# Why margin-based ranking loss is reversed in these two papers?

For knwoledge graph completion, it is very common to use margin-based ranking loss

In the paper:margin-based ranking loss is defined as

$$\min \sum_{(h,l,t)\in S} \sum_{(h',l,t')\in S'}[\gamma + d(h,l,t) - d(h',l,t')]_+$$

Here $$d(\cdot)$$ is the predictive model, $$(h,l,t)$$ means a positive training instance, and $$(h',l,t')$$ means a negative training instance corresponding to $$(h,l,t)$$.

However, in the Andrew's paper, it defines

$$\min \sum_{(h,l,t)\in S} \sum_{(h',l,t')\in S'}[\gamma + d(h',l,t') - d(h,l,t)]_+$$

It seems that they switch the terms $$d(h',l,t')$$ and $$d(h,l,t)$$.

My question is that

does it matter to switch $$d(h',l,t')$$ and $$d(h,l,t)$$? it's real strange definition. Thanks

In this paper, $$d$$ denotes "dissimilarity" which should be minimized for positive samples.
In this paper, $$d$$ ($$g$$ in the paper) denotes "similarity" which should be maximized for positive samples (or equivalently $$-g\left(T^{(i)}\right)$$ should be minimized)