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題名 自動鏈結資料產生器之發展與數位人文教育應用研究
Development of an Automatic Linked Data Generator and Its Application in Digital Humanities Education作者 陳仙姁
Chen, Xian-Xu貢獻者 陳志銘
Chen, Chih-Ming
陳仙姁
Chen, Xian-Xu關鍵詞 數位人文
文本鏈結
鏈結資料
文本推薦
Digital humanities
Textual link
Linked data
Text recommendation日期 2022 上傳時間 1-三月-2022 17:08:16 (UTC+8) 摘要 本研究旨在開發支援數位人文探究之「自動鏈結資料產生器」，以輔助數位人文學習者在進行大量文本閱讀時，能藉由文本關聯推薦快速掌握及解讀文本內容，以利於梳理出相關人、事、物之間的關聯脈絡。同時，藉由相關文章之文章摘要提供遠讀和細讀相互鏈結的功能，以利於數位人文學習者能更有效率地在相關聯的文章之間進行探索。為了驗證此一工具對於支援數位人文探究之效益，本研究邀請具中文、歷史、哲學等相關背景共16位學生為實驗對象，以實驗研究法之對抗平衡設計比較實驗對象依序使用有/無「自動鏈結資料產生器」之「羅家倫先生文存數位人文平台」輔助進行不同面向文本探索，在文本探索摘要短文分數及科技接受度是否具有顯著的差異，並以半結構式深度訪談瞭解實驗對象對「自動鏈結資料產生器」的看法與感受，最後透過滯後序列分析探討實驗對象使用「具自動鏈結資料產生器」之「羅家倫先生文存數位人文平台」輔助進行不同面向文本探索的有效行為轉移模式。實驗結果發現，實驗對象採用有/無「自動鏈結資料產生器」之「羅家倫先生文存數位人文平台」輔以進行文本探索，其探索文本成效依據探索之文本主題不同而有不同的顯著差異。「具自動鏈結資料產生器」之「羅家倫先生文存數位人文平台」較適合於探索歷史類主題文本，而「無自動鏈結資料產生器」之「羅家倫先生文存數位人文平台」較適合於探索教育類主題文本。在科技接受度分析部分，採用「具自動鏈結資料產生器」之「羅家倫先生文存數位人文平台」在認知有用性上顯著高於「無自動鏈結資料產生器」之「羅家倫先生文存數位人文平台」，表示實驗對象認為「自動鏈結資料產生器」更能輔助其在文本探索中，有效率地掌握相關聯的文章與可探討的主題。在行為分析得知，交互使用「自動鏈結資料產生器」與傳統關鍵字檢索及後分類功能，為比較有效的系統操作行為模式。此外，透過訪談結果得知，大多數實驗對象認為「自動鏈結資料產生器」所產生的相關文章之摘要，提供了遠讀和細讀相互鏈結的功能，可以幫助他們更有效率地在相關聯的文章之間進行探索。在未來研究方向上，可提供文章與文章之間人物的社會網絡關係圖，以藉由人物關係進行文本探索，並發展人機互動的合作模式，提升文本實體標註的準確性，以及嘗試以主題分析（topic analysis）（Pan & Li, 2010）的方式來建立文章之間的關聯，以提供文本探索上更多其他不同面向關係文章的連結性。
This research aims to develop an “Automatic Linked Data Generator” on the Digital Humanities Research Platform for Mr. Lo Chia-lun’s Writings in order to assist learners in quickly grasping and interpreting text contents through the texts recommended by the generator for supporting digital humanities education. It can assist learners to explore the texts relating to a certain topic after viewing the linked data relation map provided by “Automatic Linked Data Generator”, so that learners can more efficiently explore interested topics from a huge amount of texts on Digital Humanities Research Platform for Mr. Lo Chia-lun’s Writings. To verify the effectiveness of “Automatic Linked Data Generator” in supporting the exploration of digital texts for a target topic, a total of 16 students were recruited as the research participants. The counterbalanced design in quasi-experimental research was applied in this study to compare whether the text exploration effectiveness and technology acceptance of the research subjects with and without the support of “Automatic Linked Data Generator” on Digital Humanities Research Platform for Mr. Lo Chia-lun’s Writings were significant differences. Additionally, lag sequential analysis was also used to analyze learners’ operation behaviors on the Digital Humanities Research Platform for Mr. Lo Chia-lun’s Writings with “Automatic Linked Data Generator.” A semi-structured in-depth interview was also applied to understand learners’ opinions and perception of using the Digital Humanities Research Platform for Mr. Lo Chia-lun’s Writings with “Automatic Linked Data Generator” to interpret texts through reading.The results of the experiment reveal that the text exploration effectiveness of the research subjects with and without the support of “Automatic Linked Data Generator” on Digital Humanities Research Platform for Mr. Lo Chia-lun`s Writings has statistically significant difference and the text exploration effectiveness of the research subjects depends on the text topic explored. Particularly, the analytical results show that the Digital Humanities Research Platform for Mr. Lo Chia-lun`s Writings with the “Automatic Linked Data Generator” is more suitable for exploring historical texts, while the Mr. Lo Chia-lun`s Works Digital Humanities Research Platform without the “Automatic Linked Data Generator” is more suitable for exploring educational texts. The perceived usefulness in the technology acceptance of the Digital Humanities Research Platform for Mr. Lo Chia-lun`s Writings with “Automatic Linked Data Generator” is significantly higher than the Digital Humanities Research Platform for Mr. Lo Chia-lun`s Writings without “Automatic Linked Data Generator.” The results indicated that the “Automatic Linked Data Generator” can support learners to efficiently grasp related texts and explore topics during text exploration. In the behavioral analysis, it was found that the interactive use of the “Automatic Linked Data Generator” and the traditional keyword retrieval and post-categorization functions were more effective modes of system operation. In addition, the interview results showed that most of the research participants agreed with that the texts recommended by the “Automatic Linked Data Generator” could help them explore the target topic more efficiently. In the future, the social network relationship graph of characters between texts can be provided to assist learners in exploring the target topic through character relationship and to develop a cooperative model of human-computer interaction to enhance the accuracy of textual entity annotation. It is also possible to try to establish relationship between texts by topic analysis (Pan & Li, 2010), in order to provide more links to other texts with different relationship in text exploration.參考文獻 參考文獻一、中文文獻中國哲學書電子化計劃（2021）。中國哲學書電子化計劃。上網日期：110年4月18日，檢自：https://ctext.org/zh王聿均（1989）。羅志希先生對史學與文學的貢獻。載於羅家倫先生文存第十二冊（頁901-933）。臺北縣：國史館。李玉勝（2016）。學術救國：淺談羅家倫的核心教育理念。現代教育科學，08，137-143。李菁（2007）。羅家倫與三所名校的教育情緣。教育，17，52-53。李漢嶽、楊介銘、宋曜廷（2017）。數位學習實驗研究品質評估與現況分析：以行動學習為例。教育科學研究期刊，62（2），31–60。doi:10.6209/JORIES.2017.62(2).02杜協昌（2018）。DocuSky：個人文字資料庫的建構與分析平臺。數位典藏與數位人文，2，71–90。doi:10.6853/DADH.201810_2.0004林巧敏、陳志銘（2017）。古籍風華再現：關於古籍數位人文平臺之建置。國家圖書館館刊，1，111-132。林正和、張鐘、徐志帆（2020）。數位人文研究平臺之觀點變遷和年代劃分工具發展與應用。圖資與檔案學刊，96，110–170。政治大學圖書館。doi:10.6575/JILA.202006(96).0004林楠（2008）。羅家倫大學辦學實踐述評。中國電力教育，5，161-162。邱詩雯（2019）。數位人文在國學導讀課程的設計與實踐。「國際大數據產學前沿應用教學研討會(WEDHIA 2019)」發表之論文，臺北市國立台灣師範大學。施伯燁（2017）。數位時代的人文研究：數位人文發展沿革、論辯與組織概述。南華社會科學論叢，3，3-19。張慧銖、陳淑燕、邱子恒、陳淑君（2017）。資訊組織。新北市：華藝。粘慈卿（2018）。羅家倫校長學之研究（碩士論文）。取自https://www.AiritiLibrary.com/Publication/Index/U0005-1307201810120100陳光華、薛弼心（2015）。數位人文研究的在地特性與全球特性之探討。人文與社會科學簡訊，17(1)，83-88。陳志銘、張鐘、徐志帆（2020）。羅家倫先生文存數位人文研究平臺之建置與應用。數位典藏與數位人文，5，73–115。doi:10.6853/DADH.202004(5).0003陳淑君（2017）。鏈結資料於數位典藏之研究：以畫家陳澄波為例。圖書館學與資訊科學，43（1），71–96。馬偉雲、李朋軒（2020年7月9日）。結合斷詞、詞性標記、實體辨識的中文處理套件(CkipTagger)。智財技轉處。上網日期：110年1月19日，檢自：https://iptt.sinica.edu.tw/shares/928謝曉欣（2015）。教育家羅家倫及其高等教育思想研究。當代教育實踐與教學研究，8，43-44。項潔、塗豐恩（2011）。導論─什麼是數位人文。載於從保存到創造：開啟數位人文研究（頁9-28）。臺北：國立臺灣大學出版中心。楊希震（1989）。志希先生在中大十年。載於羅家倫先生文存第十二冊（頁600-607）。臺北縣：國史館。榮曹家（2020）。社群媒體研究的異質行動網絡：重新想像數位時代的知識生產。新聞學研究，143，167–213。doi:10.30386/MCR.202004(143).0004劉學銚（2017）。介析羅家倫先生有關邊疆論著。中國邊政，212，1-34。蔣永敬（1974）。羅家倫先生的生平及其對中國近代史研究的貢獻。中央研究院近代史研究所集刊，4，461-495。蕭勝文（2000）。羅家倫與中央大學發展之研究（1932-1941）（碩士論文）。取自https://www.AiritiLibrary.com/Publication/Index/U0021-1804200714563808戴榮冠（2019）。運用GIS重構楊廷理《知還書屋詩鈔》宦遊蘭陽路線及其教學設計。「第十屆數位典藏與數位人文國際研討會」發表之論文，臺北市國立臺灣師範大學。羅久芳（1989）。追念我的父親。載於羅家倫先生文存第十二冊（頁565-581）。臺北縣：國史館。羅久芳（2013）。我的父親羅家倫。北京：商務印書館。羅家倫先生文存編輯委員會（編輯）（1989）。羅家倫先生文存第十二冊。臺北縣：國史館。蘇雲峯（1987）。羅家倫與清華大學。近代史研究所集刊，16，367-382。doi:10.6353/BIMHAS.198706.0367 二、英文文獻Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., & Ives, Z. (2007). DBpedia: A nucleus for a web of open data. In Aberer, K., Choi, K. S., Noy, N., Allemang, D., Lee, K. I., Nixon, L., … Cudré-Mauroux, P. (Eds.), The semantic web (pp. 722-735). Berlin, Heidelberg: Springer Berlin Heidelberg.Augenstein, I., Padó, S., & Rudolph, S. (2012). LODifier: Generating linked data from unstructured text. In E. Simperl, P. Cimiano, A. Polleres, O. Corcho, & V. Presutti (Eds.), The Semantic Web: Research and Applications (pp. 210-224). Berlin, Heidelberg: Springer Berlin Heidelberg.Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis (2nd ed.). Cambridge: Cambridge University Press. doi:10.1017/CBO9780511527685Bassett, C. (2012). Canonicalism and the Computational Turn. In D. M. Berry (Ed.), Understanding Digital Humanities (pp. 105-126). London: Palgrave Macmillan UK. doi:10.1057/9780230371934_6Benard Magara, M., Ojo, S. O., & Zuva, T. (2018). A comparative analysis of text similarity measures and algorithms in research paper recommender systems. In 2018 Conference on Information Communications Technology and Society (ICTAS) (pp. 1-5). Durban: IEEE. doi:10.1109/ICTAS.2018.8368766Benedetti, F., Beneventano, D., Bergamaschi, S., & Simonini, G. (2019). Computing inter-document similarity with Context Semantic Analysis. Information Systems, 80, 136-147. doi:10.1016/j.is.2018.02.009Berners-Lee, T., Hall, W., Hendler, J., Shadbolt, N., & Weitzner, D. J. (2006). Creating a science of the web. Science, 313(5788), 769-771. doi:10.1126/science.1126902Berners-Lee, Tim. (1989). Information management: A proposal. Retrieved from https://www.w3.org/History/1989/proposal.htmlBerners-Lee, Tim. (2006). Linked data - design issues. Retrieved from https://www.w3.org/DesignIssues/LinkedData.htmlBerners-Lee, Tim. (2009). The next web. Retrieved from https://www.ted.com/talks/tim_berners_lee_the_next_webBerry, D. M. (2011). The philosophy of software: Code and mediation in the digital age. London: Palgrave Macmillan UK. doi:10.1057/9780230306479Berry, D. M. (2012). Understanding digital humanities. Houndmills, Basingstoke, Hampshire; New York: Palgrave Macmillan.Bizer, C. (2011). Evolving the web into a global data space. In Fernandes, A. A. A., Gray, A. J. G., & Belhajjame, K.(Eds.), Advances in databases (p. 1). Berlin, Heidelberg: Springer Berlin Heidelberg.Bizer, C., Heath, T., & Berners-Lee, T. (2009). Linked data - the story so far. International Journal on Semantic Web and Information Systems, 5(3), 1-22. doi:10.4018/jswis.2009081901Bizer, C., Heath, T., Idehen, K., & Berners-Lee, T. (2008). Linked data on the web (LDOW2008). In Proceeding of the 17th international conference on World Wide Web - WWW ’08 (pp. 1265-1266). Beijing, China: ACM Press. doi:10.1145/1367497.1367760Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Chicago: Rand McNally.Candela, G., Escobar, P., Carrasco, R. C., & Marco-Such, M. (2019). A linked open data framework to enhance the discoverability and impact of culture heritage. Journal of Information Science, 45(6), 756-766. doi:10.1177/0165551518812658Caraballo, A. A. M., Nunes, B. P., Lopes, G. R., Leme, L. A. P. P., & Casanova, M. A. (2016). Automatic creation and analysis of a linked data cloud diagram. In W. Cellary, M. F. Mokbel, J. Wang, H. Wang, R. Zhou, & Y. Zhang (Eds.), Web Information Systems Engineering – WISE 2016 (Vol. 10041, pp. 417-432). Cham: Springer International Publishing. doi:10.1007/978-3-319-48740-3_31Chen, C. M., Chen, Y. T., & Liu, C. Y. (2019). Development and evaluation of an automatic text annotation system for supporting digital humanities research. Library Hi Tech, 37(3), 436-455. doi:10.1108/LHT-10-2017-0219Cherrington, M., Airehrour, D., Lu, J., Xu, Q., Wade, S., & Madanian, S. (2019). Feature selection methods for linked data: Limitations, capabilities and potentials. In Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies - BDCAT ’19 (pp. 103-112). Auckland, New Zealand: ACM Press. doi:10.1145/3365109.3368792Cimiano, P., Chiarcos, C., McCrae, J. P., & Gracia, J. (2020). Linguistic linked data in digital humanities. In Cimiano, P., Chiarcos, C., McCrae, J. P., & Gracia, J.(Eds.), Linguistic linked data: Representation, generation and applications (pp. 229-262). Cham: Springer International Publishing. doi:10.1007/978-3-030-30225-2_13D`Aquin, M. (2012). Linked data for open and distance learning. England: The Open University, UK.Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. doi:10.2307/249008Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Vol. 1, pp. 4171-4186). Minneapolis, Minnesota: Association for Computational Linguistics. doi:10.18653/v1/N19-1423Dimou, A., Heyvaert, P., De Meester, B., & Verborgh, R. (2018). What factors influence the design of a linked data generation algorithm? (pp. 1-6). Presented at the the 11th Workshop on Linked Data on the Web (LDOW2018).Drucker, J., Kim, D., Salehian, I., & Bushong, A. (2014). Introduction to digital humanities: Concepts, methods, and tutorials for students and instructors. Los Angeles: University of California. Retrieved from https://archive.org/details/IntroductionToDigitalHumanitiesDumitru, D. (2019). Creating meaning. The importance of arts, humanities and culture for critical thinking development. Studies in Higher Education, 44(5), 870-879.El-Roby, A., & Aboulnaga, A. (2015). ALEX: Automatic link exploration in linked data. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (pp. 1839-1853). Melbourne Victoria Australia: ACM. doi:10.1145/2723372.2749428Enis, M. (2015). Ending the Invisible Library. Library Journal, 140(3), 36.Ess, C. (2004). “Revolution? what revolution?” successes and limits of computing technologies in philosophy and religion. In S. Schreibman, R. Siemens, & J. Unsworth (Eds.), A companion to digital humanities (pp. 132-142). Malden, MA, USA: Blackwell Publishing Ltd. doi:10.1002/9780470999875.ch12Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3), 75-174. doi:10.1016/j.physrep.2009.11.002Gorman, A. M. (1961). Recognition memory for nouns as a function of abstractness and frequency. Journal of Experimental Psychology, 61(1), 23-29. doi:10.1037/h0040561Grishman, R., & Sundheim, B. (1996). Message understanding conference-6: A brief history. In Proceedings of the 16th Conference on Computational Linguistics - Volume 1 (pp. 466-471). USA: Association for Computational Linguistics. doi:10.3115/992628.992709Haklay, M., & Weber, P. (2008). OpenStreetMap: User-generated street maps. IEEE Pervasive Computing, 7(4), 12-18. doi:10.1109/MPRV.2008.80Hirsch, B. (2012). Digital humanities pedagogy: Practices, principles and politics. Open Book Publishers. Retrieved from https://research-repository.uwa.edu.au/en/publications/digital-humanities-pedagogy-practices-principles-and-politicsHirsch, B. D., & Timney, M. (2010). The importance of pedagogy: Towards a companion to teaching digital humanities. In Association for Literary and Linguistic Computing, Association for Computers and the Humanities, Society for Digital Humanities, King’s College London, & Office for Humanities Communication (Eds.), Digital humanities 2010: conference abstracts : King’s College London, London, July 7-10, 2010. London: Office for Humanities Communication, Centre for Computing in the Humanities, King’s College London.Ho, S. Y., Chen, C. M., & Chang, C. (2019). A Chinese ancient book digital humanities research platform to support digital humanities research. In 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI) (pp. 1-6). Toyama, Japan: IEEE. doi:10.1109/IIAI-AAI.2019.00012Hockey, S. (2004). The history of humanities computing. In S. Schreibman, R. Siemens, & J. Unsworth (Eds.), A companion to digital humanities (pp. 3-19). Malden, MA, USA: Blackwell Publishing Ltd. doi:10.1002/9780470999875.ch1Jänicke, S., Franzini, G., Cheema, M. F., & Scheuermann, G. (2017). Visual text analysis in digital humanities. Computer Graphics Forum, 36(6), 226-250. doi:10.1111/cgf.12873Jiang, X., Hu, P., Hou, L., & Wang, X. (2018). Improving pointer-generator network with keywords information for chinese abstractive summarization. In M. Zhang, V. Ng, D. Zhao, S. Li, & H. Zan (Eds.), Natural Language Processing and Chinese Computing (pp. 464-474). Cham: Springer International Publishing.Kanakia, A., Shen, Z., Eide, D., & Wang, K. (2019). A scalable hybrid research paper recommender system for Microsoft Academic. The World Wide Web Conference, 2893-2899. doi:10.1145/3308558.3313700Klyne, G., & Carroll, J. J. (2004). Resource description framework (RDF): Concepts and abstract syntax. W3C. Retrieved January 22, 2022, from http://www.w3.org/TR/rdf-concepts/Klyne, G., Carroll, J. J., & McBride, B. (eds) (2014). RDF 1.1 Concepts and Abstract Syntax. W3C. Retrieved April 26, 2021, from https://www.w3.org/TR/rdf11-concepts/Kong, N. N., Bynum, C., Johnson, C., Sdunzik, J., & Qin, X. (2017). Spatial information literacy for digital humanities: The case study of leveraging geospatial information for African American history education. College & Undergraduate Libraries, 24(2-4), 376-392.Ledford, J. L., & Tyler, M. E. (2007). Google Analytics 2.0. Indianapolis, IN: Wiley.Li, J., Sun, A., Han, J., & Li, C. (2020). A survey on deep learning for named entity recognition. IEEE Transactions on Knowledge and Data Engineering, 20. doi:10.1109/TKDE.2020.2981314Li, P. H., Fu, T. J., & Ma, W. Y. (2020). Why attention? Analyze BiLSTM deficiency and its remedies in the case of NER. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8236-8244. doi:10.1609/aaai.v34i05.6338Li, Y., Chu, V., Blohm, S., Zhu, H., & Ho, H. (2011). Facilitating pattern discovery for relation extraction with semantic-signature-based clustering. In Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM ’11 (p. 1415). Glasgow, Scotland, UK: ACM Press. doi:10.1145/2063576.2063781Lin, Y. wei. (2012). Transdisciplinarity and Digital Humanities: Lessons Learned from Developing Text-Mining Tools for Textual Analysis. In D. M. Berry (Ed.), Understanding Digital Humanities (pp. 295-314). London: Palgrave Macmillan UK. doi:10.1057/9780230371934_16Manola, F., Miller, E., & McBride, B. (eds) (2014). RDF 1.1 primer. W3C. Retrieved April 26, 2021, from https://www.w3.org/TR/rdf11-primer/McLaughlin, A. C., & McGill, A. E. (2017). Explicitly teaching critical thinking skills in a history course. Science & Education, 26, 93-105.Mihalcea, R. (2004). Graph-based ranking algorithms for sentence extraction, applied to text summarization. In Proceedings of the ACL 2004 on Interactive poster and demonstration sessions (pp. 170-173). Barcelona, Spain: Association for Computational Linguistics. doi:10.3115/1219044.1219064Mihalcea, R., & Tarau, P. (2004). TextRank: Bringing order into texts. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (pp. 404-411). Barcelona, Spain: Association for Computational Linguistics. Retrieved from https://www.aclweb.org/anthology/W04-3252/Moretti, G., Sprugnoli, R., Menini, S., & Tonelli, S. (2016). ALCIDE: Extracting and visualising content from large document collections to support humanities studies. Knowledge-Based Systems, 111, 100-112. doi:10.1016/j.knosys.2016.08.003Nadeau, D., & Sekine, S. (2007). A survey of named entity recognition and classification. Lingvisticæ Investigationes. John Benjamins. doi:https://doi.org/10.1075/li.30.1.03nadNavigli, R. (2009). Word Sense Disambiguation: A Survey. ACM Comput. Surv., 41(2). doi:10.1145/1459352.1459355Pan, C., & Li, W. (2010). Research paper recommendation with topic analysis. In 2010 International Conference On Computer Design and Applications (Vol. 4, pp. 264-268). Qinhuangdao, China: IEEE. doi:10.1109/ICCDA.2010.5541170Paulovich, F. V., & Minghim, R. (2006). Text Map Explorer: a Tool to Create and Explore Document Maps. In Tenth International Conference on Information Visualisation (IV’06) (pp. 245-251). London, England: IEEE. doi:10.1109/IV.2006.104Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In Brusilovsky, P., Kobsa,A., & Nejdl, W., (Eds.), The Adaptive Web (Vol. 4321, pp. 325-341). Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/978-3-540-72079-9_10Pereira, C. K., Matsui Siqueira, S. W., Nunes, B. P., & Dietze, S. (2018). Linked Data in Education: A Survey and a Synthesis of Actual Research and Future Challenges. IEEETransactions on Learning Technologies, 11(3), 400-412. doi:10.1109/TLT.2017.2787659Pérez, J., Arenas, M., & Gutierrez, C. (2009). Semantics and complexity of SPARQL. ACM Trans. Database Syst., 34(3), 16:1-16:45. doi:10.1145/1567274.1567278Ramakrishnan, C., Kochut, K. J., & Sheth, A. P. (2006). A Framework for Schema-Driven Relationship Discovery from Unstructured Text. In I. Cruz, S. Decker, D. Allemang, C. Preist, D. Schwabe, P. Mika, … L. M. Aroyo (Eds.), The Semantic Web - ISWC 2006 (Vol. 4273, pp. 583-596). Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/11926078_42Rosling, H. (2006). The best stats you’ve ever seen. Retrieved from https://www.ted.com/talks/hans_rosling_the_best_stats_you_ve_ever_seenRubín, S. (2013). Critical thinking and humanist up-bringing integration in a multimodal curriculum. EDULEARN13 Proceedings, 4775-4785.Sadoski, M., & Quast, Z. (1990). Reader Response and Long-Term Recall for Journalistic Text: The Roles of Imagery, Affect, and Importance. Reading Research Quarterly, 25(4), 256-272. doi:10.2307/747691Sadoski, M., Goetz, E. T., & Rodriguez, M. (2000). Engaging texts: Effects of concreteness on comprehensibility, interest, and recall in four text types. Journal of Educational Psychology, 92(1), 85-95. doi:10.1037/0022-0663.92.1.85Sarwar, B., Karypis, G., Konstan, J., & Reidl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the tenth international conference on World Wide Web - WWW ’01 (pp. 285-295). Hong Kong: ACM Press. doi:10.1145/371920.372071Spiro, L. (2012). Opening up Digital Humanities Education. In B. D. Hirsch (Ed.), Digital Humanities Pedagogy (1st ed., Vol. 3, pp. 331-364). Open Book Publishers. doi:10.2307/j.ctt5vjtt3.19Sturgeon, D. (2020). Digitizing premodern text with the chinese text project. Journal of Chinese History, 4(2), 486-498. doi:10.1017/jch.2020.19Suchanek, F. M., Abiteboul, S., & Senellart, P. (2011). PARIS: probabilistic alignment of relations, instances, and schema. Proceedings of the VLDB Endowment, 5(3), 157-168. doi:10.14778/2078331.2078332Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). How to fine-tune BERT for text classification? In M. Sun, X. Huang, H. Ji, Z. Liu, & Y. Liu (Eds.), Chinese Computational Linguistics (pp. 194-206). Cham: Springer International Publishing. doi:https://doi.org/10.1007/978-3-030-32381-3_16Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: an introduction. Cambridge, Mass: MIT Press.Sztyler, T., Huber, J., Noessner, J., Murdock, J., Allen, C., & Niepert, M. (2014). LODE: Linking digital humanities content to the web of data. In IEEE/ACM Joint Conference on Digital Libraries (pp. 423-424). doi:10.1109/JCDL.2014.6970206Tao, C., Song, D., Sharma, D., & Chute, C. G. (2013). Semantator: Semantic annotator for converting biomedical text to linked data. Journal of Biomedical Informatics, 46(5), 882-893. doi:10.1016/j.jbi.2013.07.003Trace, C. B., & Karadkar, U. P. (2017). Information management in the humanities: Scholarly processes, tools, and the construction of personal collections. Journal of the Association for Information Science and Technology, 68(2), 491-507. doi:10.1002/asi.23678Tsui, L. H., & Wang, H. (2020). Harvesting big biographical data for chinese history: The china biographical database (CBDB). Journal of Chinese History, 4(2), 505-511. doi:10.1017/jch.2020.21Tu, H. C., Hsiang, J., Hung, I. M., & Hu, C. (2020). DocuSky, A Personal Digital Humanities Platform for Scholars. Journal of Chinese History, 4(2), 564-580. doi:10.1017/jch.2020.28Vrandečić, D., & Krötzsch, M. (2014). Wikidata: A free collaborative knowledgebase. Commun. ACM, 57(10), 78-85. doi:10.1145/2629489Vukotic, A., Watt, N., Abedrabbo, T., Fox, D., & Partner, J. (2015). Neo4j in action. Shelter Island, NY: Manning Publications Co.Waltzer, L. (2012). Digital humanities and the “Ugly Stepchildren” of American higher education. In M. K. Gold (Ed.), Debates in the Digital Humanities (NED-New edition., pp. 335-349). University of Minnesota Press. Retrieved from https://www.jstor.org/stable/10.5749/j.ctttv8hq.22Webber, J. (2012). A programmatic introduction to Neo4j. In Proceedings of the 3rd annual conference on Systems, programming, and applications: software for humanity - SPLASH ’12 (p. 217). Tucson, Arizona, USA: ACM Press. doi:10.1145/2384716.2384777Wei, Q., Chen, Y., Salimi, M., Denny, J. C., Mei, Q., Lasko, T. A., et al. (2019). Cost-aware active learning for named entity recognition in clinical text. Journal of the American Medical Informatics Association, 26(11), 1314-1322. doi:10.1093/jamia/ocz102Wood, D., Zaidman, M., Ruth, L., & Hausenblas, M. (2014). Linked data: Structured data on the web. New York: Manning Publications.Xia, P., Zhang, L., & Li, F. (2015). Learning similarity with cosine similarity ensemble. Information Sciences, 307, 39-52. doi:10.1016/j.ins.2015.02.024Zeng, M. L. (2019). Semantic enrichment for enhancing LAM data and supporting digital humanities. Profesional De La Información, 28(1). doi:10.3145/epi.2019.ene.03 描述 碩士
國立政治大學
圖書資訊與檔案學研究所
108155005資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108155005 資料類型 thesis dc.contributor.advisor 陳志銘 zh_TW dc.contributor.advisor Chen, Chih-Ming en_US dc.contributor.author (作者) 陳仙姁 zh_TW dc.contributor.author (作者) Chen, Xian-Xu en_US dc.creator (作者) 陳仙姁 zh_TW dc.creator (作者) Chen, Xian-Xu en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-三月-2022 17:08:16 (UTC+8) - dc.date.available 1-三月-2022 17:08:16 (UTC+8) - dc.date.issued (上傳時間) 1-三月-2022 17:08:16 (UTC+8) - dc.identifier (其他 識別碼) G0108155005 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/139200 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 圖書資訊與檔案學研究所 zh_TW dc.description (描述) 108155005 zh_TW dc.description.abstract (摘要) 本研究旨在開發支援數位人文探究之「自動鏈結資料產生器」，以輔助數位人文學習者在進行大量文本閱讀時，能藉由文本關聯推薦快速掌握及解讀文本內容，以利於梳理出相關人、事、物之間的關聯脈絡。同時，藉由相關文章之文章摘要提供遠讀和細讀相互鏈結的功能，以利於數位人文學習者能更有效率地在相關聯的文章之間進行探索。為了驗證此一工具對於支援數位人文探究之效益，本研究邀請具中文、歷史、哲學等相關背景共16位學生為實驗對象，以實驗研究法之對抗平衡設計比較實驗對象依序使用有/無「自動鏈結資料產生器」之「羅家倫先生文存數位人文平台」輔助進行不同面向文本探索，在文本探索摘要短文分數及科技接受度是否具有顯著的差異，並以半結構式深度訪談瞭解實驗對象對「自動鏈結資料產生器」的看法與感受，最後透過滯後序列分析探討實驗對象使用「具自動鏈結資料產生器」之「羅家倫先生文存數位人文平台」輔助進行不同面向文本探索的有效行為轉移模式。實驗結果發現，實驗對象採用有/無「自動鏈結資料產生器」之「羅家倫先生文存數位人文平台」輔以進行文本探索，其探索文本成效依據探索之文本主題不同而有不同的顯著差異。「具自動鏈結資料產生器」之「羅家倫先生文存數位人文平台」較適合於探索歷史類主題文本，而「無自動鏈結資料產生器」之「羅家倫先生文存數位人文平台」較適合於探索教育類主題文本。在科技接受度分析部分，採用「具自動鏈結資料產生器」之「羅家倫先生文存數位人文平台」在認知有用性上顯著高於「無自動鏈結資料產生器」之「羅家倫先生文存數位人文平台」，表示實驗對象認為「自動鏈結資料產生器」更能輔助其在文本探索中，有效率地掌握相關聯的文章與可探討的主題。在行為分析得知，交互使用「自動鏈結資料產生器」與傳統關鍵字檢索及後分類功能，為比較有效的系統操作行為模式。此外，透過訪談結果得知，大多數實驗對象認為「自動鏈結資料產生器」所產生的相關文章之摘要，提供了遠讀和細讀相互鏈結的功能，可以幫助他們更有效率地在相關聯的文章之間進行探索。在未來研究方向上，可提供文章與文章之間人物的社會網絡關係圖，以藉由人物關係進行文本探索，並發展人機互動的合作模式，提升文本實體標註的準確性，以及嘗試以主題分析（topic analysis）（Pan & Li, 2010）的方式來建立文章之間的關聯，以提供文本探索上更多其他不同面向關係文章的連結性。 zh_TW dc.description.abstract (摘要) This research aims to develop an “Automatic Linked Data Generator” on the Digital Humanities Research Platform for Mr. Lo Chia-lun’s Writings in order to assist learners in quickly grasping and interpreting text contents through the texts recommended by the generator for supporting digital humanities education. It can assist learners to explore the texts relating to a certain topic after viewing the linked data relation map provided by “Automatic Linked Data Generator”, so that learners can more efficiently explore interested topics from a huge amount of texts on Digital Humanities Research Platform for Mr. Lo Chia-lun’s Writings. To verify the effectiveness of “Automatic Linked Data Generator” in supporting the exploration of digital texts for a target topic, a total of 16 students were recruited as the research participants. The counterbalanced design in quasi-experimental research was applied in this study to compare whether the text exploration effectiveness and technology acceptance of the research subjects with and without the support of “Automatic Linked Data Generator” on Digital Humanities Research Platform for Mr. Lo Chia-lun’s Writings were significant differences. Additionally, lag sequential analysis was also used to analyze learners’ operation behaviors on the Digital Humanities Research Platform for Mr. Lo Chia-lun’s Writings with “Automatic Linked Data Generator.” A semi-structured in-depth interview was also applied to understand learners’ opinions and perception of using the Digital Humanities Research Platform for Mr. Lo Chia-lun’s Writings with “Automatic Linked Data Generator” to interpret texts through reading.The results of the experiment reveal that the text exploration effectiveness of the research subjects with and without the support of “Automatic Linked Data Generator” on Digital Humanities Research Platform for Mr. Lo Chia-lun`s Writings has statistically significant difference and the text exploration effectiveness of the research subjects depends on the text topic explored. Particularly, the analytical results show that the Digital Humanities Research Platform for Mr. Lo Chia-lun`s Writings with the “Automatic Linked Data Generator” is more suitable for exploring historical texts, while the Mr. Lo Chia-lun`s Works Digital Humanities Research Platform without the “Automatic Linked Data Generator” is more suitable for exploring educational texts. The perceived usefulness in the technology acceptance of the Digital Humanities Research Platform for Mr. Lo Chia-lun`s Writings with “Automatic Linked Data Generator” is significantly higher than the Digital Humanities Research Platform for Mr. Lo Chia-lun`s Writings without “Automatic Linked Data Generator.” The results indicated that the “Automatic Linked Data Generator” can support learners to efficiently grasp related texts and explore topics during text exploration. In the behavioral analysis, it was found that the interactive use of the “Automatic Linked Data Generator” and the traditional keyword retrieval and post-categorization functions were more effective modes of system operation. In addition, the interview results showed that most of the research participants agreed with that the texts recommended by the “Automatic Linked Data Generator” could help them explore the target topic more efficiently. In the future, the social network relationship graph of characters between texts can be provided to assist learners in exploring the target topic through character relationship and to develop a cooperative model of human-computer interaction to enhance the accuracy of textual entity annotation. It is also possible to try to establish relationship between texts by topic analysis (Pan & Li, 2010), in order to provide more links to other texts with different relationship in text exploration. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究背景與動機 1第二節 研究目的 4第三節 研究問題 5第四節 研究範圍與限制 6第五節 名詞解釋 7第二章 文獻探討 8第一節 數位人文現況與發展 8第二節 鏈結資料現況與應用 12第三節 羅家倫先生之相關研究 19第四節 數位人文教育 22第三章 系統設計 24第一節 系統設計理念 24第二節 系統設計方法之初探 25第三節 系統架構 32第四節 系統元件 34第五節 系統開發環境 39第六節 系統介面與功能 41第四章 研究設計與實施 46第一節 研究架構 46第二節 研究方法 48第三節 研究對象 50第四節 研究工具 51第五節 實驗設計 54第六節 資料與分析 57第七節 研究實施步驟 59第五章 實驗結果分析 61第一節 實驗對象基本資料分析 62第二節 有/無「自動鏈結資料產生器」之「羅家倫先生文存數位人文平台」之文本探索成效差異分析 64第三節 有/無「自動鏈結資料產生器」之「羅家倫先生文存數位人文平台」之科技接受度差異分析 66第四節 採用「具自動鏈結資料產生器」之「羅家倫先生文存數位人文平台」實驗對象行為歷程分析 70第五節 訪談資料分析 82第六節 系統使用案例 93第七節 綜合討論 99第六章 結論與建議 106第一節 結論 106第二節 工具改善建議 111第三節 未來研究方向 113參考文獻 115附錄一 受試者參與研究同意書 125附錄二 具「自動鏈結資料產生器」之「歷史面向」文本探索紀錄表 126附錄三 具「自動鏈結資料產生器」之「教育面向」文本探索紀錄表 128附錄四 無「自動鏈結資料產生器」之「歷史面向」文本探索紀錄表 130附錄五 無「自動鏈結資料產生器」之「教育面向」文本探索紀錄表 131附錄六 「自動鏈結資料產生器」科技接受度 132附錄七 「全文檢索」科技接受度 133附錄八 「具自動鏈結資料產生器」訪談大綱 134 zh_TW dc.format.extent 6900852 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108155005 en_US dc.subject (關鍵詞) 數位人文 zh_TW dc.subject (關鍵詞) 文本鏈結 zh_TW dc.subject (關鍵詞) 鏈結資料 zh_TW dc.subject (關鍵詞) 文本推薦 zh_TW dc.subject (關鍵詞) Digital humanities en_US dc.subject (關鍵詞) Textual link en_US dc.subject (關鍵詞) Linked data en_US dc.subject (關鍵詞) Text recommendation en_US dc.title (題名) 自動鏈結資料產生器之發展與數位人文教育應用研究 zh_TW dc.title (題名) Development of an Automatic Linked Data Generator and Its Application in Digital Humanities Education en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 參考文獻一、中文文獻中國哲學書電子化計劃（2021）。中國哲學書電子化計劃。上網日期：110年4月18日，檢自：https://ctext.org/zh王聿均（1989）。羅志希先生對史學與文學的貢獻。載於羅家倫先生文存第十二冊（頁901-933）。臺北縣：國史館。李玉勝（2016）。學術救國：淺談羅家倫的核心教育理念。現代教育科學，08，137-143。李菁（2007）。羅家倫與三所名校的教育情緣。教育，17，52-53。李漢嶽、楊介銘、宋曜廷（2017）。數位學習實驗研究品質評估與現況分析：以行動學習為例。教育科學研究期刊，62（2），31–60。doi:10.6209/JORIES.2017.62(2).02杜協昌（2018）。DocuSky：個人文字資料庫的建構與分析平臺。數位典藏與數位人文，2，71–90。doi:10.6853/DADH.201810_2.0004林巧敏、陳志銘（2017）。古籍風華再現：關於古籍數位人文平臺之建置。國家圖書館館刊，1，111-132。林正和、張鐘、徐志帆（2020）。數位人文研究平臺之觀點變遷和年代劃分工具發展與應用。圖資與檔案學刊，96，110–170。政治大學圖書館。doi:10.6575/JILA.202006(96).0004林楠（2008）。羅家倫大學辦學實踐述評。中國電力教育，5，161-162。邱詩雯（2019）。數位人文在國學導讀課程的設計與實踐。「國際大數據產學前沿應用教學研討會(WEDHIA 2019)」發表之論文，臺北市國立台灣師範大學。施伯燁（2017）。數位時代的人文研究：數位人文發展沿革、論辯與組織概述。南華社會科學論叢，3，3-19。張慧銖、陳淑燕、邱子恒、陳淑君（2017）。資訊組織。新北市：華藝。粘慈卿（2018）。羅家倫校長學之研究（碩士論文）。取自https://www.AiritiLibrary.com/Publication/Index/U0005-1307201810120100陳光華、薛弼心（2015）。數位人文研究的在地特性與全球特性之探討。人文與社會科學簡訊，17(1)，83-88。陳志銘、張鐘、徐志帆（2020）。羅家倫先生文存數位人文研究平臺之建置與應用。數位典藏與數位人文，5，73–115。doi:10.6853/DADH.202004(5).0003陳淑君（2017）。鏈結資料於數位典藏之研究：以畫家陳澄波為例。圖書館學與資訊科學，43（1），71–96。馬偉雲、李朋軒（2020年7月9日）。結合斷詞、詞性標記、實體辨識的中文處理套件(CkipTagger)。智財技轉處。上網日期：110年1月19日，檢自：https://iptt.sinica.edu.tw/shares/928謝曉欣（2015）。教育家羅家倫及其高等教育思想研究。當代教育實踐與教學研究，8，43-44。項潔、塗豐恩（2011）。導論─什麼是數位人文。載於從保存到創造：開啟數位人文研究（頁9-28）。臺北：國立臺灣大學出版中心。楊希震（1989）。志希先生在中大十年。載於羅家倫先生文存第十二冊（頁600-607）。臺北縣：國史館。榮曹家（2020）。社群媒體研究的異質行動網絡：重新想像數位時代的知識生產。新聞學研究，143，167–213。doi:10.30386/MCR.202004(143).0004劉學銚（2017）。介析羅家倫先生有關邊疆論著。中國邊政，212，1-34。蔣永敬（1974）。羅家倫先生的生平及其對中國近代史研究的貢獻。中央研究院近代史研究所集刊，4，461-495。蕭勝文（2000）。羅家倫與中央大學發展之研究（1932-1941）（碩士論文）。取自https://www.AiritiLibrary.com/Publication/Index/U0021-1804200714563808戴榮冠（2019）。運用GIS重構楊廷理《知還書屋詩鈔》宦遊蘭陽路線及其教學設計。「第十屆數位典藏與數位人文國際研討會」發表之論文，臺北市國立臺灣師範大學。羅久芳（1989）。追念我的父親。載於羅家倫先生文存第十二冊（頁565-581）。臺北縣：國史館。羅久芳（2013）。我的父親羅家倫。北京：商務印書館。羅家倫先生文存編輯委員會（編輯）（1989）。羅家倫先生文存第十二冊。臺北縣：國史館。蘇雲峯（1987）。羅家倫與清華大學。近代史研究所集刊，16，367-382。doi:10.6353/BIMHAS.198706.0367 二、英文文獻Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., & Ives, Z. (2007). DBpedia: A nucleus for a web of open data. In Aberer, K., Choi, K. S., Noy, N., Allemang, D., Lee, K. I., Nixon, L., … Cudré-Mauroux, P. (Eds.), The semantic web (pp. 722-735). Berlin, Heidelberg: Springer Berlin Heidelberg.Augenstein, I., Padó, S., & Rudolph, S. (2012). LODifier: Generating linked data from unstructured text. In E. Simperl, P. Cimiano, A. Polleres, O. Corcho, & V. Presutti (Eds.), The Semantic Web: Research and Applications (pp. 210-224). Berlin, Heidelberg: Springer Berlin Heidelberg.Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis (2nd ed.). Cambridge: Cambridge University Press. doi:10.1017/CBO9780511527685Bassett, C. (2012). Canonicalism and the Computational Turn. In D. M. Berry (Ed.), Understanding Digital Humanities (pp. 105-126). London: Palgrave Macmillan UK. doi:10.1057/9780230371934_6Benard Magara, M., Ojo, S. O., & Zuva, T. (2018). A comparative analysis of text similarity measures and algorithms in research paper recommender systems. In 2018 Conference on Information Communications Technology and Society (ICTAS) (pp. 1-5). Durban: IEEE. doi:10.1109/ICTAS.2018.8368766Benedetti, F., Beneventano, D., Bergamaschi, S., & Simonini, G. (2019). Computing inter-document similarity with Context Semantic Analysis. Information Systems, 80, 136-147. doi:10.1016/j.is.2018.02.009Berners-Lee, T., Hall, W., Hendler, J., Shadbolt, N., & Weitzner, D. J. (2006). Creating a science of the web. Science, 313(5788), 769-771. doi:10.1126/science.1126902Berners-Lee, Tim. (1989). Information management: A proposal. Retrieved from https://www.w3.org/History/1989/proposal.htmlBerners-Lee, Tim. (2006). Linked data - design issues. Retrieved from https://www.w3.org/DesignIssues/LinkedData.htmlBerners-Lee, Tim. (2009). The next web. Retrieved from https://www.ted.com/talks/tim_berners_lee_the_next_webBerry, D. M. (2011). The philosophy of software: Code and mediation in the digital age. London: Palgrave Macmillan UK. doi:10.1057/9780230306479Berry, D. M. (2012). Understanding digital humanities. Houndmills, Basingstoke, Hampshire; New York: Palgrave Macmillan.Bizer, C. (2011). Evolving the web into a global data space. In Fernandes, A. A. A., Gray, A. J. G., & Belhajjame, K.(Eds.), Advances in databases (p. 1). Berlin, Heidelberg: Springer Berlin Heidelberg.Bizer, C., Heath, T., & Berners-Lee, T. (2009). Linked data - the story so far. International Journal on Semantic Web and Information Systems, 5(3), 1-22. doi:10.4018/jswis.2009081901Bizer, C., Heath, T., Idehen, K., & Berners-Lee, T. (2008). Linked data on the web (LDOW2008). In Proceeding of the 17th international conference on World Wide Web - WWW ’08 (pp. 1265-1266). Beijing, China: ACM Press. doi:10.1145/1367497.1367760Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Chicago: Rand McNally.Candela, G., Escobar, P., Carrasco, R. C., & Marco-Such, M. (2019). A linked open data framework to enhance the discoverability and impact of culture heritage. Journal of Information Science, 45(6), 756-766. doi:10.1177/0165551518812658Caraballo, A. A. M., Nunes, B. P., Lopes, G. R., Leme, L. A. P. P., & Casanova, M. A. (2016). Automatic creation and analysis of a linked data cloud diagram. In W. Cellary, M. F. Mokbel, J. Wang, H. Wang, R. Zhou, & Y. Zhang (Eds.), Web Information Systems Engineering – WISE 2016 (Vol. 10041, pp. 417-432). Cham: Springer International Publishing. doi:10.1007/978-3-319-48740-3_31Chen, C. M., Chen, Y. T., & Liu, C. Y. (2019). Development and evaluation of an automatic text annotation system for supporting digital humanities research. Library Hi Tech, 37(3), 436-455. doi:10.1108/LHT-10-2017-0219Cherrington, M., Airehrour, D., Lu, J., Xu, Q., Wade, S., & Madanian, S. (2019). Feature selection methods for linked data: Limitations, capabilities and potentials. In Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies - BDCAT ’19 (pp. 103-112). Auckland, New Zealand: ACM Press. doi:10.1145/3365109.3368792Cimiano, P., Chiarcos, C., McCrae, J. P., & Gracia, J. (2020). Linguistic linked data in digital humanities. In Cimiano, P., Chiarcos, C., McCrae, J. P., & Gracia, J.(Eds.), Linguistic linked data: Representation, generation and applications (pp. 229-262). Cham: Springer International Publishing. doi:10.1007/978-3-030-30225-2_13D`Aquin, M. (2012). Linked data for open and distance learning. England: The Open University, UK.Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. doi:10.2307/249008Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Vol. 1, pp. 4171-4186). Minneapolis, Minnesota: Association for Computational Linguistics. doi:10.18653/v1/N19-1423Dimou, A., Heyvaert, P., De Meester, B., & Verborgh, R. (2018). What factors influence the design of a linked data generation algorithm? (pp. 1-6). Presented at the the 11th Workshop on Linked Data on the Web (LDOW2018).Drucker, J., Kim, D., Salehian, I., & Bushong, A. (2014). Introduction to digital humanities: Concepts, methods, and tutorials for students and instructors. Los Angeles: University of California. Retrieved from https://archive.org/details/IntroductionToDigitalHumanitiesDumitru, D. (2019). Creating meaning. The importance of arts, humanities and culture for critical thinking development. Studies in Higher Education, 44(5), 870-879.El-Roby, A., & Aboulnaga, A. (2015). ALEX: Automatic link exploration in linked data. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (pp. 1839-1853). Melbourne Victoria Australia: ACM. doi:10.1145/2723372.2749428Enis, M. (2015). Ending the Invisible Library. Library Journal, 140(3), 36.Ess, C. (2004). “Revolution? what revolution?” successes and limits of computing technologies in philosophy and religion. In S. Schreibman, R. Siemens, & J. Unsworth (Eds.), A companion to digital humanities (pp. 132-142). Malden, MA, USA: Blackwell Publishing Ltd. doi:10.1002/9780470999875.ch12Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3), 75-174. doi:10.1016/j.physrep.2009.11.002Gorman, A. M. (1961). Recognition memory for nouns as a function of abstractness and frequency. Journal of Experimental Psychology, 61(1), 23-29. doi:10.1037/h0040561Grishman, R., & Sundheim, B. (1996). Message understanding conference-6: A brief history. In Proceedings of the 16th Conference on Computational Linguistics - Volume 1 (pp. 466-471). USA: Association for Computational Linguistics. doi:10.3115/992628.992709Haklay, M., & Weber, P. (2008). OpenStreetMap: User-generated street maps. IEEE Pervasive Computing, 7(4), 12-18. doi:10.1109/MPRV.2008.80Hirsch, B. (2012). Digital humanities pedagogy: Practices, principles and politics. Open Book Publishers. Retrieved from https://research-repository.uwa.edu.au/en/publications/digital-humanities-pedagogy-practices-principles-and-politicsHirsch, B. D., & Timney, M. (2010). The importance of pedagogy: Towards a companion to teaching digital humanities. In Association for Literary and Linguistic Computing, Association for Computers and the Humanities, Society for Digital Humanities, King’s College London, & Office for Humanities Communication (Eds.), Digital humanities 2010: conference abstracts : King’s College London, London, July 7-10, 2010. London: Office for Humanities Communication, Centre for Computing in the Humanities, King’s College London.Ho, S. Y., Chen, C. M., & Chang, C. (2019). A Chinese ancient book digital humanities research platform to support digital humanities research. In 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI) (pp. 1-6). Toyama, Japan: IEEE. doi:10.1109/IIAI-AAI.2019.00012Hockey, S. (2004). The history of humanities computing. In S. Schreibman, R. Siemens, & J. Unsworth (Eds.), A companion to digital humanities (pp. 3-19). Malden, MA, USA: Blackwell Publishing Ltd. doi:10.1002/9780470999875.ch1Jänicke, S., Franzini, G., Cheema, M. F., & Scheuermann, G. (2017). Visual text analysis in digital humanities. Computer Graphics Forum, 36(6), 226-250. doi:10.1111/cgf.12873Jiang, X., Hu, P., Hou, L., & Wang, X. (2018). Improving pointer-generator network with keywords information for chinese abstractive summarization. In M. Zhang, V. Ng, D. Zhao, S. Li, & H. Zan (Eds.), Natural Language Processing and Chinese Computing (pp. 464-474). Cham: Springer International Publishing.Kanakia, A., Shen, Z., Eide, D., & Wang, K. (2019). A scalable hybrid research paper recommender system for Microsoft Academic. The World Wide Web Conference, 2893-2899. doi:10.1145/3308558.3313700Klyne, G., & Carroll, J. J. (2004). Resource description framework (RDF): Concepts and abstract syntax. W3C. Retrieved January 22, 2022, from http://www.w3.org/TR/rdf-concepts/Klyne, G., Carroll, J. J., & McBride, B. (eds) (2014). RDF 1.1 Concepts and Abstract Syntax. W3C. Retrieved April 26, 2021, from https://www.w3.org/TR/rdf11-concepts/Kong, N. N., Bynum, C., Johnson, C., Sdunzik, J., & Qin, X. (2017). Spatial information literacy for digital humanities: The case study of leveraging geospatial information for African American history education. College & Undergraduate Libraries, 24(2-4), 376-392.Ledford, J. L., & Tyler, M. E. (2007). Google Analytics 2.0. Indianapolis, IN: Wiley.Li, J., Sun, A., Han, J., & Li, C. (2020). A survey on deep learning for named entity recognition. IEEE Transactions on Knowledge and Data Engineering, 20. doi:10.1109/TKDE.2020.2981314Li, P. H., Fu, T. J., & Ma, W. Y. (2020). Why attention? Analyze BiLSTM deficiency and its remedies in the case of NER. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8236-8244. doi:10.1609/aaai.v34i05.6338Li, Y., Chu, V., Blohm, S., Zhu, H., & Ho, H. (2011). Facilitating pattern discovery for relation extraction with semantic-signature-based clustering. In Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM ’11 (p. 1415). Glasgow, Scotland, UK: ACM Press. doi:10.1145/2063576.2063781Lin, Y. wei. (2012). Transdisciplinarity and Digital Humanities: Lessons Learned from Developing Text-Mining Tools for Textual Analysis. In D. M. Berry (Ed.), Understanding Digital Humanities (pp. 295-314). London: Palgrave Macmillan UK. doi:10.1057/9780230371934_16Manola, F., Miller, E., & McBride, B. (eds) (2014). RDF 1.1 primer. W3C. Retrieved April 26, 2021, from https://www.w3.org/TR/rdf11-primer/McLaughlin, A. C., & McGill, A. E. (2017). Explicitly teaching critical thinking skills in a history course. Science & Education, 26, 93-105.Mihalcea, R. (2004). Graph-based ranking algorithms for sentence extraction, applied to text summarization. In Proceedings of the ACL 2004 on Interactive poster and demonstration sessions (pp. 170-173). Barcelona, Spain: Association for Computational Linguistics. doi:10.3115/1219044.1219064Mihalcea, R., & Tarau, P. (2004). TextRank: Bringing order into texts. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (pp. 404-411). Barcelona, Spain: Association for Computational Linguistics. Retrieved from https://www.aclweb.org/anthology/W04-3252/Moretti, G., Sprugnoli, R., Menini, S., & Tonelli, S. (2016). ALCIDE: Extracting and visualising content from large document collections to support humanities studies. Knowledge-Based Systems, 111, 100-112. doi:10.1016/j.knosys.2016.08.003Nadeau, D., & Sekine, S. (2007). A survey of named entity recognition and classification. Lingvisticæ Investigationes. John Benjamins. doi:https://doi.org/10.1075/li.30.1.03nadNavigli, R. (2009). Word Sense Disambiguation: A Survey. ACM Comput. Surv., 41(2). doi:10.1145/1459352.1459355Pan, C., & Li, W. (2010). Research paper recommendation with topic analysis. In 2010 International Conference On Computer Design and Applications (Vol. 4, pp. 264-268). Qinhuangdao, China: IEEE. doi:10.1109/ICCDA.2010.5541170Paulovich, F. V., & Minghim, R. (2006). Text Map Explorer: a Tool to Create and Explore Document Maps. In Tenth International Conference on Information Visualisation (IV’06) (pp. 245-251). London, England: IEEE. doi:10.1109/IV.2006.104Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In Brusilovsky, P., Kobsa,A., & Nejdl, W., (Eds.), The Adaptive Web (Vol. 4321, pp. 325-341). Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/978-3-540-72079-9_10Pereira, C. K., Matsui Siqueira, S. W., Nunes, B. P., & Dietze, S. (2018). Linked Data in Education: A Survey and a Synthesis of Actual Research and Future Challenges. IEEETransactions on Learning Technologies, 11(3), 400-412. doi:10.1109/TLT.2017.2787659Pérez, J., Arenas, M., & Gutierrez, C. (2009). Semantics and complexity of SPARQL. ACM Trans. Database Syst., 34(3), 16:1-16:45. doi:10.1145/1567274.1567278Ramakrishnan, C., Kochut, K. J., & Sheth, A. P. (2006). A Framework for Schema-Driven Relationship Discovery from Unstructured Text. In I. Cruz, S. Decker, D. Allemang, C. Preist, D. Schwabe, P. Mika, … L. M. Aroyo (Eds.), The Semantic Web - ISWC 2006 (Vol. 4273, pp. 583-596). Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/11926078_42Rosling, H. (2006). The best stats you’ve ever seen. Retrieved from https://www.ted.com/talks/hans_rosling_the_best_stats_you_ve_ever_seenRubín, S. (2013). Critical thinking and humanist up-bringing integration in a multimodal curriculum. EDULEARN13 Proceedings, 4775-4785.Sadoski, M., & Quast, Z. (1990). Reader Response and Long-Term Recall for Journalistic Text: The Roles of Imagery, Affect, and Importance. Reading Research Quarterly, 25(4), 256-272. doi:10.2307/747691Sadoski, M., Goetz, E. T., & Rodriguez, M. (2000). Engaging texts: Effects of concreteness on comprehensibility, interest, and recall in four text types. Journal of Educational Psychology, 92(1), 85-95. doi:10.1037/0022-0663.92.1.85Sarwar, B., Karypis, G., Konstan, J., & Reidl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the tenth international conference on World Wide Web - WWW ’01 (pp. 285-295). Hong Kong: ACM Press. doi:10.1145/371920.372071Spiro, L. (2012). Opening up Digital Humanities Education. In B. D. Hirsch (Ed.), Digital Humanities Pedagogy (1st ed., Vol. 3, pp. 331-364). Open Book Publishers. doi:10.2307/j.ctt5vjtt3.19Sturgeon, D. (2020). Digitizing premodern text with the chinese text project. Journal of Chinese History, 4(2), 486-498. doi:10.1017/jch.2020.19Suchanek, F. M., Abiteboul, S., & Senellart, P. (2011). PARIS: probabilistic alignment of relations, instances, and schema. Proceedings of the VLDB Endowment, 5(3), 157-168. doi:10.14778/2078331.2078332Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). How to fine-tune BERT for text classification? In M. Sun, X. Huang, H. Ji, Z. Liu, & Y. Liu (Eds.), Chinese Computational Linguistics (pp. 194-206). Cham: Springer International Publishing. doi:https://doi.org/10.1007/978-3-030-32381-3_16Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: an introduction. Cambridge, Mass: MIT Press.Sztyler, T., Huber, J., Noessner, J., Murdock, J., Allen, C., & Niepert, M. (2014). LODE: Linking digital humanities content to the web of data. In IEEE/ACM Joint Conference on Digital Libraries (pp. 423-424). doi:10.1109/JCDL.2014.6970206Tao, C., Song, D., Sharma, D., & Chute, C. G. (2013). Semantator: Semantic annotator for converting biomedical text to linked data. Journal of Biomedical Informatics, 46(5), 882-893. doi:10.1016/j.jbi.2013.07.003Trace, C. B., & Karadkar, U. P. (2017). Information management in the humanities: Scholarly processes, tools, and the construction of personal collections. Journal of the Association for Information Science and Technology, 68(2), 491-507. doi:10.1002/asi.23678Tsui, L. H., & Wang, H. (2020). Harvesting big biographical data for chinese history: The china biographical database (CBDB). Journal of Chinese History, 4(2), 505-511. doi:10.1017/jch.2020.21Tu, H. C., Hsiang, J., Hung, I. M., & Hu, C. (2020). DocuSky, A Personal Digital Humanities Platform for Scholars. Journal of Chinese History, 4(2), 564-580. doi:10.1017/jch.2020.28Vrandečić, D., & Krötzsch, M. (2014). Wikidata: A free collaborative knowledgebase. Commun. ACM, 57(10), 78-85. doi:10.1145/2629489Vukotic, A., Watt, N., Abedrabbo, T., Fox, D., & Partner, J. (2015). Neo4j in action. Shelter Island, NY: Manning Publications Co.Waltzer, L. (2012). Digital humanities and the “Ugly Stepchildren” of American higher education. In M. K. Gold (Ed.), Debates in the Digital Humanities (NED-New edition., pp. 335-349). University of Minnesota Press. Retrieved from https://www.jstor.org/stable/10.5749/j.ctttv8hq.22Webber, J. (2012). A programmatic introduction to Neo4j. In Proceedings of the 3rd annual conference on Systems, programming, and applications: software for humanity - SPLASH ’12 (p. 217). Tucson, Arizona, USA: ACM Press. doi:10.1145/2384716.2384777Wei, Q., Chen, Y., Salimi, M., Denny, J. C., Mei, Q., Lasko, T. A., et al. (2019). Cost-aware active learning for named entity recognition in clinical text. Journal of the American Medical Informatics Association, 26(11), 1314-1322. doi:10.1093/jamia/ocz102Wood, D., Zaidman, M., Ruth, L., & Hausenblas, M. (2014). Linked data: Structured data on the web. New York: Manning Publications.Xia, P., Zhang, L., & Li, F. (2015). Learning similarity with cosine similarity ensemble. Information Sciences, 307, 39-52. doi:10.1016/j.ins.2015.02.024Zeng, M. L. (2019). Semantic enrichment for enhancing LAM data and supporting digital humanities. Profesional De La Información, 28(1). doi:10.3145/epi.2019.ene.03 zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200280 en_US
