Entity Spatio temporal Evolution Summarization in Knowledge Graphs
Knowledge graph has been growing in popularity with extensive applications in recent years, such as entity alignment, entity summarization, question answering, etc. However, the majority of research only focuses on one snapshot of the knowledge graph and neglects its dynamicity in nature, which often causes missing important information contained in other versions of the knowledge graph. Even worse, the incompleteness of the data in the knowledge graph is a challenge issue, which hinders the further utilization of the data. Considering that knowledge graph can evolve with time as well as the changing locations, it is necessary to summarize and integrate the entity temporal and spatial evolution information. To address this challenge, this paper pioneers to formulate the problem of entity spatio temporal evolution summarization, capturing the entity evolution with time and location changes and integrating the data from two groups of various knowledge graphs. Further, we propose a two stage approach: 1) generate entity temporal summarization and spatial summarization by utilizing the Triadic Formal Concept Analysis; 2) produce the spatio temporal evolution summarization of the entity by adopting a fusion strategy. The obtained summarization results can be used to the visualization of the entity spatio temporal evolution, data integration, and question answering.
2020 IEEE International Conference on Knowledge Graph (ICKG)