iSummary: Workload-based, Personalized Summaries for Knowledge Graphs
Giannis Vassiliou, Fanouris Alevizakis, Nikolaos Papadakis, Haridimos, Kondylakis

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…
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Taxonomy
TopicsSemantic Web and Ontologies · Advanced Graph Neural Networks · Data Quality and Management
