【摘要】：在當(dāng)下這個(gè)互聯(lián)網(wǎng)技術(shù)飛速發(fā)展的時(shí)代,文檔萬維網(wǎng)己經(jīng)轉(zhuǎn)變成為語(yǔ)義網(wǎng),語(yǔ)義網(wǎng)應(yīng)用在各行各業(yè)的各個(gè)領(lǐng)域,而知識(shí)圖譜是語(yǔ)義網(wǎng)的最為直觀有效的表示,這也使得知識(shí)圖譜的構(gòu)建成為了當(dāng)下的研究熱點(diǎn)。尤其對(duì)于特定領(lǐng)域而言,實(shí)現(xiàn)個(gè)性化服務(wù)更加需要知識(shí)圖譜來作為其堅(jiān)定的基礎(chǔ),因此,近年來知識(shí)圖譜在推薦系統(tǒng)的應(yīng)用也成為了研究的重點(diǎn)。本文針對(duì)特定領(lǐng)域的知識(shí)圖譜在構(gòu)建過程中實(shí)體消歧環(huán)節(jié)和在領(lǐng)域知識(shí)圖譜在信息推薦方面的應(yīng)用,主要做了以下幾個(gè)方面的研究工作。1.結(jié)合詞向量和圖模型的方法來實(shí)現(xiàn)實(shí)體消歧。針對(duì)特定領(lǐng)域知識(shí)圖譜的構(gòu)建提出了一種結(jié)合詞向量和圖模型的實(shí)體消歧方法。通過詞向量計(jì)算工具Word2Vec構(gòu)建詞向量模型,結(jié)合人工標(biāo)注的實(shí)體關(guān)系圖譜,采用一種基于圖的隨機(jī)游走算法輔助計(jì)算相似度,使其能夠較準(zhǔn)確地計(jì)算旅游領(lǐng)域詞與詞之間的相似度。最后,提取待消歧實(shí)體的背景文本的若干關(guān)鍵詞和知識(shí)庫(kù)中候選實(shí)體文本的若干關(guān)鍵詞,利用訓(xùn)練好的詞向量模型結(jié)合圖模型分別進(jìn)行交叉相似度計(jì)算,把相似度均值最高的候選實(shí)體作為最終的目標(biāo)實(shí)體。實(shí)驗(yàn)結(jié)果表明,這種新的相似度計(jì)算方法能夠有效獲取實(shí)體指稱項(xiàng)與目標(biāo)實(shí)體之間的相似度,從而能夠較為準(zhǔn)確地實(shí)現(xiàn)特定領(lǐng)域的實(shí)體消歧。2.基于屬性圖聚類的旅游領(lǐng)域個(gè)性化信息推薦。在前面的基礎(chǔ)上構(gòu)建領(lǐng)域?qū)嶓w的屬性圖,然后利用屬性圖聚類的方法進(jìn)行用戶的偏好發(fā)現(xiàn),接著將領(lǐng)域?qū)嶓w劃分成不同的旅游實(shí)體類別,再將用戶的偏好信息結(jié)合領(lǐng)域?qū)嶓w的實(shí)體類別進(jìn)行領(lǐng)域?qū)嶓w的屬性圖聚類,從而針對(duì)不同的用戶做出相應(yīng)的推薦,最后對(duì)這種基于屬性圖聚類的聚類推薦模型進(jìn)行實(shí)驗(yàn)分析。3.旅游領(lǐng)域個(gè)性化信息推薦原型系統(tǒng)實(shí)現(xiàn)。本文將領(lǐng)域信息推薦算法用程序?qū)崿F(xiàn),通過抓取用戶搜索的關(guān)鍵詞來作為系統(tǒng)的輸入,然后結(jié)合屬性圖聚類模型進(jìn)行計(jì)算,最終將符合用戶興趣愛好的旅游景點(diǎn)展現(xiàn)給用戶,實(shí)現(xiàn)領(lǐng)域知識(shí)的個(gè)性化推薦。
[Abstract]:In the current era of rapid development of Internet technology, the document World wide Web has been transformed into a semantic Web, and the semantic Web is applied in various fields in various industries, and the knowledge graph is the most intuitive and effective representation of the semantic Web. This also makes the construction of knowledge graph become a hot research topic at present. Especially for specific fields, the realization of personalized service needs knowledge graph as its firm foundation. Therefore, in recent years, the application of knowledge graph in recommendation system has also become the focus of research. Aiming at the application of entity disambiguation in the construction of domain-specific knowledge graph and the application of domain knowledge graph in information recommendation, this paper has done the following research work. 1. The method of combining word vector and graph model to realize entity disambiguation. In this paper, an entity disambiguation method combining word vector and graph model is proposed for the construction of domain-specific knowledge graph. The word vector model is constructed by word vector computing tool Word2Vec, and the similarity degree is calculated by a random walk algorithm based on graph, which is combined with the manually labeled entity relation graph. So that it can calculate the similarity between words in tourism domain more accurately. Finally, several keywords of the background text of the entity to be disambiguated and some keywords of the candidate entity text in the knowledge base are extracted, and the cross-similarity degree is calculated by using the trained word vector model combined with the graph model. The candidate entity with the highest similarity mean is regarded as the final target entity. The experimental results show that the new similarity calculation method can effectively obtain the similarity between the entity reference term and the target entity, so that the entity disambiguation in a specific field can be realized more accurately. Personalized information recommendation in tourism field based on attribute graph clustering. On the basis of the above, we construct the attribute graph of domain entity, then use the method of attribute graph clustering to discover the user's preference, and then divide the domain entity into different categories of tourism entities. Then the user's preference information is combined with the entity category of the domain entity to cluster the domain entity's attribute map, so as to make the corresponding recommendation for different users. Finally, this clustering recommendation model based on attribute graph clustering is experimentally analyzed. 3. Implementation of personalized information recommendation prototype system in tourism field. In this paper, the domain information recommendation algorithm is implemented by program, by grabbing the keywords of user search as the input of the system, and then combining the attribute graph clustering model to calculate, finally, the tourist attractions that accord with the user's interests will be displayed to the user. Implement personalized recommendation of domain knowledge.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張劍;屈丹;李真;;基于詞向量特征的循環(huán)神經(jīng)網(wǎng)絡(luò)語(yǔ)言模型[J];模式識(shí)別與人工智能;2015年04期

2 懷寶興;寶騰飛;祝恒書;劉淇;;一種基于概率主題模型的命名實(shí)體鏈接方法[J];軟件學(xué)報(bào);2014年09期

3 寧博;張菲菲;;基于異構(gòu)知識(shí)庫(kù)的命名實(shí)體消歧[J];西安郵電大學(xué)學(xué)報(bào);2014年04期

4 朱敏;賈真;左玲;吳安峻;陳方正;柏玉;;中文微博實(shí)體鏈接研究[J];北京大學(xué)學(xué)報(bào)(自然科學(xué)版);2014年01期

5 趙軍;劉康;周光有;蔡黎;;開放式文本信息抽取[J];中文信息學(xué)報(bào);2011年06期

6 東興;賈宇波;王義;范紅丹;;一種改進(jìn)的向量空間信息檢索模型研究[J];工業(yè)控制計(jì)算機(jī);2011年05期

7 鄭偉;王朝坤;劉璋;王建民;;一種基于隨機(jī)游走模型的多標(biāo)簽分類算法[J];計(jì)算機(jī)學(xué)報(bào);2010年08期

8 趙軍;;命名實(shí)體識(shí)別、排歧和跨語(yǔ)言關(guān)聯(lián)[J];中文信息學(xué)報(bào);2009年02期

9 孫吉貴;朱興軍;張永剛;李瑩;;一種基于預(yù)處理技術(shù)的約束滿足問題求解算法[J];計(jì)算機(jī)學(xué)報(bào);2008年06期

10 賀玲;吳玲達(dá);蔡益朝;;數(shù)據(jù)挖掘中的聚類算法綜述[J];計(jì)算機(jī)應(yīng)用研究;2007年01期

相關(guān)碩士學(xué)位論文 前1條

1 李宏博;約束滿足問題研究及其在配置問題中的應(yīng)用[D];吉林大學(xué);2010年

，

本文編號(hào)：2468768