柑橘肥水智能決策支持系統(tǒng)的變化預(yù)測(cè)方法及應(yīng)用效果

王 藝，王 英（西南大學(xué)計(jì)算機(jī)與信息科學(xué)學(xué)院，重慶 400715）

柑橘肥水智能決策支持系統(tǒng)的變化預(yù)測(cè)方法及應(yīng)用效果

王 藝，王 英
（西南大學(xué)計(jì)算機(jī)與信息科學(xué)學(xué)院，重慶 400715）

本體是農(nóng)業(yè)智能信息系統(tǒng)的核心，是實(shí)現(xiàn)精準(zhǔn)農(nóng)業(yè)信息服務(wù)的關(guān)鍵。本體的維護(hù)和管理過(guò)程將導(dǎo)致本體發(fā)生各種變化，從而對(duì)其支撐的應(yīng)用程序產(chǎn)生不同程度的影響。如何有效地分析本體元素的變化對(duì)應(yīng)用程序的影響是農(nóng)業(yè)智能信息系統(tǒng)維護(hù)和管理的難題。該文提出一種基于界面組件依賴矩陣、本體概念依賴矩陣及本體概念-界面組件依賴矩陣的系統(tǒng)變化預(yù)測(cè)方法，實(shí)現(xiàn)了避免代碼層分析而較準(zhǔn)確預(yù)測(cè)本體概念的變化對(duì)應(yīng)用程序用戶界面組件的影響。以包含22個(gè)本體概念和6個(gè)界面組件的柑橘肥水智能決策支持系統(tǒng)為案例分析，驗(yàn)證結(jié)果表明：該變化預(yù)測(cè)方法能夠達(dá)到85%的平均準(zhǔn)確率和98%的平均召回率。該變化預(yù)測(cè)方法對(duì)解決以本體為核心的農(nóng)業(yè)智能信息系統(tǒng)的變化管理難題可提供有效的解決方案。

系統(tǒng)分析；軟件結(jié)構(gòu)；預(yù)測(cè)；語(yǔ)義本體；本體變化管理；柑橘肥水決策支持系統(tǒng)；軟件變化管理

0 引 言

智能農(nóng)業(yè)信息系統(tǒng)以高質(zhì)量的領(lǐng)域本體為核心，以期實(shí)現(xiàn)精準(zhǔn)及個(gè)性化的農(nóng)業(yè)決策支持服務(wù)[1-8]。在開(kāi)發(fā)此類系統(tǒng)時(shí)，由于農(nóng)業(yè)領(lǐng)域知識(shí)具有海量和多源的特點(diǎn)，本體的開(kāi)發(fā)一般以決策支持任務(wù)為目標(biāo)，進(jìn)行相關(guān)部分本體的構(gòu)建，而不是完成全部本體再進(jìn)行應(yīng)用程序開(kāi)發(fā)工作[9-13]。針對(duì)不同應(yīng)用領(lǐng)域的農(nóng)業(yè)本體構(gòu)建工作取得一定進(jìn)展[4,14-20]，包括標(biāo)準(zhǔn)詞匯如AGROVOC[14]和NAL農(nóng)業(yè)術(shù)語(yǔ)集[15]，柑橘肥水本體[4]、土豆本體[16]、大米本體[17]、農(nóng)產(chǎn)品冷鏈管理體系本體[18]、水質(zhì)本體[19]及玉米病蟲(chóng)害本體[20]。當(dāng)本體變化時(shí)，例如本體元素的刪除或者增加，本體所支撐的應(yīng)用程序，將會(huì)受到不同程度的影響，甚至導(dǎo)致應(yīng)用程序的功能失效[21-24]。因此，以本體為核心的農(nóng)業(yè)信息系統(tǒng)需要解決：當(dāng)本體元素有變化時(shí)，如何預(yù)測(cè)并獲取其支撐的應(yīng)用程序功能受到的影響情況。

現(xiàn)有相關(guān)研究主要集中于本體進(jìn)化管理[21-24]，以及軟件系統(tǒng)的變化管理[25-32]，而缺乏針對(duì)基于本體的智能決策系統(tǒng)的變化管理的研究，因而無(wú)法解決本體變化所導(dǎo)致的應(yīng)用程序的變化影響分析和預(yù)測(cè)問(wèn)題。

本文提出一種本體概念的變化對(duì)應(yīng)用程序界面組件影響的預(yù)測(cè)方法，以期為智能農(nóng)業(yè)信息系統(tǒng)的變化管理難題提供可行的解決方法，并以柑橘肥水智能決策支持系統(tǒng)[4]為案例進(jìn)行驗(yàn)證。

1 柑橘肥水智能決策支持系統(tǒng)變化管理

如圖1所示，基于本體的柑橘肥水智能決策支持系統(tǒng)由柑橘肥水本體、基于本體的應(yīng)用程序以及界面組件構(gòu)成。本體包含了施肥和排灌專家知識(shí)，是決策支持系統(tǒng)的核心。本體由概念、實(shí)例和屬性組成。實(shí)例是概念所包含的成員，屬性體現(xiàn)實(shí)例間的關(guān)系?；诟涕俦倔w的應(yīng)用及程序可基于各類平臺(tái)，如Web應(yīng)用、安卓手機(jī)應(yīng)用等，進(jìn)行訪問(wèn)。界面組件包含文本框和下拉菜單等常用界面元素，它體現(xiàn)了應(yīng)用程序的功能，實(shí)現(xiàn)應(yīng)用程序與用戶的交互[4,28]。

圖1 柑橘肥水智能決策支持系統(tǒng)及其變化管理Fig.1 Citrus fertilization and irrigation intelligent decision support system and its change management

由于系統(tǒng)維護(hù)以及用戶需求變化等，本體會(huì)不斷進(jìn)行修改和擴(kuò)展[23-24]。當(dāng)本體有變化時(shí)，構(gòu)建在本體之上的應(yīng)用程序?qū)⑹艿讲煌潭鹊挠绊?。直觀地說(shuō)，用戶交互界面組件可能發(fā)生改變，例如：增加了用戶輸入信息的要求或者刪除某個(gè)下拉菜單選項(xiàng)等。本文的方法是基于本體概念和界面組件在概念層的直接關(guān)聯(lián)矩陣，構(gòu)建變化影響傳播樹(shù)，計(jì)算本體概念的變化預(yù)測(cè)值，實(shí)現(xiàn)本體概念變化對(duì)用戶界面組件的預(yù)測(cè)。

2 基于依賴矩陣及變化影響傳播樹(shù)的變化預(yù)測(cè)方法

設(shè)集合C={c1, …, cn}包含本體的所有與領(lǐng)域直接相關(guān)的概念，不考慮通用的概念如owl:Thing等。集合IC= {ic1, …, icm}包含應(yīng)用程序的所有組件。集合V=, …, v包含所有界面變量。令V(ic)?V，表示組件ic關(guān)聯(lián)的界面變量集合。對(duì)界面組件依賴矩陣、本體概念依賴矩陣、本體概念-界面組件依賴矩陣和變化影響傳播樹(shù)進(jìn)行定義和說(shuō)明如下。

2.1 依賴矩陣定義

2.1.1 界面組件依賴矩陣

界面組件依賴矩陣Mic=(ωij)m×m,是m階矩陣，表示組件ici依賴于組件icj的程度，ωij介于0～1之間

式中|V(ici) ∩V(icj)|表示ici和icj共同的界面變量的個(gè)數(shù)，|V(ici) ∪V(icj)|表示ici和icj包含的所有界面變量的個(gè)數(shù)。

界面組件依賴矩陣Mic通過(guò)界面組件間共同的界面變量在概念層建立了界面組件間的關(guān)聯(lián)[28]。這里，概念層是指應(yīng)用程序的設(shè)計(jì)和用戶界面層，它是相對(duì)于程序源代碼層而言。

2.1.2 本體概念依賴矩陣

本體概念依賴矩陣Mc=(rij)n×n，是n階矩陣，rij介于0～1之間，表示本體概念ci依賴cj的程度。設(shè)在本體中ci與包含cj的共K＜n個(gè)概念通過(guò)本體屬性有關(guān)聯(lián)，則

本體概念依賴矩陣描述本體概念間的相互關(guān)聯(lián)度。Mc元素rij的計(jì)算采用依賴圖算法[29]。

圖2 本體概念及與界面組件間的依賴關(guān)系圖Fig.2 Ontology concept dependency and dependency between ontology concept and interface component

2.1.3 本體概念-界面組件依賴矩陣

本體概念-界面組件依賴矩陣Mc->ic=(dij)m×n，dij介于0～1之間，表示概念cj對(duì)組件ici的影響程度。設(shè)ici同時(shí)關(guān)聯(lián)于包含cj的R＜n個(gè)概念，則

本體概念-界面組件依賴矩陣描述了本體概念對(duì)界面組件在概念層的直接關(guān)聯(lián)。

2.2 變化影響傳播樹(shù)

2.2.1 變化影響傳播樹(shù)定義及算法

變化影響傳播樹(shù)T=(X, Z)是一棵樹(shù)，其根節(jié)點(diǎn)是r∈C, r為發(fā)生初始變化的本體概念，節(jié)點(diǎn)集合X由IC和C中所有元素構(gòu)成，邊集合Z=(vi,vj)，vi, vj∈X，表示vi對(duì)vj有影響。從根節(jié)點(diǎn)r到任何葉節(jié)點(diǎn)的路徑p=r…vt，不允許有重復(fù)的節(jié)點(diǎn)出現(xiàn)。算法1是構(gòu)建以本體概念cs為根的變化影響傳播樹(shù)的方法，其中threshold是樹(shù)高閾值，h是表示樹(shù)高的變量。

算法1

2.2.2 變化影響傳播樹(shù)的構(gòu)建過(guò)程

給定Mic，Mc及Mc->ic，圖3是概念c1的變化影響傳播樹(shù)且樹(shù)高閾值threshold為3。變化影響樹(shù)的樹(shù)根為c1，即X0={c1}。檢索Mic，Mc及Mc->ic知c1對(duì)ic1, ic3, c3, c5均有影響，得到X1={ic1, ic3, c3, c5}，即如圖3所示的樹(shù)高度為1的所有節(jié)點(diǎn)。對(duì)X1中的所有節(jié)點(diǎn)，檢索Mic，Mc及Mc->ic，找出相應(yīng)的影響節(jié)點(diǎn)，并排除從根節(jié)點(diǎn)到所有節(jié)點(diǎn)的路徑上有重復(fù)節(jié)點(diǎn)的情況。以ic1為例，由Mic知，ic1影響ic2, ic4, ic5, 得到圖3所示ic1的3個(gè)子節(jié)點(diǎn)。以此類推，得到X2={ ic1, ic2, ic4, ic5, c2, c4, c6}，如圖3所示的樹(shù)高度為2的所有節(jié)點(diǎn)。同理可得樹(shù)高度為3的所有節(jié)點(diǎn)集合X3。

圖3 概念c1的變化影響傳播樹(shù)及概念c1對(duì)ic2的變化影響路徑Fig.3 Change impact propagation tree for concept c1and change impact paths from c1to ic2

2.2.3 變化預(yù)測(cè)值的計(jì)算方法

根據(jù)界面組件依賴矩陣Mic、本體概念依賴矩陣Mc及本體概念-界面組件依賴矩陣Mc->ic所描述的元素間直接關(guān)聯(lián)所構(gòu)建的變化影響傳播樹(shù)，反映了本體概念對(duì)界面組件的綜合影響情況。為量化本體概念對(duì)界面組件的綜合影響程度，本文提出如下方法計(jì)算該綜合影響程度，并將量化的綜合影

響程度稱為變化預(yù)測(cè)值。變化預(yù)測(cè)值的計(jì)算方法是：將變化影響傳播樹(shù)看作邏輯樹(shù)，即樹(shù)的同一條路徑的節(jié)點(diǎn)看作“與”（and），而分支節(jié)點(diǎn)看作“或”（or）運(yùn)算[30]，根據(jù)邏輯樹(shù)的“與”和“或”計(jì)算式（4）（5）所示，可獲取本體概念對(duì)界面組件的變化預(yù)測(cè)值。

其中vivjvk是一條路徑，evi→vj和evj→vk分別是vi對(duì)vj的變化預(yù)測(cè)值（表示vi的變化對(duì)vj產(chǎn)生影響的程度，其值介于0～1之間）和vj對(duì)vk的變化預(yù)測(cè)值（表示vj的變化對(duì)vk產(chǎn)生影響的程度，其值介于0～1之間）。若vi(vj)對(duì)vj(vk)有直接影響，其數(shù)值分別由矩陣Mic，Mc和Mc->ic的相應(yīng)元素ωij、γij和dij給出。

其中vivj和vivk是變化影響傳播樹(shù)的2條路徑，vi是分支節(jié)點(diǎn)。

圖3中c1到ic2有8條間接影響路徑（圖3所示圓圈標(biāo)示），分別通過(guò)4個(gè)節(jié)點(diǎn)ic1、ic3、c3和c5傳播變化影響，故c1對(duì)ic2的變化預(yù)測(cè)值可依據(jù)式（4）、式（5）計(jì)算所得為0.52。

故當(dāng)c1改變時(shí)，導(dǎo)致界面組件ic2發(fā)生變化的可能性為52%。

2.2.4 變化影響傳播樹(shù)的高度限制

變化影響傳播樹(shù)的樹(shù)高用變量threshold進(jìn)行了限制，其原因是：首先，當(dāng)節(jié)點(diǎn)數(shù)量較大時(shí)，變化影響傳播樹(shù)從根至葉節(jié)點(diǎn)的路徑會(huì)很長(zhǎng)，數(shù)據(jù)處理的時(shí)間復(fù)雜度呈路徑長(zhǎng)度的指數(shù)增長(zhǎng)[30]。語(yǔ)義本體的概念數(shù)量通常較大（例如AGROVOC[14]本體有32 000個(gè)概念），因此必須限定樹(shù)高以控制計(jì)算復(fù)雜度。其次，由式（4）知，路徑越長(zhǎng)，概念對(duì)組件的間接影響值會(huì)減弱較快。故限定樹(shù)的高度以控制計(jì)算復(fù)雜度并獲得滿意的預(yù)測(cè)結(jié)果是合理且相關(guān)研究推薦策略，一般建議樹(shù)高為3[30]。

3 案例分析及方法驗(yàn)證

本文以柑橘肥水智能決策支持系統(tǒng)[4]對(duì)所提變化預(yù)測(cè)方法進(jìn)行驗(yàn)證。驗(yàn)證方法是對(duì)本體概念的變化，根據(jù)所提變化預(yù)測(cè)方法計(jì)算得到界面組件的變化預(yù)測(cè)值，通過(guò)與變化預(yù)測(cè)閾值比較，得到預(yù)測(cè)所有影響的界面組件，將其與系統(tǒng)組件變化的實(shí)際結(jié)果比較，以評(píng)價(jià)所提預(yù)測(cè)方法的有效性。系統(tǒng)變化的實(shí)際結(jié)果通過(guò)直接分析柑橘肥水智能決策支持系統(tǒng)的源代碼得到。

柑橘肥水智能決策支持系統(tǒng)[4]有3個(gè)子系統(tǒng)：施肥查詢、病癥查詢及排灌監(jiān)測(cè)，其界面組件共6個(gè)：按果園查詢（ic1），初次查詢（ic2），施肥建議（ic3），選擇病癥（ic4），查詢結(jié)果（ic5），監(jiān)控主頁(yè)（ic6），共關(guān)聯(lián)30個(gè)系統(tǒng)變量，得到界面組件依賴矩陣Mic。

柑橘肥水本體[4]共22個(gè)領(lǐng)域概念，根據(jù)其本體概念依賴關(guān)系圖，得到相應(yīng)的本體概念依賴矩陣Mc。

最后得到柑橘肥水決策系統(tǒng)的本體概念-界面組件依賴矩陣Mc->ic

表1是通過(guò)構(gòu)建變化影響傳播樹(shù)，并根據(jù)變化預(yù)測(cè)值的計(jì)算方法得到的22個(gè)概念對(duì)6個(gè)界面組件的變化預(yù)測(cè)值，以表示本體概念的變化對(duì)界面組件的綜合影響程度。

為驗(yàn)證變化影響預(yù)測(cè)的準(zhǔn)確性，對(duì)柑橘肥水智能決策支持系統(tǒng)[4]JSP/Servlet源碼進(jìn)行人工分析，得到本體概念變化對(duì)界面組件影響的實(shí)際結(jié)果（如表2所示）。為從表1獲取本體概念對(duì)界面組件的變化預(yù)測(cè)結(jié)果，以便與實(shí)際結(jié)果比較，本文采用設(shè)定變化預(yù)測(cè)閾值λ的方法。變化預(yù)測(cè)閾值λ介于0%～100%之間，是變化預(yù)測(cè)值有效的最低值，即：當(dāng)概念對(duì)界面組件的變化預(yù)測(cè)值大于等于λ時(shí)，則判斷概念的變化對(duì)界面組件有影響；否則概念的變化對(duì)界面組件沒(méi)有影響。以概念c1為例，當(dāng)變化預(yù)測(cè)閾值λ為5%時(shí)，由表1知c1對(duì)ic3, ic4和ic5的變化預(yù)測(cè)值均大于λ，故c1變化會(huì)影響ic3, ic4和ic5，即c1的變化預(yù)測(cè)結(jié)果為{ic3, ic4, ic5}。表2是根據(jù)表1的數(shù)據(jù)分別取變化預(yù)測(cè)閾值λ1=5%和λ2=10%所得到的界面組件的預(yù)測(cè)結(jié)果。

本文采用廣泛應(yīng)用于統(tǒng)計(jì)學(xué)和信息檢索領(lǐng)域的2個(gè)度量值準(zhǔn)確率和召回率（見(jiàn)式（6）、（7）），用于評(píng)價(jià)方法的有效性。P是變化影響預(yù)測(cè)結(jié)果集合，E是源代碼分析結(jié)果，p是預(yù)測(cè)的準(zhǔn)確率，r是預(yù)測(cè)的召回率。注意到，當(dāng)E為空集時(shí)，r為1。若E和P同時(shí)為空集，p為1。這里，準(zhǔn)確率p表示預(yù)測(cè)結(jié)果集合P中，有多少組件是真正的受到變化影響的。召回率r表示有多少實(shí)際組件集合E中的元素被正確預(yù)測(cè)到。以表2中c22為例，當(dāng)λ1=5%時(shí)，P={ic3,ic4, ic5}，E={ic4, ic5}，由式（6）得p為0.67，由式（7）得r為1，說(shuō)明該方法能正確預(yù)測(cè)67%的組件，且100%預(yù)測(cè)到受影響的組件。一般情況下，準(zhǔn)確率和召回率是沖突的，即高準(zhǔn)確率會(huì)導(dǎo)致低召回率，反之，高召回率可能導(dǎo)致低準(zhǔn)確率。表2給出了變化預(yù)測(cè)閾值λ1=5%和λ2=10%的相應(yīng)準(zhǔn)確率和召回率。試驗(yàn)結(jié)果表明，當(dāng)設(shè)定較小的變化預(yù)測(cè)閾值時(shí)可以得到較高的召回率，而準(zhǔn)確率有所下降；反之，當(dāng)設(shè)定較大的變化預(yù)測(cè)閾值時(shí)，則可以得到較高的準(zhǔn)確率，但召回率就有所下降。式（8）、（9）是平均準(zhǔn)確率和平均召回率，用于評(píng)價(jià)22個(gè)本體概念的平均預(yù)測(cè)結(jié)果。其中pi表示ci的準(zhǔn)確率，ri是召回率，n是概念總數(shù)。

表1 本體概念對(duì)界面組件的變化預(yù)測(cè)值Table 1 Change prediction values for interface components relating to ontology concepts %

表2 本體概念對(duì)界面組件變化預(yù)測(cè)結(jié)果及其準(zhǔn)確率和召回率分析Table 2 Change prediction results for interface components relating to ontology concepts and analysis of precision and recall rates

準(zhǔn)確率p和召回率r在不同應(yīng)用領(lǐng)域和需求有不同的參考范圍[28,31-34]。在軟件系統(tǒng)管理領(lǐng)域，準(zhǔn)確率高于40%而召回率高于60%，可以證明方法的有效性[28,31-32]；在知識(shí)提取領(lǐng)域[33-34]，準(zhǔn)確率高于60%，而召回率高于40%，可以證明方法的有效性。表2的驗(yàn)證結(jié)果顯示，當(dāng)變化預(yù)測(cè)閾值取5%時(shí)，22個(gè)概念的預(yù)測(cè)平均準(zhǔn)確率為77%，平均召回率為98%；當(dāng)變化預(yù)測(cè)閾值取10%時(shí)，22個(gè)本體概念的預(yù)測(cè)平均準(zhǔn)確率為85%，平均召回率為74%，證明該方法的有效性。當(dāng)把預(yù)測(cè)結(jié)果的閾值從5%提高到10%時(shí)，精確率由77%提升到85%，而召回率由98%下降到74%。閾值5%和10%是根據(jù)參考文獻(xiàn)[28,30]及筆者試驗(yàn)所取的經(jīng)驗(yàn)值，可以根據(jù)實(shí)際需要在此基礎(chǔ)上調(diào)整。

4 結(jié) 論

本文針對(duì)在基于語(yǔ)義本體的農(nóng)業(yè)決策支持系統(tǒng)中，分析和管理本體概念變化對(duì)應(yīng)用程序界面組件的綜合變化影響難題，提出了基于界面組件依賴矩陣、本體概念依賴矩陣、本體概念-界面組件依賴矩陣及變化影響傳播樹(shù)的變化影響分析和預(yù)測(cè)方法。本文以柑橘肥水智能決策支持系統(tǒng)為案例研究對(duì)象，驗(yàn)證了所提出的系統(tǒng)變化預(yù)測(cè)方法的有效性，主要有以下結(jié)論：

1）對(duì)柑橘肥水本體的22個(gè)本體概念，6個(gè)界面組件，在不需要查看系統(tǒng)源代碼的情況下，僅根據(jù)概念層的直觀依賴關(guān)系，能夠達(dá)到85%的平均準(zhǔn)確率和98%的平均召回率，證明了所提方法的有效性。

2）在軟件系統(tǒng)變化管理領(lǐng)域，該方法的準(zhǔn)確率和召回率能夠滿足應(yīng)用需求，可大幅度降低本體變化影響分析的人工成本，提高智能決策支持系統(tǒng)的管理效率。試驗(yàn)結(jié)果說(shuō)明，當(dāng)期望較高準(zhǔn)確率時(shí)，應(yīng)設(shè)定較大的變化預(yù)測(cè)閾值；當(dāng)期望較高召回率時(shí)，應(yīng)設(shè)定較小的變化預(yù)測(cè)閾值。

3）本文針對(duì)基于語(yǔ)義本體的柑橘肥水智能決策支持系統(tǒng)所提出的變化預(yù)測(cè)方法能夠應(yīng)用到其他智能信息系統(tǒng)，可用于預(yù)測(cè)本體元素的變化對(duì)系統(tǒng)其他組件的影響情況。

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Change prediction approach and application effect for citrus fertilization and irrigation intelligent decision support system

Wang Yi, Wang Ying
(School of Computer and Information Science, Southwest University, Chongqing 400715, China)

Agricultural information systems rely heavily on ontologies to realize intelligent and precision agricultural information services such as disease diagnosis and crop planting management. In the development of agricultural applications, due to the massive and cross domain knowledge required in the agricultural domain, it is impossible to develop applications after the completion of domain ontologies. Due to various reasons, ontologies are constantly modified, augmented, or evolved during the application development. Since ontologies are often tightly interwoven with applications, when changes occur in ontologies, the applications such as query services and decision support services that rely on them will be affected in different ways and may not function correctly. Therefore, it is important to provide mechanisms that fill the gap between ontology evolution management and the change management of knowledge based systems. In this paper, we proposed an approach to analyze and predict change impacts on user interface components when the underlying ontology is changed of its concepts. Our approach avoided the hard and error-prone task to analyze change impacts at the lower level, i.e., source code level. Instead, in our method, the change impact prediction was accomplished at the higher conceptual level. Specifically, we focused on the problem that when ontology concepts were changed, how to determine the affected user interface components of applications without diving into the source codes of the system. Our approach was based on constructing three matrices: the interface component dependency matrix, the ontology concept dependency matrix, and the ontology concept-user interface component correlation matrix, at the conceptual level. The interface component dependency matrix specified the direct reliance between interface components based on the shared interface variables of interface components. The ontology concept dependency matrix described the direct relationships between ontology concepts derived from domain ontology. The ontology concept-user interface component correlation matrix specified the direct dependencies between concepts and interface components. With the three matrices, we provided an algorithm to create the change impact propagation tree for each involved ontology concept. By treating the change impact propagation tree as a logical tree, we were able to calculate the change impact prediction probabilities for each concept and interface component. By setting appropriate prediction thresholds, we can obtain the predicted change impact results. To evaluate our approach of change prediction for interface components relating to ontology concepts, we applied the proposed method to the citrus fertilization and irrigation intelligent decision support system. The citrus decision support system was supported by a citrus fertilization and irrigation ontology, which contained 22 domain concepts. The decision support system had six user interface components. For each of the 22 concepts, we calculated the change impact probabilities for each of the six interface components by the change impact propagation trees. In addition, we obtained the actual data by analyzing the Java source codes of the citrus decision support systems. In order to compare the experiment data with the actual data, we set two empirical prediction thresholds, 5% and 10%, based on the existing related studies for filtering the experiment data. We applied two traditional statistic indicators, precision and recall, to evaluate the results. The final evaluation results showed that given the prediction threshold of 5%, the average precision of change impact prediction for the 22 concepts was 77% and the average recall was 98%. Given the threshold of 10%, the average precision of change impact prediction for the 22 concepts reached 85% and the average recall was 74%. There was a tradeoff between precision and recall, i.e., a higher precision indicated a lower recall. In our cases, the precision and recall rates for the both thresholds indicated satisfied results for our proposed change impact prediction approach. The proposed approach provides a feasible and effective solution to the challenging task of change management problem in agricultural information systems based on ontologies.

systems analysis; software architecture; prediction; semantic ontology; ontology change management; citrus fertilization and irrigation decision support system; software change management

10.11975/j.issn.1002-6819.2017.16.023

S126

A

1002-6819(2017)-16-0174-08

王 藝，王 英. 柑橘肥水智能決策支持系統(tǒng)的變化預(yù)測(cè)方法及應(yīng)用效果[J]. 農(nóng)業(yè)工程學(xué)報(bào)，2017，33(16)：174-181.

10.11975/j.issn.1002-6819.2017.16.023 http://www.tcsae.org

Wang Yi, Wang Ying. Change prediction approach and application effect for citrus fertilization and irrigation intelligent decision support system[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(16): 174-181. (in Chinese with English abstract) doi：10.11975/j.issn.1002-6819.2017.16.023 http://www.tcsae.org

2017-03-08

2017-06-30

國(guó)家自然科學(xué)基金（61303229）；第47批留學(xué)回國(guó)人員科研啟動(dòng)基金；西南大學(xué)基本科研業(yè)務(wù)費(fèi)專項(xiàng)（XDJK2016C040）

王 藝，女，重慶人，副教授，博士，主要從事語(yǔ)義網(wǎng)應(yīng)用、服務(wù)計(jì)算及工作流變化管理研究。重慶 西南大學(xué)計(jì)算機(jī)與信息科學(xué)學(xué)院，400715。Email：echowang@swu.edu.cn