本文選題：知識(shí)圖譜 + 信息檢索��； 參考：《湘潭大學(xué)》2017年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)、移動(dòng)互聯(lián)網(wǎng)的成熟與發(fā)展以及網(wǎng)絡(luò)數(shù)據(jù)的爆炸式增長(zhǎng),如何從海量信息中快速、方便、準(zhǔn)確的獲取需要信息是一個(gè)具有挑戰(zhàn)的問(wèn)題。而目前主流搜索引擎的“查詢-應(yīng)答”式的一次性交互模式難以滿足用戶便捷探索知識(shí)的需求,為達(dá)到探索目的,用戶不得不分析、理解查詢結(jié)果,并修正關(guān)鍵詞再次進(jìn)行查詢。這一過(guò)程效率較低,并且需要用戶自身使用一定的搜索策略,才能達(dá)到預(yù)期的目的,因此導(dǎo)致用戶體驗(yàn)較差。而這些搜索策略由搜索引擎通過(guò)一定算法實(shí)現(xiàn),使其對(duì)于用戶透明。另外,產(chǎn)業(yè)信息化的進(jìn)程使得生物醫(yī)學(xué)領(lǐng)域的各類信息資源都以數(shù)字化存儲(chǔ)形式下來(lái)。其中蘊(yùn)含的大量的信息為人類醫(yī)學(xué)的進(jìn)步提供了助力,但如何從中挖掘出關(guān)鍵信息,便于醫(yī)學(xué)研究者利用好海量信息資源找到感興趣的研究點(diǎn)也是亟待解決的問(wèn)題。醫(yī)學(xué)信息檢索需要有醫(yī)學(xué)背景知識(shí),利用知識(shí)圖譜將專家知識(shí)保留并加以處理,是將領(lǐng)域數(shù)據(jù)用好的一個(gè)途徑。因此,本文針對(duì)上述的一些問(wèn)題,做了如下幾點(diǎn)創(chuàng)新性的工作:(1)為彌補(bǔ)一次性交互模式的不足,我們利用共現(xiàn)關(guān)系構(gòu)建了語(yǔ)義圖譜,將知識(shí)概念通過(guò)語(yǔ)義關(guān)系關(guān)聯(lián)起來(lái),方便用戶快速瀏覽知識(shí)網(wǎng)絡(luò)。另外,我們提出了一種新穎的基于圖譜的挖掘多目標(biāo)關(guān)聯(lián)關(guān)系的探索式搜索算法,通過(guò)扁平化壓縮圖譜和逆扁平化解壓圖譜操作,能快速、有效的從圖中搜索出多個(gè)目標(biāo)之間有較強(qiáng)關(guān)聯(lián)的節(jié)點(diǎn)和路徑,以推測(cè)用戶的搜索意圖。并實(shí)驗(yàn)結(jié)果得到,我們提出的方法挖掘的關(guān)聯(lián)關(guān)系較其他方法更好。(2)將醫(yī)學(xué)文本作為研究對(duì)象,分別基于Medline引文數(shù)據(jù)和CT影像報(bào)告文本從不同關(guān)系粒度上構(gòu)建了知識(shí)圖譜,提出了一種相對(duì)共現(xiàn)關(guān)系具有更細(xì)粒度的基于CRF和規(guī)則推導(dǎo)的知識(shí)圖譜構(gòu)建方法。測(cè)試發(fā)現(xiàn)在不同粒度圖譜中,挖掘出的實(shí)體之間關(guān)聯(lián)關(guān)系在不同應(yīng)用場(chǎng)景下都具有較好的效果。(3)構(gòu)建了醫(yī)療信息的探索式搜索引擎的原型系統(tǒng),我們?cè)谙到y(tǒng)中采用了基于邊的索引機(jī)制,便于關(guān)系集合的運(yùn)算。并提出了一種高可擴(kuò)展性的分布式關(guān)系抽取算法,提高系統(tǒng)計(jì)算吞吐,以適應(yīng)海量數(shù)據(jù)需求。
[Abstract]:With the maturity and development of the Internet, mobile Internet and the explosive growth of network data, how to quickly, conveniently and accurately obtain the information needed from mass information is a challenging problem. At present, the "query-response" mode of the mainstream search engine is difficult to meet the needs of the users to explore knowledge conveniently. In order to achieve the purpose of exploration, the users have to analyze and understand the query results. And correct keywords to query again. This process is inefficient and requires users to use certain search strategies in order to achieve the desired purpose, resulting in poor user experience. These search strategies are implemented by search engines through certain algorithms to make them transparent to users. In addition, the process of industry informatization makes all kinds of information resources in biomedical field in the form of digital storage. The large amount of information contained therein has provided the help for the progress of human medicine, but how to dig out the key information from it and make it easy for medical researchers to make good use of the massive information resources to find interesting research points is also an urgent problem to be solved. Medical information retrieval requires medical background knowledge. Using knowledge map to retain and process expert knowledge is a way to make good use of domain data. Therefore, in order to make up for the deficiency of one-off interaction model, we construct a semantic map by using co-occurrence relation, and associate the concept of knowledge with semantic relation. It is convenient for users to browse the knowledge network quickly. In addition, we propose a novel exploratory search algorithm for mining multi-object association relations based on atlas. By using flat compression map and inverse flat decompression map, we can quickly, The nodes and paths with strong correlation between multiple targets are effectively searched from the graph to speculate the user's search intention. The experimental results show that the association relation of the proposed method is better than other methods. (2) Medical text is taken as the research object, and the knowledge map is constructed based on Medline citation data and CT image report text from different relational granularity, respectively. In this paper, a method of constructing knowledge map based on CRF and rule derivation is proposed, which has a finer granularity than co-occurrence relation. It is found that in different granularity maps, the relationship between the entities mined out has a good effect in different application scenarios.) the prototype system of exploratory search engine for medical information is constructed. We use the edge-based indexing mechanism in the system to facilitate the operation of relational sets. A highly scalable distributed relational extraction algorithm is proposed to improve the throughput of the system to meet the needs of mass data.
【學(xué)位授予單位】：湘潭大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

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本文編號(hào)：1801957