【正文】 信息 科學(xué)與工程 學(xué)院 專 業(yè)： 計 算機(jī)科學(xué)與 技術(shù) 姓 名： xxx 學(xué) 號： xxx 外文出處： __FengYang,Youquan Application Framework for Network Eduction Resources Library. [J].Tsinghua Science and Technology, 2020,(4):35～ 45. 附 件： ； 。語義網(wǎng) 在機(jī)器理解的基礎(chǔ)上 可以提供 關(guān)更 多的功能 ，執(zhí)行語義層次的計算。網(wǎng)絡(luò)教育資源 起源于電腦輔助教學(xué)系統(tǒng)的發(fā)展 ，其主要特征是，根據(jù)用戶的具體情況 提供適當(dāng)?shù)慕滩模瑩Q句話說，它有個別教學(xué)的功能，此功能 的 實(shí)現(xiàn)是通過 人工智能 的 手段 [4]。 由于本體是用于描述在語義和知識層次的信息系統(tǒng)中 的 概念模型 ，它已 被用于許多領(lǐng)域。這意味著，資源庫 像每個人提供資源，與此同 時，從每個人那里獲取自己本身沒有的資源。該框架結(jié)構(gòu)由三部分組成。為了實(shí)現(xiàn)顯示與邏輯分離 這一目標(biāo) ， JSP 和JavaBean 技術(shù) 已被用于在本層的開發(fā)。它包括網(wǎng)絡(luò)服務(wù) 、 用戶的要求 處理、 語義分析和推理引擎 。學(xué)習(xí)對象屬于不同的本地資源，它們在地理位置上分開。語義擴(kuò)展規(guī)則的定義為： 如果 O 是 Literlas 類型，則 S 是擴(kuò)張中心，查詢的概念 可根據(jù)學(xué)的概念和屬性關(guān)系 獲得。根據(jù)這些語義的相關(guān)性，本體圖匹配的框架，它可以計算這些 相互關(guān)系 設(shè)計。 D、 推理引擎 這是整個框架的關(guān)鍵控制模塊。然后，它可以對運(yùn)行和處理這一模式利用該模型 API 和 Onotlogy 空氣污染指數(shù)， 因此語義層次上的信息檢索可以實(shí)現(xiàn)。 在今后的工作中，我們將沿著三個方向延伸我們當(dāng)前的工作。s query requests are submitted by search engine, semantic analysis ponent obtains the concepts and semantic relations that user needs through the semantic analysis, and then Jena OWL inference engine takes on reasoning on the basis of this concepts and semantic relations, ultimately the query Beans accesses this resource ontology library and returns the query results to the user. C. Data Layer Data Layer is posed of three parts: ontology library, education resource Description Database and MySQL/DB2/oracle Database. As the storage medium of knowledge resource ontology, this layer is responsible for creating and refining a structural description of the OWLbased knowledge ontology, which is the direct source of knowledge retrieval. In addition, in order to achieve knowledge sharing and reuse, it will carry out semantic annotation to the relevant resources in the web page and store them in the ontology library by the data acquisition ponents. Fig 1. Ontologybased Framework for Network Education Resources Library IV. KEY TECHNOLOGIES OF FRAMEWORK In this section the key technologies of this framework are presented. It includes web services, processing of user request, semantic parsing and inference engine. A. Web Services Representation Layer may also be known as the Network Service Layer, its main task is to achieve interaction with the user. Under the case of a variety of the resource building ways, the web service technologies are selected in order to integrate distributed resource library of education effectively and share information between them. On the basis of a variety of heterogeneous platforms, Web Services are available to build a mon platformindependent, languageindependent technical level, which applications of different platform rely on to implement the connection and integration between them. Throughout the resource library, as a service provider, each local resource make their own services and features promulgate to the global system, which UDDI (Universal Description Discovery and Integration) on it. And then the UDDI is responsible for the registration and sending. In this, all the information is send to the entire system through the SOAP (Simple Object Access Protocol). B. Processing of user request In our framework, user takes on resource search by an ordinary bination method that fields are limited or natural statement. Learning objects belong to different local resources, they are geographically separated. Different resource libraries are usually built on different puter platforms, data formats and programming languages, which provide their own mechanisms to query and access resources. In this case, the central server needs a certain mechanism to orient local resource libraries that can provide server, and then user request will be sent to local servers. In the specific system design, we refer to a number of mathematical methods [8]. Figure 2 illustrates how a user request to be processed by these modules. C. Semantic parsing In our framework, semantic parsing is responsible for the transition from the user requests submitted by search engine to concepts of ontology. The conversional process of user semantic to machine presentation need query ontology library, analyze user intents and use Jena Owl inference mechanism to plete. As user query are uncertainty in a certain degree, it is feasible that expand the concept set of queries according to the needs of users. Semantic expansion rule is defined as: If O is Literlas type, then S is the center of expansion, the concepts for a query can be obtained according to the concepts and attribute relations of S. If S does not exist, the concepts are not to be taken. If