Incremental Construction of Time-Series Knowledge Graph

Abstract

Knowledge graph (KG) with time-series feature is referred to as time-series KG, which depicts the incre-mental concepts and corresponding relations in knowledge base. In view of knowledge being dramatically changing, by adding new knowledge to time-series KG, the evolution and update of knowledge can be reflected in time. Thus, this paper gives the definition of time-series KG and proposes the method for its incremental construction model based on TransH. In order to add new and relevant triple set to time-series KG, this paper proposes a model for calculating the coincidence between the triple and the current KG, and the technique for extracting the optimal triples by the idea of greedy algorithm. Then, the optimal set of triples is added to the time-series KG and the incremental update is fulfilled. Experimental results show that optimal triples can be extracted efficiently and added into the time-series KG by the proposed method. The effectiveness and efficiency of the method are verified.

Keywords

• |time-series knowledge graph|coincidence|incremental construction|greedy algorithm