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Register a new userStructured Self-Supervised Pretraining for Commonsense Knowledge Graph Completion
احتجاج منظم تحت الإشراف الذاتي لمعرفة الرسم البياني المعرفة المنطقية
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الملخص لتطوير تطبيقات NLP المتطرفة المنطقية، وهو رسم بياني معرفي شامل ودقيق للعموم (CKG).إنها تستغرق وقتا طويلا لإنشاء CKGS يدويا والعديد من جهود البحثية التي تم تخصيصها للبناء التلقائي CKGS.تركز النهج السابقة على توليد المفاهيم التي لديها علاقات مباشرة واضحة مع المفاهيم القائمة وتفتقر إلى القدرة على توليد مفاهيم غير واضحة.في هذا العمل، نهدف إلى سد هذه الفجوة.نقترح الإطار العام لإحاطاء بيئة الرسم البياني إلى مسارات يرفع هياكل مرتفعة في CKGS لالتقاط علاقات عالية الجودة بين المفاهيم.نحن إنشاء هذا الإطار العام إلى أربع حالات خاصة: المسار الطويل، المسار إلى المسار، جهاز التوجيه، ورأس الرسم البياني - مسار العقدة.تجارب على مجموعة بيانات اثنين تثبت فعالية أساليبنا.سيتم إصدار الرمز عبر مستودع GitHub العام.
Abstract To develop commonsense-grounded NLP applications, a comprehensive and accurate commonsense knowledge graph (CKG) is needed. It is time-consuming to manually construct CKGs and many research efforts have been devoted to the automatic construction of CKGs. Previous approaches focus on generating concepts that have direct and obvious relationships with existing concepts and lack an capability to generate unobvious concepts. In this work, we aim to bridge this gap. We propose a general graph-to-paths pretraining framework that leverages high-order structures in CKGs to capture high-order relationships between concepts. We instantiate this general framework to four special cases: long path, path-to-path, router, and graph-node-path. Experiments on two datasets demonstrate the effectiveness of our methods. The code will be released via the public GitHub repository.
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