Why the left and right results differ so much? about conve HOT 1 CLOSED

This has mostly to do with the data and how it is constructed. Since in many languages are read from left-to-right, including English, the dataset language, most relationships in knowledge graphs are read in a left-to-right manner. For example, you would see and expect (Tom Hanks, won, Academy Award for Best Actor) rather than (Academy Award for Best Actor, won, Tom Hanks). As you can see in this example, left-to-right and subject-verb-object relationships are both the main reasons why triples in English datasets often have a specific natural order.

The second part of the issue, if you reverse it, you still have a valid problem, but it is often much more difficult. It is common, especially in the datasets used in this case, for heads to be subjects or people, and for tails to be objects or things. Since people are unique and things are often not for (h, r, x) you usually predict fewer things and these fewer things are dissimilar to each other, while predicting (x, r, t) is more difficult because a thing is often shared by many people and these people have quite similar other attributes.

For example, if you need to predict (x, won, Academy Award for Best Actor) there are so many possibilities of actors all looking quite similar although only a few of them won the award. (Tom Hanks, won, x) is a much easier problem since their are fewer awards and it is likely that Tom Hanks won the award if he already won many other awards.

from conve.