APEX$^2$: Adaptive and Extreme Summarization for Personalized Knowledge Graphs

Understand

Knowledge graphs (KGs), which store an extensive number of relational facts, serve various applications.

• Recently, personalized knowledge graphs (PKGs) have emerged as a solution to optimize storage costs by customizing their content to align with users' specific interests within particular domains.
• In the real world, on one hand, user queries and their underlying interests are inherently evolving, requiring PKGs to adapt continuously; on the other hand, the summarization is constantly expected to be as small as possible in terms of storage cost.
• However, the existing PKG summarization methods implicitly assume that the user's interests are constant and do not shift.