Integrating Meta-Features with Knowledge Graph Embeddings for Meta-Learning

• URL: http://arxiv.org/abs/2603.19888v1
• Date: Fri, 20 Mar 2026 11:57:25 GMT
• Title: Integrating Meta-Features with Knowledge Graph Embeddings for Meta-Learning
• Abstract summary: Two crucial meta-learning tasks are pipeline performance estimation (PPE) and dataset performance-based similarity estimation (DPSE)<n>Existing approaches primarily rely on dataset meta-features to represent datasets numerically and approximate these meta-learning tasks.<n>We propose KGmetaSP, a knowledge-graph-embeddings approach that leverages existing experiment data to capture these interactions and improve both PPE and DPSE.
• Score: 8.071711833525475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The vast collection of machine learning records available on the web presents a significant opportunity for meta-learning, where past experiments are leveraged to improve performance. Two crucial meta-learning tasks are pipeline performance estimation (PPE), which predicts pipeline performance on target datasets, and dataset performance-based similarity estimation (DPSE), which identifies datasets with similar performance patterns. Existing approaches primarily rely on dataset meta-features (e.g., number of instances, class entropy, etc.) to represent datasets numerically and approximate these meta-learning tasks. However, these approaches often overlook the wealth of past experimental results and pipeline metadata available. This limits their ability to capture dataset - pipeline interactions that reveal performance similarity patterns. In this work, we propose KGmetaSP, a knowledge-graph-embeddings approach that leverages existing experiment data to capture these interactions and improve both PPE and DPSE. We represent datasets and pipelines within a unified knowledge graph (KG) and derive embeddings that support pipeline-agnostic meta-models for PPE and distance-based retrieval for DPSE. To validate our approach, we construct a large-scale benchmark comprising 144,177 OpenML experiments, enabling a rich cross-dataset evaluation. KGmetaSP enables accurate PPE using a single pipeline-agnostic meta-model and improves DPSE over baselines. The proposed KGmetaSP, KG, and benchmark are released, establishing a new reference point for meta-learning and demonstrating how consolidating open experiment data into a unified KG advances the field.

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