Connect, Understand and Learn: Dynamic Knowledge Graph Transforms Learning

The automation of knowledge graphs is a challenge if only small training data sets are available for the corresponding learning methods. The approach presented in this paper can work with small training data sets and enables the solution of tasks with previously hidden syntactic structures. In this research, a new conceptual algorithm for learning and updating knowledge graphs is proposed. We combined a powerful NLP approach with statistical methods to build a word frequency-based corpus for various question answering problems. Then, we used specific similarity measures to find the best answer for the given problem. For this purpose, a vector model is used and weights are calculated for the association between terms and problems. In the last phase, we created a continuous learning model with a dynamic knowledge graph that can be updated with new tasks and predict answers to upcoming problems. The knowledge graph is updated with new information when the pattern of a problem is unknown and therefore not found. The implementation of the algorithm is validated using various openly available data sets from the field of user support in business and medicine. The proposed method supports an incremental learning approach and real-time implementation.