基于綜合語(yǔ)義相似度的化工信息檢索方法
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中圖分類(lèi)號(hào)：TQ011;G252 文獻(xiàn)標(biāo)志碼：A 文章編號(hào)：1001-5922（2025）07-0121-04
Abstract：To improve theaccuracyof chemical informationretrieval，acomprehensivesemantic similarityretrieval method was proposed.A comprehensive semantic similarity calculation model was constructed by combining string semantic similarity，node semantic distance similarity，and neighboring nodesimilarity.To determine the weightsof diffrent semantic similarities，a generalized regression neural network（GRNN）optimized bythebat algorithm was used to train and alocate weight values.The comprehensive semantic similarity formula was used to retrieve semanticsinthe fieldof chemical engineering.Theresults indicatethatthesemanticretrieval resultsofchemical information obtained bythe proposed method are close to the expert rating results，with an average Pearson corelation coefficient value ofO.94.Compared to the comparison method，the calculation results of this method are more similar to the expert rating results.
Keywords：comprehensive semanticsimilarity;chemical information;semanticretrieval;bat algorithm；GRNN network
信息檢索是利用標(biāo)準(zhǔn)化知識(shí)表達(dá)對(duì)歷史數(shù)據(jù)進(jìn)行復(fù)用，為安全分析提供有效信息的重要途徑。（剩余5660字）