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TEC: Knowledge Graph Embedding with Triple Context
2022-07-01 03:33:00 【Re:fused】
TEC:Knowledge Graph Embedding with Triple Context
1 problem
The knowledge map is not fully utilized , Rich information , Use its surrounding information and path information , Complete the knowledge map based on the above information .
2 Model
context The information includes :neighbor context and path context.
2.1 Context Details
- Neighbor Context
For its Neighbor Context Only calculate the output , C N ( h ) = ( r 4 , e 1 ) , ( r 3 , e 2 ) , ( r 2 , e 3 ) , ( r 1 , e 8 ) , ( r 1 , e 1 0 ) CN (h) = {(r_4, e_1), (r_3, e_2), (r_2, e_3), (r_1, e_8), (r_1, e_10)} CN(h)=(r4,e1),(r3,e2),(r2,e3),(r1,e8),(r1,e10) - Path Context
about Path Context, C P ( h , t ) = ( r 1 , r 2 ) , ( r 2 , r 1 , r 2 ) . CP (h,t) = {(r_1,r_2), (r_2,r_1,r_2)}. CP(h,t)=(r1,r2),(r2,r1,r2)., Save only h and t The relationship between .
C ( h , r , t ) = C N ( h ) ∪ C P ( h , t ) C(h,r,t) = CN (h) ∪CP (h,t) C(h,r,t)=CN(h)∪CP(h,t)
2.2 scoring function
Maximize possibilities :
It can be written in the following form :
Train through the above formula , Consumption of resources , Therefore, negative sampling is used for training
The other two are similar .
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