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2020
Conference Paper
Titel
Verbalizing the evolution of knowledge graphs with formal concept analysis
Abstract
Questioning 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.