arXiv:2305.02966
공식 코드 · Papers with Code
ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics
원문 초록
Many machine learning (ML) libraries are accessible online for ML practitioners. Typical ML pipelines are complex and consist of a series of steps, each of them invoking several ML libraries. In this demo paper, we present ExeKGLib, a Python library that allows users with coding skills and minimal ML knowledge to build ML pipelines. ExeKGLib relies on knowledge graphs to improve the transparency and reusability of the built ML workflows, and to ensure that they are executable. We demonstrate the usage of ExeKGLib and compare it with conventional ML code to show its benefits.
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Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs
Leveraging Predictive Equivalence in Decision Trees
A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs
Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering
midr: Learning from Black-Box Models by Maximum Interpretation Decomposition
Bridging RDF Knowledge Graphs with Graph Neural Networks for Semantically-Rich Recommender Systems
토론
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