Riveros, M.A.M.A.RiverosTasnim, MayeshaMayeshaTasnimGraux, DamienDamienGrauxOrlandi, FabrizioFabrizioOrlandiCollarana, DiegoDiegoCollarana2022-03-152022-03-152020https://publica.fraunhofer.de/handle/publica/412382Questioning Answering and Verbalization over Knowledge Graphs (KGs) are gaining momentum as they provide natural interfaces to knowledge harvested from a myriad of data sources. KGs are dynamic: new facts are added and removed over time, producing multiple versions, each representing a knowledge snapshot of a point in time. Verbalizing a report of the evolution of entities is useful in many scenarios, e.g., reporting digital twins' evolution in manufacturing or healthcare. We envision a method to verbalize a graph summary capturing the temporal evolution of entities across different KG versions. Technically, our approach considers revisions of a graph over time and converts them into RDF molecules. Formal Concept Analysis is then performed on these RDF molecules to synthesize summary information. Finally, a verbalization pipeline generates a report in natural language.en005006629conference paper