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- W4385958648 abstract "Due to the explosive growth of semantic data over recent years, extracting critical and representative information from knowledge graphs (KGs), i.e., KG summarization is a challenging task under real-world resource constraints such as memories and response time. We present scalable utility-driven algorithms for summarizing large-scale KGs based on the features of entities. Specifically, we propose the notion of utility for KG summaries and develop the SiFS algorithm for efficient lossless KG summarization. Second, we present the highly scalable KG summarization algorithm ScFS for generating lossy summaries by user defined utility threshold. The experiments over real-world and synthetic datasets show that SiFS achieves more compact summary sizes and significant reductions in execution time. Moreover, ScFS outperforms state-of–the–art summarization methods significantly in summary size and is about two orders of magnitude better in terms of speed. Third, we present a query evaluation algorithm over KGs based on SiFS and ScFS. The experiments results show that the proposed summaries facilitate efficient and high-quality KG queries." @default.
- W4385958648 created "2023-08-18" @default.
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- W4385958648 date "2023-01-01" @default.
- W4385958648 modified "2023-10-17" @default.
- W4385958648 title "Scalable Summarization for Knowledge Graphs with Controlled Utility Loss" @default.
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- W4385958648 doi "https://doi.org/10.1007/978-3-031-39847-6_2" @default.
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