關(guān)鍵詞查詢的推薦技術(shù)研究
發(fā)布時間：2019-01-30 08:55
【摘要】：關(guān)鍵詞查詢是文本數(shù)據(jù)(如萬維網(wǎng)等)的經(jīng)典查詢方式,它只需用戶輸入簡單的關(guān)鍵詞即可得到結(jié)果,省去了學(xué)習查詢語言和了解底層數(shù)據(jù)的負擔。因其良好的易用性,關(guān)鍵詞查詢在結(jié)構(gòu)化數(shù)據(jù)(如關(guān)系數(shù)據(jù)庫和深度萬維網(wǎng)數(shù)據(jù)庫)上同樣得到了廣泛的應(yīng)用。然而,隨著底層數(shù)據(jù)越來越復(fù)雜,這種簡單易用的查詢方式在表達能力方面的局限性日益暴露：一方面,語義模糊或表述不準確的關(guān)鍵詞查詢難以檢索到高質(zhì)量的結(jié)果；另一方面,無結(jié)構(gòu)的關(guān)鍵詞難以描述結(jié)構(gòu)化的查詢需求。針對這些問題,論文提出了文本數(shù)據(jù)和結(jié)構(gòu)化數(shù)據(jù)上的查詢推薦技術(shù),在保證易用性的前提下,輔助用戶準確地表達查詢意圖。論文的主要研究工作和貢獻包括： 1.文本數(shù)據(jù)上主題相關(guān)的查詢詞推薦：為了輔助用戶生成高質(zhì)量的關(guān)鍵詞查詢,論文提出了一種主題相關(guān)的查詢詞推薦方法。該方法考慮用戶輸入的部分查詢,分析隱含的查詢主題,推薦與主題相關(guān)的關(guān)鍵詞,從而輔助用戶生成高質(zhì)量的完整查詢,準確地表達查詢意圖。此外,查詢詞推薦支持實時的自動補全,隨著用戶逐字母地敲入查詢詞的前綴,可以實時地進行響應(yīng),推薦包含該前綴的查詢詞,從而方便用戶對查詢詞進行快速地選擇或修改,提高了查詢的效率。 2.傳統(tǒng)關(guān)系數(shù)據(jù)上交互式SQL查詢語句推薦：針對傳統(tǒng)關(guān)鍵詞查詢難以準確表達關(guān)系數(shù)據(jù)庫結(jié)構(gòu)化查詢需求的問題,論文提出了一種交互式的SQL查詢語句推薦方法,根據(jù)用戶輸入的關(guān)鍵詞實時地推薦相關(guān)的SQL語句,并按照相關(guān)性對SQL語句進行排序。該方法提出了有效的推薦模型度量關(guān)鍵詞與SQL語句之間的相關(guān)性,設(shè)計了快速的算法支持SQL語句的實時推薦,從而在減輕查詢負擔的同時,輔助用戶準確地表達結(jié)構(gòu)化數(shù)據(jù)上的查詢意圖,有效地解決了無結(jié)構(gòu)的關(guān)鍵詞查詢與結(jié)構(gòu)化數(shù)據(jù)之間的信息鴻溝問題,提供了一種既好用又有很強表達能力的關(guān)系數(shù)據(jù)庫查詢方式。 3.深度萬維網(wǎng)數(shù)據(jù)庫查詢推薦：在深度萬維網(wǎng)數(shù)據(jù)庫訪問受限的前提下,論文提出了一種基于關(guān)鍵詞的深度萬維網(wǎng)數(shù)據(jù)庫查詢推薦方法。該方法通過查詢?nèi)罩就诰蚝蛿?shù)據(jù)庫采樣技術(shù),在訪問受限情況下分析用戶的查詢意圖,將關(guān)鍵詞映射為數(shù)據(jù)庫上的結(jié)構(gòu)化表單查詢并在線地獲取相關(guān)的結(jié)果,從而為深度萬維網(wǎng)查詢與現(xiàn)有搜索引擎的無縫集成提供了一種有效的手段。
[Abstract]:Keyword query is a classical query method for text data (such as the World wide Web). It only needs users to input simple keywords to get the results, which saves the burden of learning query language and understanding the underlying data. Because of its good usability, keyword query is also widely used in structured data (such as relational database and deep Web database). However, as the underlying data become more and more complex, the limitations of this simple and easy-to-use query in terms of expressive ability are increasingly exposed: on the one hand, it is difficult to retrieve high quality results for keyword queries with fuzzy semantics or inaccurate representation; On the other hand, unstructured keywords are difficult to describe structured query requirements. Aiming at these problems, this paper proposes query recommendation technology on text data and structured data, which can help users express their query intention accurately on the premise of ease of use. The main research work and contributions are as follows: 1. Topic-related query word recommendation on text data: in order to assist users to generate high-quality keyword queries, a topic-related query recommendation method is proposed in this paper. This method takes into account some queries input by users, analyzes the implicit query topics, and recommends the keywords related to the topic, so as to assist users to generate high quality complete queries and accurately express the query intention. In addition, the query word recommendation supports real-time automatic completion. As the user types the prefix of the query word alphabetically, it can respond in real time, and the query word containing the prefix is recommended. Therefore, it is convenient for users to select or modify query terms quickly and improve the efficiency of query. 2. Recommendation of interactive SQL query statement on traditional relational data: aiming at the problem that traditional keyword query can not accurately express the requirement of structured query in relational database, an interactive SQL query statement recommendation method is proposed in this paper. The relevant SQL statements are recommended in real time according to the keywords entered by the user, and the SQL statements are sorted according to the correlation. This method proposes an effective recommendation model to measure the correlation between keywords and SQL statements, and designs a fast algorithm to support the real-time recommendation of SQL statements, so as to reduce the query burden at the same time. In order to solve the problem of information gap between unstructured keyword query and structured data, the user can express the query intention of structured data accurately, and solve the problem of information gap between unstructured keyword query and structured data. This paper provides a query method of relational database which is easy to use and has strong expressive ability. 3. Deep Web Database query recommendation: under the premise of restricted access to Deep World wide Web database, this paper proposes a keyword based deep Web database query recommendation method. In this method, query log mining and database sampling technology are used to analyze the user's query intention in the case of restricted access, and map the keywords to the structured form query on the database and obtain the related results online. It provides an effective method for the seamless integration of deep web query and existing search engines.
【學(xué)位授予單位】：清華大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2012
【分類號】：TP391.3
本文編號：2417966
[Abstract]:Keyword query is a classical query method for text data (such as the World wide Web). It only needs users to input simple keywords to get the results, which saves the burden of learning query language and understanding the underlying data. Because of its good usability, keyword query is also widely used in structured data (such as relational database and deep Web database). However, as the underlying data become more and more complex, the limitations of this simple and easy-to-use query in terms of expressive ability are increasingly exposed: on the one hand, it is difficult to retrieve high quality results for keyword queries with fuzzy semantics or inaccurate representation; On the other hand, unstructured keywords are difficult to describe structured query requirements. Aiming at these problems, this paper proposes query recommendation technology on text data and structured data, which can help users express their query intention accurately on the premise of ease of use. The main research work and contributions are as follows: 1. Topic-related query word recommendation on text data: in order to assist users to generate high-quality keyword queries, a topic-related query recommendation method is proposed in this paper. This method takes into account some queries input by users, analyzes the implicit query topics, and recommends the keywords related to the topic, so as to assist users to generate high quality complete queries and accurately express the query intention. In addition, the query word recommendation supports real-time automatic completion. As the user types the prefix of the query word alphabetically, it can respond in real time, and the query word containing the prefix is recommended. Therefore, it is convenient for users to select or modify query terms quickly and improve the efficiency of query. 2. Recommendation of interactive SQL query statement on traditional relational data: aiming at the problem that traditional keyword query can not accurately express the requirement of structured query in relational database, an interactive SQL query statement recommendation method is proposed in this paper. The relevant SQL statements are recommended in real time according to the keywords entered by the user, and the SQL statements are sorted according to the correlation. This method proposes an effective recommendation model to measure the correlation between keywords and SQL statements, and designs a fast algorithm to support the real-time recommendation of SQL statements, so as to reduce the query burden at the same time. In order to solve the problem of information gap between unstructured keyword query and structured data, the user can express the query intention of structured data accurately, and solve the problem of information gap between unstructured keyword query and structured data. This paper provides a query method of relational database which is easy to use and has strong expressive ability. 3. Deep Web Database query recommendation: under the premise of restricted access to Deep World wide Web database, this paper proposes a keyword based deep Web database query recommendation method. In this method, query log mining and database sampling technology are used to analyze the user's query intention in the case of restricted access, and map the keywords to the structured form query on the database and obtain the related results online. It provides an effective method for the seamless integration of deep web query and existing search engines.
【學(xué)位授予單位】：清華大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2012
【分類號】：TP391.3
【參考文獻】
相關(guān)期刊論文 前2條
1 劉玉奎;周立柱;范舉;;中文深度萬維網(wǎng)數(shù)據(jù)庫的現(xiàn)狀研究[J];計算機學(xué)報;2011年02期
2 范舉;周立柱;;基于關(guān)鍵詞的深度萬維網(wǎng)數(shù)據(jù)庫選擇[J];計算機學(xué)報;2011年10期
，本文編號：2417966
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