發(fā)布時(shí)間：2018-01-25 01:07

  本文關(guān)鍵詞： 信息檢索模型 概念圖 需求分析 相似度計(jì)算　出處：《上海交通大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：信息檢索模型的研究和發(fā)展已經(jīng)歷經(jīng)70余載，在過(guò)去相當(dāng)一段時(shí)間里，信息檢索還只限于專業(yè)人員使用，模型的實(shí)現(xiàn)原理也比較簡(jiǎn)單，人們獲取信息的途徑并不依賴于信息檢索，因此用戶對(duì)于信息檢索的需求還不是十分迫切。隨著互聯(lián)網(wǎng)的興起，人們信息檢索的需求也逐漸擴(kuò)大。在紛亂龐雜的信息海洋中，如何準(zhǔn)確地獲取滿足需求的信息也成為信息檢索研究中一項(xiàng)刻不容緩的工作。當(dāng)前大多搜索引擎在提供搜索服務(wù)的時(shí)候總有一些方面不如人意，究其原因，是因?yàn)樗鼈儼延脩舻男枨蠛?jiǎn)單地拆分成了若干個(gè)毫無(wú)關(guān)系的關(guān)鍵詞，而沒(méi)有把需求當(dāng)作一個(gè)概念整體來(lái)看待，于是就丟失了關(guān)鍵詞之間存在的語(yǔ)義信息。 本文首先從概念圖的相關(guān)理論研究入手，強(qiáng)調(diào)了概念分析在表征用戶需求意圖上的重要性�；诟拍顖D的檢索模型，通過(guò)需求概念圖的標(biāo)引來(lái)保持用戶需求的概念內(nèi)涵，在檢索時(shí)融入概念圖匹配和語(yǔ)義相似度計(jì)算的方法，從而提升檢索的準(zhǔn)確率。本文客觀地分析了實(shí)現(xiàn)該檢索模型的重重困難，同時(shí)重新考量了各項(xiàng)相關(guān)技術(shù)，創(chuàng)新性地提出了需求概念圖導(dǎo)引下的檢索模型。 圍繞這個(gè)模型，本文先著重討論了需求概念圖標(biāo)引的方法，分析用戶需求的相關(guān)特點(diǎn)，并結(jié)合詞匯知識(shí)獲取等方法，探討E-A-V形式的概念圖自動(dòng)標(biāo)引。其次，我們又詳細(xì)介紹了相關(guān)語(yǔ)義相似度計(jì)算以及概念圖相似度計(jì)算的方法，，對(duì)比了各自的優(yōu)劣。最后是檢索模型實(shí)現(xiàn)的各種細(xì)節(jié)，主要介紹了概念圖實(shí)際應(yīng)用時(shí)的實(shí)踐經(jīng)驗(yàn)，為今后概念圖完全應(yīng)用于信息檢索，真正實(shí)現(xiàn)語(yǔ)義搜索提供一些有益的思路。
[Abstract]:The research and development of information retrieval model has gone through more than 70 years, in the past quite a period of time, information retrieval is only limited to the use of professionals, the implementation principle of the model is also relatively simple. People's access to information does not depend on information retrieval, so users' demand for information retrieval is not very urgent. With the rise of the Internet. People's demand for information retrieval is also gradually expanding. In the chaotic sea of information. How to accurately obtain the information to meet the needs has become an urgent task in the information retrieval research. At present, most search engines are always unsatisfactory in some aspects when providing search services, which is the reason. Because they simply split the user's requirements into several unrelated keywords and did not treat the requirements as a whole, they lost the semantic information that exists between keywords. This paper starts with the related theoretical research of concept map, and emphasizes the importance of concept analysis in representing the intention of user demand. The retrieval model based on concept graph is proposed in this paper. Through the indexing of the requirement concept map to keep the concept connotation of the user requirement, the method of concept map matching and semantic similarity calculation is incorporated in the retrieval. In order to improve the accuracy of retrieval, this paper objectively analyzes the difficulties of implementing the retrieval model, reconsiders the relevant technologies, and creatively puts forward the retrieval model guided by the requirement concept map. Around this model, this paper first discusses the method of conceptual icon citation of requirements, analyzes the relevant characteristics of user requirements, and combines the methods of acquisition of lexical knowledge. E-A-V form of concept map automatic indexing. Secondly, we also introduce the relevant semantic similarity calculation and concept map similarity calculation methods in detail. Finally, the details of the implementation of the retrieval model are discussed, and the practical experience in the practical application of the concept map is mainly introduced, so that the concept map can be fully applied to information retrieval in the future. The real implementation of semantic search provides some useful ideas.
【學(xué)位授予單位】：上海交通大學(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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本文編號(hào)：1461553