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Register a new userInductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions
تمثل كيانات الرسم البياني خارج المعرفة من خلال التقدير الأمثل بموجب الافتراضات الترجمية
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يفترض إكمال الرسم البياني المعرفي التقليدي (KGC) أن جميع كيانات الاختبار تظهر أثناء التدريب.ومع ذلك، في سيناريوهات العالم الحقيقي، تتطور الرسوم البيانية المعارف (KG) بسرعة مع كيانات بياني خارج المعرفة (OOKG) المضافة بشكل متكرر، ونحن بحاجة إلى تمثيل هذه الكيانات بكفاءة.لا يمكن أن تمثل أساليب شرط Graph INFORM INGEDDING (KGE) الموجودة في الرسم البياني الحالي من كيانات OOKG دون إعادة التدريب المكلفة على كلغ كله.لتعزيز الكفاءة، نقترح طريقة بسيطة وفعالة تمثلها كيانات OOKG من خلال تقديرها الأمثل بموجب الافتراضات الترجمة.علاوة على ذلك، نظرا لأن المدينات المحددة مسبقا للكيانات الموجودة في المعرفة (IKG)، فإن طريقتنا لا تحتاج إلى تعلم إضافي.تظهر النتائج التجريبية على مهام KGC مع كيانات OOKG أن أسلوبنا تتفوق على الأساليب السابقة بتهامش كبير مع كفاءة أعلى.
Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training. However, in real-world scenarios, Knowledge Graphs (KG) evolve fast with out-of-knowledge-graph (OOKG) entities added frequently, and we need to efficiently represent these entities. Most existing Knowledge Graph Embedding (KGE) methods cannot represent OOKG entities without costly retraining on the whole KG. To enhance efficiency, we propose a simple and effective method that inductively represents OOKG entities by their optimal estimation under translational assumptions. Moreover, given pretrained embeddings of the in-knowledge-graph (IKG) entities, our method even needs no additional learning. Experimental results on two KGC tasks with OOKG entities show that our method outperforms the previous methods by a large margin with higher efficiency.
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