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Summarizing entity temporal evolution in knowledge graphs

Abstract:

Knowledge graphs are dynamic in nature, new facts about an entity are added or removed over time. Therefore, multiple versions of the same knowledge graph exist, each of which represents a snapshot of the knowledge graph at some point in time. Entities within the knowledge graph undergo evolution as new facts are added or removed. The problem of automatically generating a summary out of different versions of a knowledge graph is a long-studied problem. However, most of the existing approaches are limited to a pairwise version comparison. This limitation makes it difficult to capture a complete evolution out of several versions of the same knowledge graph. To overcome this limitation, we envision an approach to create a summary graph capturing temporal evolution of entities across different versions of a knowledge graph. The entity summary graphs may then be used for documentation generation, profiling or visualization purposes. First, we take different temporal versions of a knowledge graph and convert them into RDF molecules. Secondly, we perform Formal Concept Analysis on these molecules to generate summary information. Finally, we apply a summary fusion policy in order to generate a compact summary graph which captures the evolution of entities.