王振鵠教授被認為是影響臺灣圖書館領域深遠的學者，其在任職國家圖書館館長期間建樹良多，同時也培養出許多傑出人才。本研究運用知識本體和鏈結資料技術，分析王教授的生平事蹟與學術貢獻，設計出一套總計26 個類別、29 項屬性、720 件實例與3,645 條三元組的知識本體，用來描述王教授的事業歷程，並據此建置描述臺灣圖書資訊學領域學者事業歷程的網站，除提供瀏覽、搜尋、關聯等功能之外，亦能透過取用或下載知識本體，使本網站成為鏈結資料的提供者。為了測試網站效率，本研究召募74 名圖書資訊學研究生與圖書館館員進行四項驗證知識本體關聯性的任務測試，測試結果顯示，在一般性任務的任務一與任務二當中，本研究網站明顯快於對照組的傳統網站；在搜尋語意經過自然語言特殊處理的任務三中則兩者無異；而需要使用者應用超連結的任務四則結果相反。研究結果顯示若要提升本研究知識本體網站的有用性，仍需在介面上有所改善。未來除針對介面加以改善外，還期望能擴展知識本體的收納範圍，建立領域研究者的知識本體。

Professor Chen‐Ku Wang is considered to be one of the most influential personage for librarianship in Taiwan. He achieved great accomplishments during his terms as the Director General of National Central Library; meanwhile, he educated many outstanding students. This study exploits ontologies and Linked Data to analyze Professor Wang’s important events and achievements in his professional life. A resultant ontology comrpsing 26 classes, 29 attributes, 720 instances, and 3,645 triples were established to portray the career path of Professor Wang. A website that empowered users to browse and search the ontology and find relationships between ontology entities was created accordingly. It offers the utitlization and download of the ontology, and in this manner, the website can also play the role of a Linked Data provider. To test the efficacy of the website, 74 participants in the field of library and information were recruited for four tasks to verify the relevance of ontology. The result showed that in the first two general tasks, the website developed by this study was significantly faster than the traditional website. The task statement of Task 3 was specially processed by natural language; therefore, there was no difference in the results. Task 4 required users to retrieve answers through hyperlinks, and the test result showed that the traditional website is faster. The results of this study reveals that our ontology‐based website needs to be refined for improving the ontology utilization, and which is one important future work of this study. Other future works include expanding the scope of the ontology, and apply the ontology to other LIS or non‐LIS scholars.

王振鵠；圖書資訊學；鏈結資料；知識本體；語意網

With the rapid growth of information resources for cultural heritage images and the development of Digital Humanities research, Deep Semantic Indexing (DSI), which aims at semantic indexing of cultural heritage images, has gradually attracted more and more attentions. DSI can improve not only the efficiency of image retrieval and acquisition, but also the user understanding of the images. It can support the integration of image resources and automatic knowledge discovery, which has important theoretical and practical significance. The study conducted throughout analysis of semantic features and themes of cultural heritage images and reviewed the existing cultural heritage metadata models and ontologies. Based on the understanding of the concept of DSI and its basic requirements, we designed the workflow and technological process of DSI, constructed the cultural heritage image semantic indexing model, including an inclusive concept model, an multi‐layered information model, and a structural model of the indexing texts. We also conducted an indexing experiment of the Dunhuang mural “Nine‐colored Deer”. The DSI modeling of images reveals the semantic relationship between concepts, images and text, mines the knowledge correlation between each information layer related to image indexing, and realizes the fine‐grained organization of image information units. At the same time, the indexing experiment verified the feasibility and scientificity of cultural heritage image DSI structure. The design and implementation in DSI of cultural heritage image information is an advancement of the deep semantic indexing theory and image information organization theory. The decision on image indexing’s granularity and extensibility should be based on the indexing contents. The integration of DSI information and the publishing of such information will be studied further in the future.