Result: 圖書出版產業資料洞見指標與 關鍵詞圖譜之建構: 整合設計思考、 語意嵌入與視覺化分析.
Further Information
As intelligent transformation and data-driven decision-making have become increasingly central to digital industries, the book publishing sector faces critical challenges in semantic understanding and knowledge structuring. This study proposes an integrated methodology for constructing a semantic graph of book publishing data insight indicators and keywords, with a particular focus on three core dimensions of the publishing process: topic planning, content production, and marketing/distribution. The research process begins with the collection of relevant terms through a literature review and design thinking sessions. The data insight indicators are subsequently refined and validated using the Delphi method. Semantic embedding models are then employed to compute similarity scores between indicators and keywords, facilitating the semantic translation, alignment, and validation of concepts. The semantic graph is visualized using Python, in combination with Plotly and Pyvis, to illustrate the distribution of semantic nodes and their relational structure. The resulting semantic graph of data insight indicators and keywords in the book publishing industry reveal latent relationships among key publishing concepts. It enables a more precise understanding of editorial decision-making, content development, and user-targeted marketing strategies. This proposed semantic framework not only provides a data-informed foundation for publishing decisions but also establishes a scalable infrastructure for future applications in intelligent publishing. [ABSTRACT FROM AUTHOR]
因應智慧轉型與資料驅動決策需求, 圖書出版產業面臨語意理解 與知識結構化挑戰。本研究針對出版流程中之選題企劃、內容產 製與行銷發行三大維度, 發展出版語意圖譜建構流程, 整合設計 思考語彙、德爾菲法專家意見與語意嵌入技術, 建構資料洞見指 標與關鍵詞之語意結構模型。研究過程中, 先透過文獻探討與設 計思考會議蒐集語彙, 經由德爾菲法確認資料洞見指標; 再以語 意嵌入模型計算相似度, 連結洞見指標與關鍵詞, 並完成語意 轉譯與驗證; 最終運用Python語言結合Plotly與Pyvis製作互動式 視覺圖譜, 展現語意節點分布以及其間的語意關聯結構。結果顯 示, 語意圖譜有效揭示潛藏於資料之語意脈絡, 並可支援出版策 略決策與知識結構分析, 對出版產業智慧化具有參考價值。 [ABSTRACT FROM AUTHOR]