8 papers
When Metrics Disagree: A Meta-Analysis of Knowledge-Graph-Completion Model Benchmarking
Haji Gul, Ajaz Ahmad Bhat
Evaluating Knowledge Graph Completion (KGC) models remains challenging because standard assessment relies on isolated rank-based metrics such as MRR, Hitsk, and Mean Rank, which…
Evaluating Cumulative Spectral Gradient as a Complexity Measure
Haji Gul, Abdul Ghani Naim, Ajaz Ahmad Bhat
Accurate estimation of dataset complexity is crucial for evaluating and comparing link prediction models for knowledge graphs (KGs). The Cumulative Spectral Gradient (CSG) metric d…
KG-EDAS: A Meta-Metric Framework for Evaluating Knowledge Graph Completion Models
Haji Gul, Abul Ghani Naim, Ajaz Ahmad Bhat
Knowledge Graphs (KGs) enable applications in various domains such as semantic search, recommendation systems, and natural language processing. KGs are often incomplete, missing en…
Evaluating Knowledge Graph Complexity via Semantic, Spectral, and Structural Metrics for Link Prediction
Haji Gul, Abul Ghani Naim, Ajaz Ahmad Bhat
Understanding dataset complexity is fundamental to evaluating and comparing link prediction models on knowledge graphs (KGs). While the Cumulative Spectral Gradient (CSG) metric, d…
MuCoS: Efficient Drug Target Discovery via Multi Context Aware Sampling in Knowledge Graphs
Haji Gul, Abdul Ghani Naim, Ajaz Ahmad Bhat
Accurate prediction of drug target interactions is critical for accelerating drug discovery and elucidating complex biological mechanisms. In this work, we frame drug target predic…
MuCo-KGC: Multi-Context-Aware Knowledge Graph Completion
Haji Gul, Ajaz Ahmad Bhat, Abdul Ghani Haji Naim
Knowledge graph completion (KGC) seeks to predict missing entities (e.g., heads or tails) or relationships in knowledge graphs (KGs), which often contain incomplete data. Tradition…