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基于知識(shí)圖譜的花卉病蟲害知識(shí)管理方法
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上海市科技創(chuàng)新計(jì)劃項(xiàng)目(20dz1203800)


Knowledge Management Method of Flower Diseases and Pests Based on Knowledge Graph
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    摘要:

    為解決花卉病蟲害領(lǐng)域中病蟲害防治因素關(guān)系復(fù)雜,、知識(shí)冗余等問(wèn)題,,結(jié)合知識(shí)圖譜對(duì)知識(shí)組織和管理的技術(shù),,提出一種基于知識(shí)圖譜的花卉病蟲害知識(shí)管理方法,。首先,,根據(jù)文獻(xiàn)提取包括環(huán)境在內(nèi)的花卉病蟲害防治要素,,構(gòu)建花卉病蟲害本體模型并存儲(chǔ)在RDF圖中,實(shí)現(xiàn)對(duì)知識(shí)規(guī)范性和完整性的控制,;其次,,對(duì)花卉病蟲害領(lǐng)域文本進(jìn)行分析,針對(duì)分析得到的文本特點(diǎn),,提出融合頭尾實(shí)體分離“01”標(biāo)注方法,、輕量級(jí)雙向轉(zhuǎn)換編碼表示模型(A lite BERT, ALBERT)和引入詞性特征的級(jí)聯(lián)標(biāo)注模型(CasPOSRel)的抽取框架進(jìn)行三元組抽取,;之后利用自定義RDF2PG映射算法,,按照RDF圖中的本體模型將抽取到的三元組存入Neo4j數(shù)據(jù)庫(kù)中,完成對(duì)花卉病蟲害知識(shí)的存儲(chǔ)及管理,。實(shí)驗(yàn)結(jié)果證明提出的抽取框架中標(biāo)注方法,、預(yù)訓(xùn)練模型與抽取模型相比基線方法F1值分別提升0.88、4.90,、8.57個(gè)百分點(diǎn),,同時(shí)得到抽取結(jié)果F1值為95.07%。通過(guò)知識(shí)發(fā)現(xiàn)表明該知識(shí)管理方法能有效組織管理病蟲害知識(shí),,幫助花卉種植人員進(jìn)行更為有效的病蟲害防治工作,。

    Abstract:

    In order to solve the problems of complex relationship of factors and mixed knowledge in the field of flower diseases and pests, combined with the knowledge organization and management technology of knowledge graph, a knowledge management method of flower diseases and pests based on knowledge graph was proposed. Firstly, according to the literatures, the flower diseases and pests control elements, including environment were extracted, the flower diseases and pests ontology model was constructed and stored in RDF to realize the control of knowledge standardization and integrity. Secondly, according to the text characteristics obtained from the analysis, the triple extraction framework was proposed which combined the “01” tagging method of head and tail entity separation, a lite bidirectional encoder representations from transformers (ALBERT) and cascade tagging model with part of speech features (CasPOSRel).Then using the custom RDF2PG mapping algorithm to complete the storage and management of flower diseases and pests knowledge. The experiments showed that the F1 value of the tagging methods, pretrained model and extraction model in proposed extraction framework was increased by 0.88, 4.90 and 8.57 percentage points compared with that of baseline methods, and the F1 value of the extraction result was 95.07%. The knowledge discovery showed that the knowledge management method effectively organized and managed the knowledge of flower diseases and pests, and helped the flower growers to carry out more effective pests control work.

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陳明,朱玨樟,席曉桃.基于知識(shí)圖譜的花卉病蟲害知識(shí)管理方法[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2023,54(3):291-300. CHEN Ming, ZHU Juezhang, XI Xiaotao. Knowledge Management Method of Flower Diseases and Pests Based on Knowledge Graph[J]. Transactions of the Chinese Society for Agricultural Machinery,2023,54(3):291-300.

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  • 收稿日期:2022-05-24
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  • 在線發(fā)布日期: 2023-03-10
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