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New KMAS method enhances Knowledge Graph Foundation Models

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method called KMAS to improve the performance of Knowledge Graph Foundation Models (KGFMs). This approach enhances the training process by constructing "hard negative triples" more effectively than traditional random sampling. KMAS adaptively adjusts the proportion of these hard negatives throughout training to better align with the KGFM's evolving capabilities. Experiments across 44 datasets show that KMAS boosts state-of-the-art KGFMs without significant increases in time or memory. AI

IMPACT This research offers a more efficient way to train knowledge graph models, potentially improving performance in applications like question answering and recommendation systems.

RANK_REASON The cluster contains an academic paper detailing a new method for improving AI models.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New KMAS method enhances Knowledge Graph Foundation Models

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yinan Liu, Wenjin Xu, Zhiyuan Zha, Xiaochun Yang, Bin Wang ·

    Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling

    arXiv:2605.27023v1 Announce Type: new Abstract: Knowledge graphs (KGs) have become the core backbone of numerous downstream tasks such as question answering and recommender systems. However, despite all this, KGs are often very incomplete. To perform zero-shot knowledge graph com…

  2. arXiv cs.AI TIER_1 English(EN) · Bin Wang ·

    Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling

    Knowledge graphs (KGs) have become the core backbone of numerous downstream tasks such as question answering and recommender systems. However, despite all this, KGs are often very incomplete. To perform zero-shot knowledge graph completion in unseen KGs, which have different rela…

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