying Distributed Training of Graph Neural Networks for Link Prediction
Learnable Spatial-Temporal Positional Encoding for Link Prediction
A Survey of Link Prediction in N-ary Knowledge Graphs
Invariant Link Selector for Spatial-Temporal Out-of-Distribution Problem
GenIC: An LLM-Based Framework for Instance Completion in Knowledge Graphs
토론
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