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Register a new userGMH: A General Multi-hop Reasoning Model for KG Completion
GMH: نموذج منطق متعدد القفز العام لإنجاز KG
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الرسوم البيانية المعرفة ضرورية للعديد من تطبيقات معالجة اللغة الطبيعية المصب، ولكنها غير مكتملة عادة مع العديد من الحقائق المفقودة. ينتج عن هذا الجهود البحثية في مهمة التفكير المتعدد القفزات، والتي يمكن صياغة كعملية بحث ونماذج حالية تؤدي عادة منطق بعيد المنال. ومع ذلك، فإن السبب البعيد الطويل أمر حيوي أيضا بالقدرة على توصيل الكيانات غير المرتبطة بسطحية. بأفضل من علمنا، يفتقر إلى وجود إطار عام يقترب من التفكير المتعدد القفز في سيناريوهات التفكير المسؤولة لفترة طويلة مختلطة. نقول أن هناك مشكلتان رئيسيتان لنموذج التفكير المتعدد القفز العام: ط) أين تذهب، والثاني) عند التوقف. لذلك، نقترح نموذج عام يحدد المشكلات ذات ثلاث وحدات: 1) وحدة المعرفة المحلية المحلية لتقدير المسارات المحتملة، 2) وحدة التسرب الإجراءات المختلفة لاستكشاف مجموعة متنوعة من المسارات، و 3) التوقف التكيفي وحدة البحث لتجنب البحث عنها. توضح النتائج الشاملة على ثلاث مجموعات بيانات تفوق نموذجنا مع تحسينات كبيرة ضد خطوط الأساس في سيناريوهات التفكير المسافة القصيرة والطويلة.
Knowledge graphs are essential for numerous downstream natural language processing applications, but are typically incomplete with many facts missing. This results in research efforts on multi-hop reasoning task, which can be formulated as a search process and current models typically perform short distance reasoning. However, the long-distance reasoning is also vital with the ability to connect the superficially unrelated entities. To the best of our knowledge, there lacks a general framework that approaches multi-hop reasoning in mixed long-short distance reasoning scenarios. We argue that there are two key issues for a general multi-hop reasoning model: i) where to go, and ii) when to stop. Therefore, we propose a general model which resolves the issues with three modules: 1) the local-global knowledge module to estimate the possible paths, 2) the differentiated action dropout module to explore a diverse set of paths, and 3) the adaptive stopping search module to avoid over searching. The comprehensive results on three datasets demonstrate the superiority of our model with significant improvements against baselines in both short and long distance reasoning scenarios.
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