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تنبؤ الكيان في الرسوم البيانية المعرفة مع Adgeddings مشترك
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أصبحت الرسوم البيانية المعرفة (KGS) شعبية بشكل متزايد في السنوات الأخيرة. ومع ذلك، نظرا لأن المعرفة تنمو باستمرار وتغييرات، فمن المحتم أن تمتد KGS الموجودة مع الكيانات التي ظهرت أو أنها ذات صلة بنطاق كجم بعد إنشائها. تعتمد البحث في تحديث KGS عادة على استخراج الكيانات المسماة والعلاقات من النص. ومع ذلك، لا يمكن لهذه الأساليب استنتاج كيانات أو علاقات غير مذكورة صراحة. بدلا من ذلك، استغلال نماذج التضمين الانتظار الهيكلية الضمنية للتنبؤ بالعلاقات المفقودة، ولكن لا يمكن التنبؤ بالكيانات المفقودة. في هذه المقالة، نقدم طريقة جديدة لإثراء KG مع كيانات جديدة بالنظر إلى وصفها النصي. لدينا طريقة ترفع نماذج تضمين مشتركة، وبالتالي لا تتطلب كيانات أو علاقات يمكن تسميتها صراحة. نظرا لأن نهجنا يمكن أن تحدد مفاهيم جديدة في كوربوس وثيقة ونقلها إلى كجم، ونجد أن أداء طريقتنا يحسن بشكل كبير عند تمديده مع تقنيات من تعدين حكم الرابطة، والتعدين النصي، والتعلم النشط.
Knowledge Graphs (KGs) have become increasingly popular in the recent years. However, as knowledge constantly grows and changes, it is inevitable to extend existing KGs with entities that emerged or became relevant to the scope of the KG after its creation. Research on updating KGs typically relies on extracting named entities and relations from text. However, these approaches cannot infer entities or relations that were not explicitly stated. Alternatively, embedding models exploit implicit structural regularities to predict missing relations, but cannot predict missing entities. In this article, we introduce a novel method to enrich a KG with new entities given their textual description. Our method leverages joint embedding models, hence does not require entities or relations to be named explicitly. We show that our approach can identify new concepts in a document corpus and transfer them into the KG, and we find that the performance of our method improves substantially when extended with techniques from association rule mining, text mining, and active learning.
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