文献类型：期刊文献

英文题名：EASC: An exception-aware semantic compression framework for real-world knowledge graphs

作者：Jiang, Sihang[1];Feng, Jianchuan[1];Wang, Chao[2,3];Liu, Jingping[4];Xiong, Zhuozhi[1];Sha, Chaofeng[1];Zheng, Weiguo[5];Liang, Jiaqing[5];Xiao, Yanghua[1]

机构：[1]Fudan Univ, Sch Comp Sci, Shanghai Key Lab Data Sci, Shanghai, Peoples R China;[2]Shanghai Univ, Inst Artificial Intelligence, Shanghai, Peoples R China;[3]Shanghai Univ, Sch Future Technol, Shanghai, Peoples R China;[4]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China;[5]Fudan Univ, Sch Data Sci, Shanghai, Peoples R China

年份：2023

卷号：278

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：;EI(收录号：20233514658782);WOS:【SCI-EXPANDED(收录号:WOS:001069293300001)】；

语种：英文

外文关键词：Knowledge graph; Lossless compression; Semantic compression

摘要：Knowledge graphs (KGs) have achieved great success in many real applications, and great efforts have been dedicated to constructing larger knowledge graphs. An obvious trend in KG construction is that the KGs become ever-increasingly bigger. However, we argue that constructing a KG by directly inserting more triples may harm the performance of the KG, and one possible solution is KG compression. In this paper, we propose an exception-aware semantic lossless compression framework EASC to compress a KG. Since many triples can be inferred from other triples with semantic rules, we remove the triples that can be inferred and store the rules and exception cases. Specifically, we formalize the lossless compression problem as a weighted set cover problem, which is NP-hard, and propose a semantic lossless compression algorithm to get an approximation result. We conduct extensive experiments on seven real-world large-scale KGs. The results show that EASC achieves stateof-the-art performance in semantic compression methods. Furthermore, by combining EASC as an independent module with syntactic compression methods, we achieve state-of-the-art performance in lossless compression methods.& COPY; 2023 Elsevier B.V. All rights reserved.