# iSummary: Workload-based, Personalized Summaries for Knowledge Graphs

arXiv: https://arxiv.org/abs/2403.02934 · HTML: https://mlsanity.com/paper/2403.02934/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2024.

## Built on by (most cited first, 3 of 3)

- [Abstractive Text Summarization: State of the Art, Challenges, and Improvements](https://mlsanity.com/paper/2409.02413/) (2024)
- [APEX2: Adaptive and Extreme Summarization for Personalized Knowledge Graphs](https://mlsanity.com/paper/2412.17336/) (2024)
- [COREKG: Coreset-Guided Personalized Summarization of Knowledge Graphs](https://mlsanity.com/paper/2605.14900/) (2026)

## Abstract (arXiv)

The explosion in the size and the complexity of the available Knowledge Graphs on the web has led to the need for efficient and effective methods for their understanding and exploration. Semantic summaries have recently emerged as methods to quickly explore and understand the contents of various sources. However in most cases they are static not incorporating user needs and preferences and cannot scale. In this paper we present iSummary a novel scalable approach for constructing personalized summaries. As the size and the complexity of the Knowledge Graphs for constructing personalized summaries prohibit efficient summary construction, in our approach we exploit query logs. The main idea behind our approach is to exploit knowledge captured in existing user queries for identifying the most interesting resources and linking them constructing as such highquality personalized summaries. We present an algorithm with theoretical guarantees on the summarys quality linear in the number of queries available in the query log. We evaluate our approach using three realworld datasets and several baselines showing that our approach dominates other methods in terms of both quality and efficiency.