  Knowledge graph construction consists of two tasks: extracting information
from external resources (knowledge population) and inferring missing
information through a statistical analysis on the extracted information
(knowledge completion). In many cases, insufficient external resources in the
knowledge population hinder the subsequent statistical inference. The gap
between these two processes can be reduced by an incremental population
approach. We propose a new probabilistic knowledge graph factorisation method
that benefits from the path structure of existing knowledge (e.g. syllogism)
and enables a common modelling approach to be used for both incremental
population and knowledge completion tasks. More specifically, the probabilistic
formulation allows us to develop an incremental population algorithm that
trades off exploitation-exploration. Experiments on three benchmark datasets
show that the balanced exploitation-exploration helps the incremental
population, and the additional path structure helps to predict missing
information in knowledge completion.
