Reducing Large Semantic Graphs to Improve Semantic Relatedness

Costa, Teresa; Leal, José Paulo

doi:10.1007/978-3-319-27653-3_23

Cited by 1 publication

(3 citation statements)

References 22 publications

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“…However, the resultant graph is not suitable for querying the information. [15,17] proposed an approach to summarize large semantics graphs using namespaces. For this, the author used namespaces to create summary graphs of reduced size, for the sake of more meaningful visualization.…”

Section: A Structural Methodsmentioning

confidence: 99%

“…Idea of their approach is subjective to us but the data dependent application still misses information of triples when replaced with rules. In similar lines, [3,21,35] presented to find Bi-Similarity among the nodes as node merging criteria which was further staggered in [37,95] for information mining. Its focus is to reduce the size by ignoring its structure which causes complexity increase in the graph.…”

Section: B Pattern Mining Methodsmentioning

confidence: 99%

“…Another study [21], investigates such approaches as summarizing big graphs. [37] is a recent contribution of summarizing large knowledge graphs using namespaces. Similar to the previous, Another recent contribution of compression is discussed in [29,97].…”

Section: Hybrid Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Instance-Based Lossless Summarization of Knowledge Graph With Optimized Triples and Corrections (IBA-OTC)

Javed,

Khan,

Cheema

et al. 2024

IEEE Access

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Knowledge graph (KG) summarization facilitates efficient information retrieval for exploring complex structural data. For fast information retrieval, it requires processing on redundant data. However, it necessitates the completion of information in a summary graph. It also saves computational time during data retrieval, storage space, in-memory visualization, and preserving structure after summarization. Stateof-the-art approaches summarize a given KG by preserving its structure at the cost of information loss. Additionally, the approaches not preserving the underlying structure, compromise the summarization ratio by focusing only on the compression of specific regions. In this way, these approaches either miss preserving the original facts or the wrong prediction of inferred information. To solve these problems, we present a novel framework for generating a lossless summary by preserving the structure through super signatures and their corresponding corrections. The proposed approach summarizes only the naturally overlapped instances while maintaining its information and preserving the underlying Resource Description Framework RDF graph. The resultant summary is composed of triples with positive, negative, and star corrections that are optimized by the smart calling of two novel functions namely merge and disperse. To evaluate the effectiveness of our proposed approach, we perform experiments on nine publicly available real-world knowledge graphs and obtain a better summarization ratio than state-of-the-art approaches by a margin of 10% to 30% with achieving its completeness, correctness, and compactness. In this way, the retrieval of common events and groups by queries is accelerated in the resultant graph.

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Section: A Structural Methodsmentioning

confidence: 99%