iSummary: Workload-based, Personalized Summaries for Knowledge Graphs

TL;DR

iSummary is a scalable, personalized summarization method for Knowledge Graphs that leverages query logs to identify key resources, outperforming existing static summaries in quality and efficiency.

Contribution

The paper introduces iSummary, a novel approach that uses query logs to generate personalized, scalable summaries of Knowledge Graphs with theoretical quality guarantees.

Findings

Outperforms baseline methods in quality and efficiency

Provides theoretical guarantees on summary quality

Effective on real-world datasets

Abstract

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.