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Verbalizing the evolution of knowledge graphs with formal concept analysis

: Riveros, M.A.; Tasnim, M.; Graux, D.; Orlandi, F.; Collarana, D.

Volltext ()

Koubarakis, M.:
ASLD 2020, Advances in Semantics and Linked Data, Joint Workshop Proceedings from ISWC 2020. Online resource
La Clusaz: CEUR, 2020 (CEUR Workshop Proceedings 2722)
ISSN: 1613-0073
URN: urn:nbn:de:0074-2722-0
International Semantic Web Conference (ISWC) <19, 2020, Online>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()

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.