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Register a new userHighly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis
الرسوم البيانية المعرفة عالية الكفاءة للغاية تضمين التعلم مع تحليل متعامدة
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تم استكشاف Adment Graph Graph (KIGS) بشكل مكثف في السنوات الأخيرة بسبب وعدهم بمجموعة واسعة من التطبيقات. ومع ذلك، تركز الدراسات الحالية على تحسين أداء النموذج النهائي دون الاعتراف بالتكلفة الحسابية للنهج المقترحة، من حيث التنفيذ والتأثير البيئي. تقترح هذه الورقة إطار KGE بسيط ولكنه فعال يمكن أن يقلل من وقت التدريب وبصمة الكربون عن طريق أوامر من الأقواس مقارنة مع النهج الحديثة، مع إنتاج أداء تنافسي. نسلط الضوء على ثلاثة ابتكارات تقنية: التعلم الدفاعي الكامل عبر المصفوفات العلائقية، وتحليل العوامة المتعامدة المغلقة للملابس، والتدريب غير السلبي للأخذ العينات. بالإضافة إلى ذلك، كأول طريقة KGE الأولى التي تخزنها تضمين كيانها أيضا معلومات العلاقة الكاملة، ترمز النماذج المدربة لدينا إلى دلالات غنية ويمكن تفسيرها للغاية. تجارب شاملة ودراسات الاجتثاثات التي تنطوي على 13 خطوط بيانات قوية ومجموعات بيانات قياسية تحقق من فعالية وكفاءة خوارزميةنا.
Knowledge Graph Embeddings (KGEs) have been intensively explored in recent years due to their promise for a wide range of applications. However, existing studies focus on improving the final model performance without acknowledging the computational cost of the proposed approaches, in terms of execution time and environmental impact. This paper proposes a simple yet effective KGE framework which can reduce the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches, while producing competitive performance. We highlight three technical innovations: full batch learning via relational matrices, closed-form Orthogonal Procrustes Analysis for KGEs, and non-negative-sampling training. In addition, as the first KGE method whose entity embeddings also store full relation information, our trained models encode rich semantics and are highly interpretable. Comprehensive experiments and ablation studies involving 13 strong baselines and two standard datasets verify the effectiveness and efficiency of our algorithm.
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