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Register a new userJointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network
استخراجها بشكل مشترك ثلاثي ثلاث مرات صحيحة وتضخية مع نمط التفكير شبكة مؤشر ثنائية محسنة
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الاستخراج الثلاثي العلائقية هي مهمة حاسمة لبناء الرسم البياني المعارف. تركز الأساليب الحالية أساسا على ثلاثة أضعاف ثلاثية صحيحة يتم التعبير عنها بشكل مباشر، ولكن عادة ما تعاني من تجاهل ثلاث مرات ضمنية تفتقر إلى التعبيرات الصريحة. هذا سيؤدي إلى عدم اكتمال خطير الرسوم البيانية المعرفة المبنية. لحسن الحظ، توفر ثلاث مرات أخرى في الجملة معلومات تكميلية لاكتشاف أزواج الكيانات التي قد تكون لها علاقات ضمنية. أيضا، يمكن تحديد أنواع العلاقات بين أزواج الكيان المتصلة الضمنيا مع أنماط التفكير العلائقية في العالم الحقيقي. في هذه الورقة، نقترح إطارا موحدا لاستخراج ثلاثة أضعاف ثلاثياتي صريحة وضرورية. لاستكشاف أزواج الكيانات التي قد تكون مرتبطة ضمنيا بالعلاقات، نقترح شبكة مؤشر ثنائية لاستخراج ثلاث مرات متداخلة ثلاثية ذات صلة بكل كلمة بالتتابع والاحتفاظ بمعلومات ثلاث مرات المستخرجة سابقا في ذاكرة خارجية. لاستنتاج أنواع العلاقات ثلاث مرات التوالي الضمنية، نقترح تقديم أنماط التفكير العلائقية العالمية الحقيقية في طرازنا والتقاط هذه الأنماط مع شبكة العلاقة. نقوم بإجراء تجارب على عدة مجموعات من مجموعات البيانات القياسية، وتثبت النتائج صحة طريقتنا.
Relational triple extraction is a crucial task for knowledge graph construction. Existing methods mainly focused on explicit relational triples that are directly expressed, but usually suffer from ignoring implicit triples that lack explicit expressions. This will lead to serious incompleteness of the constructed knowledge graphs. Fortunately, other triples in the sentence provide supplementary information for discovering entity pairs that may have implicit relations. Also, the relation types between the implicitly connected entity pairs can be identified with relational reasoning patterns in the real world. In this paper, we propose a unified framework to jointly extract explicit and implicit relational triples. To explore entity pairs that may be implicitly connected by relations, we propose a binary pointer network to extract overlapping relational triples relevant to each word sequentially and retain the information of previously extracted triples in an external memory. To infer the relation types of implicit relational triples, we propose to introduce real-world relational reasoning patterns in our model and capture these patterns with a relation network. We conduct experiments on several benchmark datasets, and the results prove the validity of our method.
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