Probabilistic Knowledge Graph Construction: Compositional and Incremental Approaches
This work addresses the gap between knowledge population and completion in knowledge graph construction, offering an incremental approach to improve efficiency and accuracy for applications relying on structured knowledge bases.
The paper tackles the problem of insufficient external resources hindering statistical inference in knowledge graph construction by proposing a probabilistic factorization method that uses path structures and enables a common modeling approach for both incremental population and knowledge completion. Experiments on three benchmark datasets show that balanced exploitation-exploration aids incremental population and path structures improve missing information prediction.
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.