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Summarizing Entity Temporal Evolution in Knowledge Graphs

: Tasnim, Mayesha; Collarana, Diego; Graux, Damien; Orlandi, Fabrizio; Vidal, Maria-Esther


Amer-Yahia, S. ; Association for Computing Machinery -ACM-:
The Web Conference 2019 : Companion Proceedings of The 2019 World Wide Web Conference; May 13-17, 2019, San Francisco, CA, USA
New York: ACM, 2019
ISBN: 978-1-4503-6675-5
World Wide Web Conference (WWW) <28, 2019, San Francisco/Calif.>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
01IS18050F; MLWin
Conference Paper
Fraunhofer IAIS ()
RDF Knowledge Graph; RDF Molecules; Entity Evolution

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 limit to pairwise version comparison. Making it difficult to capture complete evolution out of several versions of the same 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.