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Register a new userZero-Shot Information Extraction to Enhance a Knowledge Graph Describing Silk Textiles
استخراج المعلومات Zero-Shot لتعزيز الرسم البياني المعرفة يصف المنسوجات الحريرية
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تعد معرفة إنتاج الأقمشة الحريرية الأوروبية هي حالة نموذجية تتمثل المعلومات التي تم جمعها غير متجانسة، وانتشرت في العديد من المتاحف والمنتجات منذ نادرا ما تكتمل. الرسوم البيانية المعرفة لهذا مجال التراث الثقافي، عند تطويرها مع الأوانيات المناسبة والمفردات، تمكن من دمج هذه المعلومات المتنوعة والتوفيقية. ومع ذلك، فإن العديد من سجلات المتحف الأصلية هذه لا تزال لديها بعض فجوات البيانات الوصفية. في هذه الورقة، نقدم نهجا للتعلم بالرصاص الذي يرفع الرسم البياني لمعرفة المعرفة بالشخصية المفهوم للتنبؤ بالبيانات الوصفية الفئوية التي تخبر عن إنتاج كائنات الحرير. قارننا أداء نهجنا مع الطرق التقليدية الإشراف على التعلم العميق القائم على البيانات التي تتطلب بيانات التدريب. نوضح الأداء الواعد والتنافسي لمجموعات البيانات والظروف مماثلة والقدرة على التنبؤ بأحيانا معلومات أكثر غرامة. يمكن إعادة إنتاج نتائجنا باستخدام التعليمات البرمجية والجمدات البيانات المنشورة في https://github.com/silknow/zsl-kg-silk.
The knowledge of the European silk textile production is a typical case for which the information collected is heterogeneous, spread across many museums and sparse since rarely complete. Knowledge Graphs for this cultural heritage domain, when being developed with appropriate ontologies and vocabularies, enable to integrate and reconcile this diverse information. However, many of these original museum records still have some metadata gaps. In this paper, we present a zero-shot learning approach that leverages the ConceptNet common sense knowledge graph to predict categorical metadata informing about the silk objects production. We compared the performance of our approach with traditional supervised deep learning-based methods that do require training data. We demonstrate promising and competitive performance for similar datasets and circumstances and the ability to predict sometimes more fine-grained information. Our results can be reproduced using the code and datasets published at https://github.com/silknow/ZSL-KG-silk.
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