發(fā)布時(shí)間：2018-07-07 10:12

  本文選題：知識(shí)查詢 + 知識(shí)工程　； 參考：《湖北大學(xué)》2013年碩士論文

【摘要】：隨著社會(huì)發(fā)展的深入,人類對信息獲取、知識(shí)查詢的便捷及高效有了更高的需求。如此需求之下便產(chǎn)生了強(qiáng)大的動(dòng)力,促使著廣大的科研工作者在問答系統(tǒng)、自然語言查詢以及搜索引擎等領(lǐng)域進(jìn)行深入的理論研究和技術(shù)研發(fā)。其中,知識(shí)庫系統(tǒng)中基于自然語言的知識(shí)查詢,便是其中非常具有研究價(jià)值的一個(gè)新型領(lǐng)域,知識(shí)庫系統(tǒng)的核心是知識(shí)庫,而知識(shí)庫中,知識(shí)表示和知識(shí)獲取是其研究的核心；而在自然語言處理領(lǐng)域,知識(shí)查詢算法也亦是其核心領(lǐng)域。知識(shí)查詢算法中,最關(guān)鍵的是分詞算法和匹配算法。論文在此背景下進(jìn)行知識(shí)庫系統(tǒng)中基于自然語言的知識(shí)查詢算法研究。 論文研究的理論基礎(chǔ)主要有知識(shí)工程、自然語言處理、關(guān)系模型以及并行計(jì)算等相關(guān)理論。論文的主要?jiǎng)?chuàng)新點(diǎn)有以下幾點(diǎn)： (一)知識(shí)查詢算法中知識(shí)庫部分,對語義網(wǎng)絡(luò)知識(shí)表示方法以及關(guān)系模型的優(yōu)劣點(diǎn)進(jìn)行分析,提出了一種基于關(guān)系模型與語義網(wǎng)絡(luò)相結(jié)合的知識(shí)表示方法,包括嵌套關(guān)系模型和鏈?zhǔn)疥P(guān)系模型兩種邏輯表示。 (二)知識(shí)查詢算法中的智能分詞部分,對詞庫結(jié)構(gòu)進(jìn)行優(yōu)化提出新穎的詞庫索引結(jié)構(gòu)以及對正向最大匹配分詞算法進(jìn)行改進(jìn),介紹了一種改進(jìn)的正向最大匹配分詞算法。 (三)知識(shí)查詢算法中句型模板匹配部分,基于數(shù)據(jù)結(jié)構(gòu)中的樹形結(jié)構(gòu),介紹了一種基于句型解析樹的句型模板存儲(chǔ)結(jié)構(gòu)。 (四)基于句型解析樹的存儲(chǔ)結(jié)構(gòu),提出了句型模板粗匹配算法,其中包括過濾算法和樹匹配算法,并對查詢算法存在的問題提出了優(yōu)化方案。 論文基于上述四點(diǎn),對基于自然語言(漢字文本)的知識(shí)查詢進(jìn)行純理論研究,但論文的研究也暴露一些問題,主要有：第一,論文是基于純漢字文本的自然語言查詢研究,而當(dāng)今的社會(huì)需求往往是基于漢字、數(shù)字、西語等多種文本字符的混合查詢,論文的研究范圍過窄；第二,論文是基于純理論研究,對提出的算法只是通過偽碼(或自然語言)的形式寫出的,并沒有通過程序設(shè)計(jì)實(shí)現(xiàn)算法、實(shí)驗(yàn)的形式對其性能進(jìn)行驗(yàn)證和測試。因此在后續(xù)工作中應(yīng)該加強(qiáng)對對算法的驗(yàn)證以及提出更好的優(yōu)化方案。
[Abstract]:With the development of society, there is a higher demand for the convenience and efficiency of information acquisition and knowledge query. Under such a demand, a powerful motive force is produced, which urges the scientific research workers to carry on the deep theoretical research and the technical research and development in the question and answer system, the natural language inquiry and the search engine and so on. Among them, the knowledge query based on natural language in knowledge base system is a new field of research value. The core of knowledge base system is knowledge base, and knowledge representation and knowledge acquisition are the core of knowledge base research. In the field of natural language processing, knowledge query algorithm is also the core field. Among knowledge query algorithms, word segmentation algorithm and matching algorithm are the most important. In this context, the knowledge query algorithm based on natural language in knowledge base system is studied. The theoretical basis of this paper is knowledge engineering, natural language processing, relational model and parallel computing. The main innovations of this paper are as follows: (1) the knowledge base part of the knowledge query algorithm analyzes the advantages and disadvantages of the semantic network knowledge representation method and the relational model. A knowledge representation method based on the combination of relational model and semantic network is proposed, which includes two logical representations: nested relational model and chain relational model. (2) in the part of intelligent word segmentation in knowledge query algorithm, a novel index structure of lexicon and an improved algorithm for word segmentation with forward maximum matching are proposed, and an improved algorithm for word segmentation with maximum forward matching is introduced. (3) in the part of sentence pattern template matching in knowledge query algorithm, based on the tree structure of data structure, a sentence pattern template storage structure based on sentence pattern parsing tree is introduced. (4) based on the storage structure of sentence pattern parsing tree, the rough matching algorithm of sentence pattern template is proposed, which includes filtering algorithm and tree matching algorithm. Based on the above four points, this paper makes a pure theoretical study on the knowledge query based on natural language (Chinese character text), but the research of this paper also exposes some problems: first, the thesis is based on the natural language query of pure Chinese character text. Nowadays, the social needs are often based on the mixed query of Chinese characters, numbers, Spanish and other text characters. The research scope of this paper is too narrow. Secondly, the thesis is based on pure theory research. The proposed algorithm is only written in the form of pseudo code (or natural language), and the algorithm is not realized by programming. The performance of the algorithm is verified and tested in the form of experiment. Therefore, in the follow-up work, we should strengthen the verification of the algorithm and put forward a better optimization scheme.
【學(xué)位授予單位】：湖北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP391.3

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