【摘要】：產(chǎn)品創(chuàng)新是現(xiàn)代企業(yè)發(fā)展中必不可少的元素,其核心是功能的創(chuàng)新,本質(zhì)是知識(shí)的創(chuàng)新。這個(gè)信息化高速發(fā)展的時(shí)代,人們對(duì)創(chuàng)新知識(shí)量的需求越來越大,那么首要解決的關(guān)鍵問題是尋找到能夠快速且有效獲取大量創(chuàng)新知識(shí)的方法和工具�？茖W(xué)效應(yīng)知識(shí)是前人從大量的科學(xué)原理和專利中總結(jié)出來的,效應(yīng)是發(fā)明問題解決理論(TRIZ)中一種基于知識(shí)的工具。該工具有助于創(chuàng)新設(shè)計(jì)領(lǐng)域中科學(xué)效應(yīng)知識(shí)的表達(dá),效應(yīng)、功能之間的關(guān)系及其所在領(lǐng)域?qū)I(yè)術(shù)語對(duì)創(chuàng)新思維起著重要的作用�？萍嘉墨I(xiàn)具有創(chuàng)造性和實(shí)用性,已逐漸成為獲取知識(shí)的焦點(diǎn),但傳統(tǒng)的信息抽取系統(tǒng)識(shí)別出來的信息有知識(shí)元、概念等,無法形成完整的知識(shí)體系,抽取的知識(shí)不能按一定的語義關(guān)系組織起來,形成完整的知識(shí)鏈存入知識(shí)庫,在創(chuàng)新領(lǐng)域的應(yīng)用性比較差。本文以科技期刊論文摘要為效應(yīng)知識(shí)抽取對(duì)象,提出了基于知網(wǎng)語義相關(guān)度和基于句式模型匹配規(guī)則的科學(xué)效應(yīng)知識(shí)抽取方法。分析科學(xué)效應(yīng)知識(shí)本體庫結(jié)構(gòu),歸納出效應(yīng)知識(shí)的表達(dá)特征,同時(shí)大量分析了期刊論文摘要內(nèi)容的知識(shí)表達(dá)特點(diǎn),采用詞法分析以及詞性標(biāo)注的方法,歸納出科技期刊文獻(xiàn)摘要句式模型規(guī)則,最終從語義的層次上實(shí)現(xiàn)了科學(xué)效應(yīng)知識(shí)的概念、功能、以及語義關(guān)系的抽取,并將所提取的知識(shí)按照三元組RDF本體的形式進(jìn)行表達(dá)。本文構(gòu)建了科學(xué)效應(yīng)知識(shí)本體庫,提出了將抽取到的效應(yīng)知識(shí)與本體庫中知識(shí)結(jié)構(gòu)匹配的填充方法,從而實(shí)現(xiàn)效應(yīng)知識(shí)本體庫的自動(dòng)填充。設(shè)計(jì)了一個(gè)科學(xué)效應(yīng)知識(shí)抽取的系統(tǒng)原型,基本實(shí)現(xiàn)了期刊文檔的相關(guān)知識(shí)抽取。通過對(duì)實(shí)驗(yàn)結(jié)果的分析表明,本方法具有一定的有效性和可用性。
[Abstract]:Product innovation is an essential element in the development of modern enterprises. Its core is the innovation of function and the essence is the innovation of knowledge. With the rapid development of information technology, people need more and more innovative knowledge, so the most important problem is to find the methods and tools to acquire a large amount of innovative knowledge quickly and effectively. Scientific effect knowledge is summarized from a large number of scientific principles and patents. Effect is a knowledge-based tool in the theory of problem solving (TRIZ). This tool is helpful to the expression, effect and function of the scientific effect knowledge in the field of innovative design, and the technical terms in the field play an important role in innovative thinking. Because of its creativity and practicability, scientific and technological documents have gradually become the focus of acquiring knowledge. However, the traditional information extraction system can not form a complete knowledge system because of its knowledge elements and concepts, etc. The extracted knowledge can not be organized according to a certain semantic relationship, forming a complete knowledge chain stored in the knowledge base, and its application in the field of innovation is relatively poor. In this paper, the abstract of scientific journals is taken as the object of knowledge extraction, and a method of knowledge extraction based on semantic relevance of knowledge net and matching rules of sentence model is proposed. This paper analyzes the structure of scientific effect knowledge ontology database, sums up the expression characteristics of effect knowledge, and analyzes the characteristics of knowledge expression of journal paper abstracts, and adopts the method of lexical analysis and part of speech tagging. The abstract sentence model rules of sci-tech journals are summarized. Finally, the concept, function and semantic relation of scientific effect knowledge are extracted from the semantic level, and the extracted knowledge is expressed in the form of triple RDF ontology. In this paper, the scientific effect knowledge ontology database is constructed, and the filling method is proposed to match the extracted effect knowledge with the knowledge structure in the ontology database, so as to realize the automatic filling of the effect knowledge ontology database. A prototype of scientific effect knowledge extraction system is designed, and the related knowledge extraction of journal documents is basically realized. The experimental results show that this method is effective and available.
【學(xué)位授予單位】：河北工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP391.1

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本文編號(hào)：2376946