Authors: Ze Zheng; Xiangfeng Luo; Hao Wang
Addresses: School of Computer Engineering and Science, Shanghai University, No. 99 Shangda Road, Shanghai, China ' School of Computer Engineering and Science, Shanghai University, No. 99 Shangda Road, Shanghai, China ' School of Computer Engineering and Science, Shanghai University, No. 99 Shangda Road, Shanghai, China
Abstract: Entity summarisation has drawn a lot of attention in recent years. But there still exist some problems. Firstly, most of the previous works focus on individual entity summarisation while ignoring the effect of neighbours. Secondly, the external resources which may be unavailable in practice are frequently used to calculate the similarity between resource description framework (RDF) triples. To solve the above two problems, this paper focuses on multi-entity summarisation. A topic model-based model multi-entity summarisation in RDF graph (MESRG) is proposed for multi-entity summarisation, which is capable of extracting informative and diverse summaries involving a two-phase process: 1) to select more important RDF triples, we propose an improved topic model that ranks triples with probability values; 2) to select diverse RDF triples, we use a graph embedding method to calculate the similarity between triples and obtain top k distinctive triples. Experiments of our model with significant results on the benchmark datasets demonstrate the effectiveness.
Keywords: semantic web; knowledge graph; multi-entity summarisation; extract subgraph; rank triples; RDF graph; topic model; Gibbs sampling; deep walk; graph embedding.
International Journal of Computational Science and Engineering, 2020 Vol.23 No.1, pp.74 - 81
Received: 08 Feb 2020
Accepted: 27 Feb 2020
Published online: 08 Oct 2020 *