A Robust Two-Part Modeling Strategy for Knowledge Graph Enhanced Recommender Systems

Gao, Min; Du, Ke-Lin; Zhu, Peiyao; Li, Jianyu; Wang, Hua; Zhan, Zhi-Hui

doi:10.1109/icaci58115.2023.10146164

Cited by 2 publications

(1 citation statement)

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“…The semantic coherence information in text, as one form of data feature, can be a source of inspiration. M. Gao et al [20] proposed that the multi-relational semantics in knowledge graphs can further enhance the model's interpretability, but an excessive reliance on the feature data from knowledge graphs may limit expressive capacity. In recent years, as deep learning has made significant gains in the field of artificial intelligence, researchers have experimented with deep learning to analyse the coherence of text, and some progress has been made.…”

Section: Related Workmentioning

confidence: 99%

Semantic Coherence Analysis of English Texts Based on Sentence Semantic Graphs

Deng,

Wang,

Huang

et al. 2023

ICST Transactions on Scalable Information Systems

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With the reform of China's education industry, more and more universities are using computers to conduct examinations. For the automatic correction of essays as subjective questions, existing automatic English text scoring systems suffer from insufficient extraction of coherence information and low accuracy when analysing text coherence. Therefore, this paper proposes an unsupervised semantic coherence analysis model for English texts based on sentence semantic graphs, taking Chinese students' English compositions as the research context. Guided by the semantic coherence theory, the English text is represented as a sentence semantic graph, and an improved VF2 subgraph matching algorithm is used to mine the frequently occurring subgraph patterns in the sentence semantic graph. After that, the set of frequent subgraphs is generated by filtering the subgraph patterns according to their frequencies, and the subgraph frequency of each frequent subgraph is calculated separately. Finally, the distribution characteristics of frequent subgraphs and the semantic values of subgraphs in the sentence semantic graphs are extracted to quantify the overall coherence quality of English texts. The experimental results show that the model proposed in this paper has higher accuracy and practical value compared with the current methods of coherence analysis.

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Section: Related Workmentioning

confidence: 99%