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Utility-driven knowledge graph summarization with structural-semantic fidelity
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AI
DOI：10.1007/s10618-026-01256-1.png)
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Abstract
En 中文
Knowledge Graph (KG) summarization is the process of producing compact representations to improve KG comprehension, support efficient search, and facilitate downstream applications. Existing summarization techniques have made significant progress in preserving either semantic or structural information. However, they cannot directly control trade-offs between the two dimensions in a lossy setting. Existing lossy summarization approaches evaluate information loss after the summary is generated (post-hoc); but they lack a mechanism to proactively control loss during the summarization process. This study introduced a dual-utility metric that evaluates both structural preservation and semantic cohesiveness. Three algorithms were developed: LIDUS, LDUS, and aLDUS. The first algorithm performs lossless summarization while preserving structural and semantic integrity. The second is a utility-guided lossy summarization method that iteratively merges ranked entity pairs, minimizing utility loss at each iteration. The third is an optimization algorithm that reduces time complexity by incrementally computing semantic loss, while guaranteeing a bounded utility loss. Experiments were conducted on five real-world and synthetic KGs to demonstrate the accuracy of the top-10% PageRank query on reconstructed KGs from aLDUS summaries. Across five datasets, the utility threshold 0.8 yields an average of 0.89. ALDUS summaries yield average cohesiveness ratios in the range of 0.93–1.21 compared with competing methods. Our model achieves the shortest summarization runtime across three datasets and remains comparable to the fastest approaches on the other two datasets. Simple and complex queries show that LDUS/aLDUS achieves an average F1 of 0.88 while reducing query time by 97% on average.
Keywords:
KG summarization
Structural preservation
Lossy summarization
Utility driven summarization
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