Semantic information query method of data ontology based on natural semantic understanding

Qiaohui Liu; Yongguang Liu; Jia Lin

doi:10.1117/12.2645047

30 August 2022 Semantic information query method of data ontology based on natural semantic understanding

Qiaohui Liu, Yongguang Liu, Jia Lin

Proceedings Volume 12309, International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2022); 123092W (2022) https://doi.org/10.1117/12.2645047
Event: International Conference on Advanced Manufacturing Technology and Manufacturing System (ICAMTMS 2022), 2022, Shijiazhuang, China

Abstract

Aiming at improving the poor effect of data ontology semantic information query, based on the existing semantic retrieval and combined with natural semantic processing technology, a data ontology semantic information query method based on natural semantic understanding is proposed. The natural semantic pattern is used to define the corresponding data ontology extraction rules, and the synonyms of domain ontology are obtained from relevant data. Further, Jena reasoning is used, and the implicit knowledge is obtained by combining domain knowledge and custom reasoning rules. Based on the natural semantics, a word similarity algorithm is proposed to analyze the syntax and semantics of the natural semantic query input by users, and construct SPARQL query sentences to realize the natural semantic query. The accuracy deviation formula is proposed to measure the rationality of the sorting algorithm, which simplifies the semantic information query steps of the data ontology. Experiments show that this method can obtain semantic information and effectively improve the accuracy of semantic information of data ontology.

Citation Download Citation

Qiaohui Liu, Yongguang Liu, and Jia Lin "Semantic information query method of data ontology based on natural semantic understanding", Proc. SPIE 12309, International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2022), 123092W (30 August 2022); https://doi.org/10.1117/12.2645047

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KEYWORDS

Information technology

Operating systems

Evolutionary algorithms

Genetic algorithms

Human-machine interfaces

Artificial intelligence

Associative arrays

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