本文選題：語義網(wǎng)絡(luò) + 中醫(yī)知識圖譜��； 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：知識圖譜(Knowledge graph)是大數(shù)據(jù)時代進(jìn)行知識管理和應(yīng)用的重要數(shù)據(jù)資源,已經(jīng)成為搜索引擎語義檢索和各領(lǐng)域基于知識的推理和決策的關(guān)鍵技術(shù)基礎(chǔ)。作為語義網(wǎng)絡(luò)的重要成員,知識圖譜使得大規(guī)模知識的存儲更為規(guī)范,應(yīng)用更加高效。知識圖譜中往往包含各類實體及其屬性,以及各種實體之間的語義關(guān)系。知識圖譜的構(gòu)建包括諸多具體技術(shù)環(huán)節(jié),如命名實體獲取、關(guān)系抽取、數(shù)據(jù)融合、知識推理和知識圖譜表示等,而本體是知識圖譜的概念模型表示的主要方法。在Web搜索和通用領(lǐng)域,已經(jīng)形成了多種大規(guī)模的知識圖譜庫,但醫(yī)學(xué)與中醫(yī)領(lǐng)域的知識圖譜的構(gòu)建仍處于起步階段,雖然已有較大規(guī)模的醫(yī)學(xué)本體庫,但專門的醫(yī)學(xué)特別是中醫(yī)知識圖譜庫的構(gòu)建研究仍較少,由此較大程度阻礙了中醫(yī)概念知識的信息應(yīng)用和共享。因此,本文通過整合多種數(shù)據(jù)資源,就以癥、證、病和藥等為主要實體的中醫(yī)健康知識圖譜的構(gòu)建進(jìn)行研究,主要研究內(nèi)容與結(jié)果包括如下兩個方面:(1)面向中醫(yī)領(lǐng)域中主要的概念實體如癥狀、證候、疾病和中藥等的知識圖譜構(gòu)建問題,設(shè)計了相應(yīng)的圖譜模式(Schema),確定了該圖譜的基本類別、類別屬性和語義關(guān)系。在此基礎(chǔ)上,通過處理和整合四種不同的數(shù)據(jù)源(包括百度百科知識庫、脾胃病臨床病例數(shù)據(jù)、病癥分類數(shù)據(jù)和現(xiàn)有西醫(yī)本體),利用信息抽取和相關(guān)性分析進(jìn)行不同數(shù)據(jù)來源的知識抽取,并采用基于屬性向量的實體對齊方法進(jìn)行不同源數(shù)據(jù)的知識融合,形成了包含4類實體(3927種癥狀,2128種疾病,450種證候和572種中藥)和5種語義關(guān)系的中醫(yī)健康知識圖譜。最后,本文通過利用Jena數(shù)據(jù)生成功能,進(jìn)行了知識圖譜OWL表示和數(shù)據(jù)生成。(2)本文還通過Protege本體編輯器對中醫(yī)知識圖譜中的實體及其關(guān)系增加了約束限定,并利用Protege將知識圖譜中部分知識進(jìn)行圖形化展示。最終在形成的知識圖譜基礎(chǔ)上,利用開源工具包Jena以及依據(jù)中醫(yī)診療邏輯設(shè)定的推理規(guī)則進(jìn)行了基于知識圖譜的知識推理示范分析和應(yīng)用,分析結(jié)果表明具有一定的可行性和診療應(yīng)用價值。本文中醫(yī)知識圖譜構(gòu)建研究重點對知識表示和多種數(shù)據(jù)來源的融合進(jìn)行了探索性研究,但在知識推理應(yīng)用和知識學(xué)習(xí)方法方面仍有待進(jìn)一步深入,此方面將在后續(xù)研究中進(jìn)行完善。
[Abstract]:Knowledge graph is an important data resource for knowledge management and application in the big data era. It has become the key technology foundation of search engine semantic retrieval and knowledge-based reasoning and decision-making in various fields. As an important member of semantic network, knowledge map makes large-scale knowledge storage more standardized and more efficient. The knowledge map often contains various entities and their attributes, as well as the semantic relationship between them. The construction of knowledge atlas includes many technical links, such as named entity acquisition, relation extraction, data fusion, knowledge reasoning and knowledge map representation. Ontology is the main method of conceptual model representation of knowledge atlas. In the field of Web search and general use, a variety of large-scale knowledge atlases have been formed, but the construction of knowledge atlas in the field of medicine and traditional Chinese medicine is still in its infancy, although there is already a large scale medical ontology database. However, there are still few researches on the construction of specialized medical knowledge database, especially on TCM knowledge atlas, which hinders the application and sharing of TCM conceptual knowledge to a large extent. Therefore, by integrating a variety of data resources, this paper studies the construction of TCM health knowledge atlas with symptoms, syndromes, diseases and medicines as the main entities. The main research contents and results include the following two aspects: 1) facing the problems of constructing the knowledge map of the main conceptual entities in the field of TCM, such as symptoms, syndromes, diseases and traditional Chinese medicine, etc. The corresponding schemata are designed, and the basic categories, category attributes and semantic relationships of the atlas are determined. On this basis, through processing and integrating four different data sources (including Baidu encyclopedia knowledge base, clinical case data of spleen and stomach disease, Disease classification data and existing western medicine ontology, using information extraction and correlation analysis for different data sources of knowledge extraction, and the use of attribute vector based entity alignment method for the knowledge fusion of different sources of data. A map of TCM health knowledge including 450syndromes and 572 TCM syndromes and 5 semantic relationships was formed. Finally, by using the function of Jena data generation, this paper carries out the knowledge map OWL representation and data generation. (2) in this paper, the entities in TCM knowledge map and their relationships are restricted by the Protege ontology editor. Some knowledge in the knowledge map is graphically displayed by Protege. Finally, on the basis of the knowledge map formed, the demonstration analysis and application of knowledge reasoning based on knowledge map are carried out by using open source toolkit (Jena) and inference rules set according to the logic of TCM diagnosis and treatment. The results show that it is feasible and valuable in diagnosis and treatment. This paper focuses on the integration of knowledge representation and multiple data sources, but the application of knowledge reasoning and knowledge learning methods need to be further deepened. This aspect will be perfected in the follow-up study.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.3

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