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الحافة: إثراء Anderments Graph Admings مع النص الخارجي
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تعاني رسوم الرسوم البيانية المعرفة من Sparsity والتي تتحلل من جودة التمثيلات الناتجة عن الطرق المختلفة. في حين أن هناك وفرة من المعلومات النصية في جميع أنحاء الويب والعديد من قواعد المعرفة الموجودة، فإن محاذاة المعلومات في جميع مصادر البيانات المتنوعة تظل تحديا في الأدبيات. وقد تناولت العمل السابق جزئيا هذه المشكلة عن طريق إثراء كيانات الرسم البياني المعرفي بناء على "حدوث كلمات" بجدية موجودة في كيانات الرسوم البيانية والنص الخارجي، بينما نحقق تكبير "" لينة "من خلال اقتراح إثراء الرسم البياني المعرفي وإطار التضمين اسمه الحافة. بالنظر إلى الرسم البياني المعرفي الأصلي، فإننا نقوم أولا بإنشاء رسم بياني معدني غني ولكن صاخبة يستخدم النصوص الخارجية في المستوى الدلالي والهيكل الهيكلية. لتقطير المعرفة ذات الصلة وقمع الضوضاء المقدمة، نقوم بتصميم مصطلح محاذاة رسم بياني في مساحة تضمين مشتركة بين الرسم البياني الأصلي والرسم البياني المعزز. لتعزيز التعلم التضمين في الرسم البياني المعزز، فإننا نتاجر مواصلة علاقة الموقع بالكيان المستهدف بناء على أخذ العينات السلبية. النتائج التجريبية على أربعة مجموعات بيانات قياسية تثبت متانة وفعالية الحافة في تبديد الارتباط وتصنيف العقدة.
Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. While there is an abundance of textual information throughout the web and many existing knowledge bases, aligning information across these diverse data sources remains a challenge in the literature. Previous work has partially addressed this issue by enriching knowledge graph entities based on hard'' co-occurrence of words present in the entities of the knowledge graphs and external text, while we achieve soft'' augmentation by proposing a knowledge graph enrichment and embedding framework named Edge. Given an original knowledge graph, we first generate a rich but noisy augmented graph using external texts in semantic and structural level. To distill the relevant knowledge and suppress the introduced noise, we design a graph alignment term in a shared embedding space between the original graph and augmented graph. To enhance the embedding learning on the augmented graph, we further regularize the locality relationship of target entity based on negative sampling. Experimental results on four benchmark datasets demonstrate the robustness and effectiveness of Edge in link prediction and node classification.
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