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

TL;DR

APEX$^2$ introduces a scalable, adaptive summarization framework for personalized knowledge graphs that dynamically adjusts to evolving user interests, maintaining high utility even under extremely small size constraints.

Contribution

The paper presents APEX$^2$, a novel framework that continuously tracks interest shifts and adapts summaries of PKGs with theoretical guarantees, outperforming existing methods.

Findings

Outperforms state-of-the-art in accuracy and efficiency

Handles extremely small summaries with compression ratios ≤ 0.1%

Effective on large benchmark KGs with up to 12 million triples

Abstract

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. Furthermore, when the size constraint of PKG is extremely small, the existing methods cannot distinguish which facts are more of immediate interest and guarantee the utility of the summarized PKG. To address these limitations, we propose APEX$^2$, a highly scalable PKG summarization framework designed with robust theoretical guarantees to excel in adaptive summarization tasks with extremely small size constraints. To be specific, after constructing an initial PKG, APEX$^2$ continuously tracks the interest shift and adjusts the previous summary. We evaluate APEX$^2$ under an evolving query setting on benchmark KGs containing up to 12 million triples, summarizing with compression ratios $\leq 0.1\%$. The experiments show that APEX outperforms state-of-the-art baselines in terms of both query-answering accuracy and efficiency. Code is available at https://github.com/iDEA-iSAIL-Lab-UIUC/APEX.