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Eventke: الرسم البياني المعرفة المحسنة للحدث
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تعكس العلاقات في معظم الرسوم البيانية المعارف التقليدية (KGS) فقط الاتصالات الثابتة والواقعية، ولكنها تفشل في تمثيل الأنشطة الديناميكية وتغير الدولة حول الكيانات. في هذه الورقة، نؤكد على أهمية دمج الأحداث في تعلم تمثيل KG، واقتراح نموذج Eventke Eventke Eventke المحسن للحدث. على وجه التحديد، نظرا لل KG الأصلية، فإننا ندمج أول عقود حدث من خلال بناء شبكة غير متجانسة، حيث يتم توزيع العقد الكيانية وعقد الحدث على جانبي الشبكة بين روابط الوسيطة في الحدث. ثم نستخدم علاقات كيان الكيان من الروابط الزمنية KG والأحداث الزمنية الأصلية إلى الكيان والكيان الداخلي والوقت على التوالي. نقوم بتصميم طريقة تمرير رسائل مفيدة وتستند إلى الرواية، والتي يتم إجراؤها على كيان كيان وكيان الحدث وحدث الأحداث لفيد معلومات الحدث في AGBeddings KG. تظهر النتائج التجريبية على مجموعات البيانات في العالم الحقيقي أن الأحداث يمكن أن تحسن إلى حد كبير جودة AGEDDINGS KG على مهام متعددة المصب.
Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities. In this paper, we emphasize the importance of incorporating events in KG representation learning, and propose an event-enhanced KG embedding model EventKE. Specifically, given the original KG, we first incorporate event nodes by building a heterogeneous network, where entity nodes and event nodes are distributed on the two sides of the network inter-connected by event argument links. We then use entity-entity relations from the original KG and event-event temporal links to inner-connect entity and event nodes respectively. We design a novel and effective attention-based message passing method, which is conducted on entity-entity, event-entity, and event-event relations to fuse the event information into KG embeddings. Experimental results on real-world datasets demonstrate that events can greatly improve the quality of the KG embeddings on multiple downstream tasks.
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