發(fā)布時(shí)間：2018-09-05 07:08

【摘要】：隨著信息技術(shù)的不斷發(fā)展,數(shù)據(jù)信息逐步呈現(xiàn)海量、多樣、非結(jié)構(gòu)化的特點(diǎn)。而傳統(tǒng)的數(shù)據(jù)庫(kù)技術(shù)已不能夠?qū)@些復(fù)雜數(shù)據(jù)進(jìn)行有效的管理,新的數(shù)據(jù)管理模式——數(shù)據(jù)空間應(yīng)運(yùn)而生,其不但可以支持文檔、Web等多種不同的異構(gòu)數(shù)據(jù)源,而且具有集成演化的特性,強(qiáng)調(diào)數(shù)據(jù)之間的關(guān)聯(lián)性及演化性。而專(zhuān)利文獻(xiàn)中含有豐富的結(jié)構(gòu)化信息及非結(jié)構(gòu)化信息,本文選取海量專(zhuān)利數(shù)據(jù)進(jìn)行分析,挖掘?qū)＠g潛在的技術(shù)關(guān)聯(lián)關(guān)系并以此發(fā)現(xiàn)新穎專(zhuān)利。由于專(zhuān)利文獻(xiàn)中引文的缺失以及作者引用動(dòng)機(jī)難以判斷,因此,不能直接使用引用關(guān)系作為專(zhuān)利技術(shù)關(guān)聯(lián)的評(píng)價(jià)指標(biāo)。針對(duì)這一問(wèn)題,本文構(gòu)建了專(zhuān)利間綜合語(yǔ)義相似度模型,用以評(píng)估專(zhuān)利間的技術(shù)關(guān)聯(lián)。首先,根據(jù)專(zhuān)利文獻(xiàn)中包含的專(zhuān)利作者、IPC專(zhuān)利分類(lèi)號(hào)等結(jié)構(gòu)化信息分別構(gòu)建了專(zhuān)利作者相同關(guān)系矩陣WA和基于IPC專(zhuān)利分類(lèi)號(hào)共類(lèi)關(guān)系矩陣WC;然后,針對(duì)專(zhuān)利標(biāo)題、摘要、權(quán)利說(shuō)明書(shū)等文本信息構(gòu)建專(zhuān)利文本相似度矩陣Ws,最后,進(jìn)行多維融合構(gòu)建綜合語(yǔ)義相似度模型。接下來(lái),引入時(shí)序因素并結(jié)合專(zhuān)利間綜合語(yǔ)義相似度模型構(gòu)建專(zhuān)利世系關(guān)聯(lián)網(wǎng)絡(luò),根據(jù)專(zhuān)利數(shù)據(jù)世系分析相關(guān)技術(shù)的演化路徑,以此來(lái)對(duì)專(zhuān)利價(jià)值進(jìn)行評(píng)估,并挖掘新穎專(zhuān)利。首先利用專(zhuān)利世系關(guān)聯(lián)網(wǎng)絡(luò)中專(zhuān)利間潛在的直接或間接被引關(guān)系,綜合考量專(zhuān)利價(jià)值隨時(shí)間指數(shù)衰減因素及潛在的直接或間接被引的專(zhuān)利對(duì)專(zhuān)利價(jià)值的貢獻(xiàn)度,提出專(zhuān)利價(jià)值評(píng)估算法;由于新加入的專(zhuān)利對(duì)原有專(zhuān)利世系關(guān)聯(lián)網(wǎng)絡(luò)中的專(zhuān)利的價(jià)值影響,為節(jié)省大量重復(fù)計(jì)算的時(shí)間,最后提出專(zhuān)利價(jià)值動(dòng)態(tài)更新算法,當(dāng)在T+1時(shí)刻新加入的專(zhuān)利與原有T時(shí)刻的專(zhuān)利存在潛在技術(shù)關(guān)聯(lián)時(shí),其價(jià)值為所有的鄰接點(diǎn)的價(jià)值傳遞度之和,從而提高算法的計(jì)算效率。最后,使用專(zhuān)利數(shù)據(jù)集進(jìn)行相關(guān)實(shí)驗(yàn),經(jīng)實(shí)驗(yàn)結(jié)果對(duì)比分析驗(yàn)證了專(zhuān)利綜合語(yǔ)義相似度模型的準(zhǔn)確性以及專(zhuān)利價(jià)值動(dòng)態(tài)更新算法的高效性。
[Abstract]:With the continuous development of information technology, data information gradually presents the characteristics of mass, diversity, unstructured. However, the traditional database technology can not manage these complex data effectively, and a new data management model, data space, emerges as the times require, which can not only support many different heterogeneous data sources, such as document and Web, etc. Moreover, it has the characteristics of integration and evolution, emphasizing the relevance and evolution of data. The patent literature contains abundant structured information and unstructured information. This paper selects massive patent data to analyze the potential technological relationships between patents and find new patents. Due to the lack of citation in patent literature and the difficulty in judging the author's citation motivation, the citation relation cannot be directly used as the evaluation index of patent technology relevance. To solve this problem, a comprehensive semantic similarity model between patents is constructed to evaluate the technical association between patents. First of all, according to the structured information of patent author WA and WC; based on IPC patent classification number, the same relationship matrix WA and WC; are constructed respectively. The patent text similarity matrix (Ws,) is constructed with the text information such as the specification. Finally, the comprehensive semantic similarity model is constructed by multi-dimensional fusion. Then, the temporal factors are introduced and combined with the comprehensive semantic similarity model among patents to construct the patent lineage correlation network. According to the patent data lineage, the evolution path of the related technology is analyzed to evaluate the patent value and explore novel patents. Firstly, by using the potential direct or indirect citation relationship between patents in the related network of patent lineages, the factors of exponential decay of patent value over time and the contribution of potential direct or indirect cited patents to patent value are considered synthetically. Due to the influence of the new patent on the value of the patent in the original patent-related network, in order to save a lot of time of repeated calculation, a dynamic updating algorithm of patent value is put forward. When there is a potential technical correlation between the newly added patent and the original patent at T1, the value of the patent is the sum of the value transfer degrees of all adjacent points, thus improving the computational efficiency of the algorithm. Finally, the patent data set is used to carry on the related experiments, and the accuracy of the patent synthesis semantic similarity model and the efficiency of the patent value dynamic updating algorithm are verified by the comparison and analysis of the experimental results.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP391.1

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