臺灣博碩士論文加值系統

Google在2012年提出知識圖譜後，由於知識圖譜在儲存資料型態和擴充上較具有彈性，因此應用面越來越廣。而領域型的知識圖譜相較於通用型的知識圖，在建置時更需要仰賴專家的協助，對於特定領域的發展與以及專業知識提供更深層的理解，因此有許多研究的目標在於如何建置領域型知識圖譜。由於學習程式語言仍是許多學生所需面臨的課題，因此本研究探討如何建構程式語言領域的知識圖譜，希望能夠用來輔助學習程式語言。本研究建置的知識圖譜包含了教材以及程式範例，並繪製其綱要圖和實例圖，表達每個節點之間的關聯性。實作上以Java為例，並設計了一套程式碼轉成知識圖譜的演算法，以便整合程式知識圖譜以及教材知識圖譜，完成Java知識圖譜。最後本研究設計出一個以知識圖譜為基礎的的教學平台，並經過66位學生使用後填寫問卷回饋，結果顯示本學習平台達到感知有用性及感知易用性。本研究的貢獻包括，（一）完成Java的領域知識圖譜。（二）知識圖譜建置的過程、圖譜綱要圖、知識轉換圖譜的原則等，都可以作為建置其他程式語言知識圖譜的參考。（三）將知識圖譜應用在輔助教學平台上，讓使用者在學習時，可以多跟教學平台互動，並且不論是在學習教材還是在練習撰寫程式碼時，都能透過知識圖譜達到更輕鬆學習。

Knowledge Graph was proposed by Google in 2012.Because of their flexibility in storing data types and expansion, knowledge graphs have become more and more widely used. Compared with the general knowledge graph, the domain knowledge graph can gain a deeper understanding of the development and expertise in a specific field, and requires more expert help when building it. Therefore, many studies have explored how to build domain knowledge graph. Learning programming language is still a topic that many students of information science need to face. This study explores how to construct a knowledge graph in the field of programming language, hoping to be used to assist students in learning programming language. The knowledge graph constructed in this study includes textbook and program examples. The schema diagrams and instance diagrams are drawn to express the correlation between each node. In practice, Java is taken as an example, and the algorithm for converting program codes into knowledge graphs is designed to integrate the program knowledge graphs and textbook knowledge graphs to complete the Java knowledge graph. Finally, this study designed a teaching platform based on knowledge graphs, and 66 students filled out the questionnaire feedback after using it. The results show that the learning platform has achieved perceived usefulness and perceived ease of use. The contributions of this research include: (1) Completion of the domain knowledge graph of Java. (2) The process of building knowledge graphs, such as the principles of converting knowledge into graphs, schema diagrams, instance diagrams, can be used as a reference for building knowledge graphs in other languages. (3) Apply the knowledge map to the teaching platform to assist students to achieve better learning.

目次
摘要 i
ABSTRACT ii
誌謝 iii
目次 iv
表目次 vi
圖目次 vii
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第二章 文獻探討 4
第一節 知識圖譜 4
第二節 通用型與領域型知識圖譜 5
2-2-1通用型知識圖譜 5
2-2-2領域型知識圖譜 7
第三節 知識圖譜應用 7
第四節 基於知識圖譜的線上教學 8
第三章 研究方法 10
第一節 研究流程 10
第二節 教材知識圖譜 11
第三節 程式知識圖譜 19
第四章 研究結果 24
第一節 Java知識圖譜 24
4.1.1 教材知識圖譜 24
4.1.2 程式知識圖譜 38
第二節 輔助Java學習平台 46
第五章 討論 50
第一節 Java程式知識圖譜 50
第二節 輔助Java學習平台 50
第六章 結論 54
參考文獻 55
表目次
表 1 Java關鍵詞 14
表 2敘述詞節點變數解釋 17
表 3主要程式碼轉換表 18
表 4範例題 19
表 5範例題轉換知識圖譜 22
表 6「tm」節點說明表 24
表 7「cp」節點說明表 25
表 8「ui」節點說明表 26
表 9「imp」節點說明表 27
表 10「kw」節點說明表 28
表 11「ope」節點說明表 30
表 12「pk」節點說明表 32
表 13「met」節點說明表 34
表 14「st」節點說明表 36
表 15「ex」節點說明表 39
表 16「hn」節點說明表 39
表 17「tp」節點說明表 40
表 18「cd」節點說明表 40
表 19「imp」節點說明表 41
表 20「kw」節點說明表 42
表 21「pk」節點說明表 42
表 22「met」節點說明表 43
表 23「st」節點說明表 44
表 24教學平台開發環境表 46
表 25問卷內容 51
表 26敘述統計 52
表 27信度分析表 53
表 28 KMO與Bartlett球型檢定分析表 53
表 29因素總解釋變異量結果 53
圖目次
圖 1 Google搜尋後右邊呈現圖 4
圖 2國立臺中科技大學知識圖譜 5
圖 3程式設計知識圖譜流程圖 10
圖 4教材知識圖譜流程圖 10
圖 5程式知識圖譜流程圖 11
圖 6教材知識圖譜綱要圖 11
圖 7「tm」與「cp」節點 12
圖 8章節實例圖 12
圖 9「cp」與「ui」節點 13
圖 10單元實例圖 13
圖 11「ui」與「imp」節點 14
圖 12「ui」與「kw」節點 15
圖 13「ui」與「ope」節點 15
圖 14「ui」與「pk」節點 16
圖 15「ui」、「pk」與「met」節點 16
圖 16「ui」與「st」節點 18
圖 17「ui」與其他節點 18
圖 18程式設計領域知識圖譜綱要圖 19
圖 19「tm」與「cp」節點 25
圖 20「cp」與「ui」節點 27
圖 21「ui」與「imp」節點 28
圖 22「ui」與「kw」節點 30
圖 23「ui」與「ope」節點 32
圖 24「ui」與「pk」節點 33
圖 25「pk」與「met」節點 35
圖 26「ui」與「st」節點 38
圖 27教材知識圖譜 38
圖 28「ex」與「hn」節點 39
圖 29「ex」與「tp」節點 40
圖 30「ex」與「cd」節點 41
圖 31「ex」與「imp」節點 41
圖 32「ex」與「kw」節點 42
圖 33「ex」與「pk」節點 43
圖 34「ex」與「met」節點 44
圖 35「ex」與「st」節點 45
圖 36程式碼知識圖譜 45
圖 37Java教學平台首頁畫面（上半部） 46
圖 38教材知識圖譜全螢幕畫面 47
圖 39教學平台首頁畫面（下半部） 47
圖 40知識卡畫面（for迴圈單元） 48
圖 41知識卡畫面（for關鍵詞） 48
圖 42範例程式碼畫面(累加倒數) 49

參考文獻
Ait-Mlouk, A., & Jiang, L. (2020). KBot: a knowledge graph based chatBot for natural language understanding over linked data. IEEE Access, 8, 149220-149230.
Atzeni, P., Bugiotti, F., Cabibbo, L., & Torlone, R. (2020). Data modeling in the NoSQL world. Computer Standards & Interfaces, 67, 103149.
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., & Ives, Z. (2007). Dbpedia: a nucleus for a web of open data. The semantic web, 722-735.
Babić, A., Jakšić, D., & Poščić, P. (2019). Querying data in NoSQL databases. Zbornik Veleučilišta u Rijeci, 7(1), 257-270.
Cai, X., Xie, L., Tian, R., & Cui, Z. (2022). Explicable recommendation based on knowledge graph. Expert Systems with Applications, 200, 117035.
Cao, Z., Qiao, X., Jiang, S., & Zhang, X. (2019). An efficient knowledge-graph-based web service recommendation algorithm. Symmetry, 11(3), 392.
Chen, P., Lu, Y., Zheng, V. W., Chen, X., & Yang, B. (2018). Knowedu: a system to construct knowledge graph for education. IEEE Access, 6, 31553-31563.
Chen, X., Xie, H., Li, Z., & Cheng, G. (2021). Topic analysis and development in knowledge graph research: a bibliometric review on three decades. Neurocomputing, 461, 497-515.
Cheng, Y., Ding, P., Wang, T., Lu, W., & Du, X. (2019). Which category is better: benchmarking relational and graph database management systems. Data Science and Engineering, 4, 309-322.
Chi, Y., Qin, Y., Song, R., & Xu, H. (2018). Knowledge graph in smart education:a case study of entrepreneurship scientific publication management. Sustainability, 10(4), 995.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Duan, C., You, H., Cai, Z., & Zhang, Z. (2020). Research progress of knowledge graph embedding. International Conference on Artificial Intelligence and Security: 6th International Conference, ICAIS 2020, Hohhot, China, July 17–20, 2020, Proceedings, Part I 6 (pp. 184-194).
Färber, M., Bartscherer, F., Menne, C., & Rettinger, A. (2018). Linked data quality of dbpedia, freebase, opencyc, wikidata, and yago. Semantic Web, 9(1), 77-129.
Gong, F., Wang, M., Wang, H., Wang, S., & Liu, M. (2021). SMR: medical knowledge graph embedding for safe medicine recommendation. Big Data Research, 23, 100174.
Holzschuher, F., & Peinl, R. (2016). Querying a graph database–language selection and performance considerations. Journal of Computer and System Sciences, 82(1), 45-68.
Jian, M., Zhang, C., Fu, X., Wu, L., & Wang, Z. (2022). Knowledge-aware multispace embedding learning for personalized recommendation. Sensors, 22(6), 2212.
Jiang, Z., Chi, C., & Zhan, Y. (2021a). Let knowledge make recommendations for you. IEEE Access, 9, 118194-118204.
Jiang, Z., Chi, C., & Zhan, Y. (2021b). Research on medical question answering system based on knowledge graph. IEEE Access, 9, 21094-21101.
Küçükkeçeci, C., & Yazıcı, A. (2018). Big data model simulation on a graph database for surveillance in wireless multimedia sensor networks. Big Data Research, 11, 33-43.
Kaiser, H. F. (1974). An index of factorial simplicity. psychometrika, 39(1), 31-36.
Kondylakis, H., Tsirigotakis, D., Fragkiadakis, G., Panteri, E., Papadakis, A., Fragkakis, A., Tzagkarakis, E., Rallis, I., Saridakis, Z., & Trampas, A. (2020). R2D2: A DBpedia chatbot using triple-pattern like queries. Algorithms, 13(9), 217.
Kotiranta, P., Junkkari, M., & Nummenmaa, J. (2022). Performance of graph and relational databases in complex queries. Applied Sciences, 12(13), 6490.
Li, J., Luo, Z., Huang, H., & Ding, Z. (2022). Towards knowledge-based tourism chinese question answering system. Mathematics, 10(4), 664.
Lu, W., & Altenbek, G. (2021). A recommendation algorithm based on fine-grained feature analysis. Expert Systems with Applications, 163, 113759.
Lv, Q., Zhang, Y., Li, Y., & Yu, Y. (2022). Research on a health care personnel training model based on multilayered knowledge mapping for the integration of Nursing courses and examinations. Journal of Healthcare Engineering, 2022.
Pal, D., & Vanijja, V. (2020). Perceived usability evaluation of Microsoft Teams as an online learning platform during COVID-19 using system usability scale and technology acceptance model in India. Children and youth services review, 119, 105535.
Pan, Z., & Chen, H. (2021). Collaborative knowledge-enhanced recommendation with self-supervisions. Mathematics, 9(17), 2129.
Qi, P., Sun, Y., Luo, H., & Guizani, M. (2020). Scratch-dkg: a framework for constructing scratch domain knowledge graph. IEEE Transactions on Emerging Topics in Computing, 10(1), 170-185.
Saad, M., Zhang, Y., Tian, J., & Jia, J. (2023). A graph database for life cycle inventory using Neo4j. Journal of Cleaner Production, 393, 136344.
Schäfer, J., Tang, M., Luu, D., Bergmann, A. K., & Wiese, L. (2022). Graph4Med: a web application and a graph database for visualizing and analyzing medical databases. BMC bioinformatics, 23(1), 537.
Sheu, H.-S., Chu, Z., Qi, D., & Li, S. (2021). Knowledge-guided article embedding refinement for session-based news recommendation. IEEE Transactions on Neural Networks and Learning Systems.
Shi, D., Wang, T., Xing, H., & Xu, H. (2020). A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning. Knowledge-Based Systems, 195, 105618.
Tan, J., Qiu, Q., Guo, W., & Li, T. (2021). Research on the construction of a knowledge graph and knowledge reasoning model in the field of urban traffic. Sustainability, 13(6), 3191.
Tomeo, P., Fernández-Tobías, I., Cantador, I., & Di Noia, T. (2017). Addressing the cold start with positive-only feedback through semantic-based recommendations. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 25(Suppl. 2), 57-78.
Turki, H., Hadj Taieb, M. A., Shafee, T., Lubiana, T., Jemielniak, D., Aouicha, M. B., Labra Gayo, J. E., Youngstrom, E. A., Banat, M. a., & Das, D. (2022). Representing COVID-19 information in collaborative knowledge graphs: the case of Wikidata. Semantic Web(Preprint), 1-32.
Vo, T. (2022). An integrated network embedding with reinforcement learning for explainable recommendation. Soft Computing, 26(8), 3757-3775.
Xiao, Y., Zhang, L., Huang, J., Zhang, L., & Wan, J. (2022). An information retrieval-based joint system for complex chinese knowledge graph question answering. Electronics, 11(19), 3214.
Xiao, Z., & Zhang, C. (2021). Construction of meteorological simulation knowledge graph based on deep learning method. Sustainability, 13(3), 1311.
Xu, D., Zhu, H., Huang, Y., Jin, Z., Ding, W., Li, H., & Ran, M. (2023). Vision-knowledge fusion model for multi-domain medical report generation. Information Fusion, 97, 101817.
Yan, C., Chen, Y., & Zhou, L. (2019). Differentiated fashion recommendation using knowledge graph and data augmentation. IEEE Access, 7, 102239-102248.
Ye, Q., Hsieh, C.-Y., Yang, Z., Kang, Y., Chen, J., Cao, D., He, S., & Hou, T. (2021). A unified drug–target interaction prediction framework based on knowledge graph and recommendation system. Nature communications, 12(1), 1-12.
Yoo, S., & Jeong, O. (2021). EP-Bot: empathetic chatbot using auto-growing knowledge graph. Computers, Materials & Continua, 67(3).
Yuan, H., & Deng, W. (2021). Doctor recommendation on healthcare consultation platforms: an integrated framework of knowledge graph and deep learning. Internet Research, 32(2), 454-476.
Zhang, X., Liu, X., Li, X., & Pan, D. (2017). MMKG: an approach to generate metallic materials knowledge graph based on DBpedia and Wikipedia. Computer Physics Communications, 211, 98-112.
Zhang, Z. (2021). A method of recommending physical education network course resources based on collaborative filtering technology. Scientific Programming, 2021, 1-9.
Zhao, H., Pan, Y., & Yang, F. (2020). Research on information extraction of technical documents and construction of domain knowledge graph. IEEE Access, 8, 168087-168098.
技能分享 | 你不可不知的知識圖譜應用. (2020, July 7). SOURCEZONES.
https://sourcezones.net/2020/07/07/02/
知識圖譜是什麼? (2020, December 14). 人人焦點.
https://ppfocus.com/0/ed51dbad2.html