COREKG: Coreset-Guided Personalized Summarization of Knowledge Graphs

TL;DR

COREKG introduces a coreset-based method for personalized knowledge graph summarization, improving query accuracy and coverage while significantly reducing graph size based on user query patterns.

Contribution

It adapts coreset theory with sensitivity sampling to create personalized KG summaries tailored to user queries, outperforming existing methods.

Findings

COREKG achieves higher query-answering accuracy than state-of-the-art methods.

It produces smaller summaries with comparable or better structural coverage.

The approach is validated on Freebase, WikiData, and DBpedia datasets.

Abstract

Knowledge Graphs (KGs) are extensively used across different domains and in several applications. Often, these KGs are very large in size. Such KGs become unwieldy for tasks such as question answering and visualization. Summarization of KGs offers a viable alternative in such cases. Furthermore, personalized KG summarization is crucial in the current data-driven world as it captures the specific requirements of users based on their query patterns. Since it only maintains relevant information, the personalized summaries of KG are small, resulting in significantly smaller storage requirements and query runtime. In this work, we adapt the coreset theory to create personalized KG summaries. For a given dataset and a user-specific query workload, we present an approach that samples a relevant subset of triples using sensitivity-based importance sampling. We ensure that the subset approximates the characteristics of the full dataset with bounded approximation error. We define sensitivity scores that measure the importance of a triple with respect to a user's query workload, which are then used by our coreset construction algorithm. We explicitly focus on personalized knowledge graph summarization by constructing summaries independently for each user based on their query behaviour. Our evaluation on Freebase, WikiData, and DBpedia shows that COREKG delivers higher query-answering accuracy and structural coverage than the state-of-the-art methods, such as GLIMPSE, PPR, iSummary, PEGASUS and APEX$^2$ while requiring only a tiny fraction of the original graph.