Details
| Original language | English |
|---|---|
| Title of host publication | The Web Conference 2019 |
| Subtitle of host publication | Companion of the World Wide Web Conference, WWW 2019 |
| Editors | Ling Liu, Ryen White |
| Place of Publication | New York |
| Pages | 961-965 |
| Number of pages | 5 |
| ISBN (electronic) | 9781450366755 |
| Publication status | Published - 13 May 2019 |
| Event | 2019 World Wide Web Conference, WWW 2019 - San Francisco, United States Duration: 13 May 2019 → 17 May 2019 |
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.
Keywords
- Entity Evolution, RDF Knowledge Graph, RDF Molecules
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Software
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The Web Conference 2019: Companion of the World Wide Web Conference, WWW 2019. ed. / Ling Liu; Ryen White. New York, 2019. p. 961-965.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Summarizing entity temporal evolution in knowledge graphs
AU - Tasnim, Mayesha
AU - Collarana, Diego
AU - Graux, Damien
AU - Orlandi, Fabrizio
AU - Vidal, Maria Esther
N1 - Funding information: This work is supported by the German Ministry of Education and Research (BMBF) in the context of the project MLwin (“Maschinelles Lernen mit Wissensgraphen”, grant no. 01IS18050F).
PY - 2019/5/13
Y1 - 2019/5/13
N2 - 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.
AB - 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.
KW - Entity Evolution
KW - RDF Knowledge Graph
KW - RDF Molecules
UR - http://www.scopus.com/inward/record.url?scp=85066903361&partnerID=8YFLogxK
U2 - 10.1145/3308560.3316521
DO - 10.1145/3308560.3316521
M3 - Conference contribution
AN - SCOPUS:85066903361
SP - 961
EP - 965
BT - The Web Conference 2019
A2 - Liu, Ling
A2 - White, Ryen
CY - New York
T2 - 2019 World Wide Web Conference, WWW 2019
Y2 - 13 May 2019 through 17 May 2019
ER -