在 ASIS&T Bulletin April/May 2015 special section : "Linked Data and the Charm of Weak Semantics" (
PDF) (鏈結資料和弱語意的魅力)，其中一篇文章是 Bibframe (BF) 負責廠商 Zepheira 公司總裁 Eric Miller 寫的 Linked Data Design for the Visible Library [1] ，談到 BF 設計理念。一些重點如下：
◆圖書館面臨最大問題之一是豐富的書目資料在網路上沒有什麼可見度(即搜尋引擎蒐尋不到、不會顯示給大眾使用者)。因此因應現代網路環境，書目資料須建立成為鏈結資料(linked data)，如此才能與一般網路資料立於同等立足點，且能被查詢者看到。[2]
◆BF具擴充性/彈性：需要者可在核心層之外，增加層次以達擴充的目的。 (BIBFRAME users can build layers on the core to support description of books, periodicals, audiovisual material, digital publications and much more. …Different users and communities can layer additional types of things on BIBFRAME, organized into profiles, which can be conceptually broad (a profile for cataloging print books) or narrow (a profile for cataloging rare books or manuscripts). [3]
◆FRBR複雜觀念架構使得圖書館界建置linked data的步伐遲滯。BF簡化此，BF參考了較簡化的
DC 格式；另外，BF基於開源軟體(based on our open-source BIBFRAME software, but with some training-specific additions)。
◆網上可見度主要賴於有用途內容的連結(連結網)，而非完美的詞彙集。(In the context of visibility there is little point having a page with the perfect vocabulary for some purposes if it is not also part of a rich network of links)
◆BF採 "
literate modeling" 方式：
In the BIBFRAME mindset the human-readable descriptions of the things being modeled are actually their most important core properties, and the natural associations users might make between one thing and another, however informal or imprecise, are essential for discovery and ultimately for visibility. These natural associations are more important than the strictness of the data structure or the formalized semantic interpretation of such a data structure. (人們自然有的聯想能力比嚴格的資料結構要來的更重要) This approach is called
literate modeling because it emphasizes data relationships that will be primarily consumed and contextualized by people. (literate modeling 重視的是資料間的關係，這種關係主要由人們所建構的)
◆由圖書館界與技術開發者共同建立初期架構，再由館員或相關人員進行測試，之後再予以修正，直到確定其有足夠用途才定案。
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同期刊的另篇Karen Coyle 寫的Simplicity in Data Models，也呼應了 weak semantics，其中一些文字也滿有意思的，如：我們走在街上看到一隻貓，會講："嘿！看那隻貓"，而非 "看那四隻腳的哺乳動物"。[我想是因人們有時太緊守科技邏輯，卻不夠了解人性？ ]
The Semantic Web protocol Resource Description Framework (RDF) comes out of work of the artificial intelligence (AI) community, which has an entirely different approach from that of previous information technology. Although machine intelligence and human intelligence are significantly different, AI attempts to model human thinking rather than the business processes that were the primary motivator for developments in previous information technology design. The study of human cognition has many facets, one of which revolves around our use of common concepts to understand the world. We walk down the street and say “Hey, look at that cat!” If instead we said “Look at that four-legged mammal” we would be considered either strange or facetious. Many conceptual categories are not exclusive.
註
[1] Libhub Initiative 應就是 Visible Library Beta (
http://www.libhub.org/) 是BF 的實際應用先導計畫，由Zepheira主導
[2] 如下圖，圖取自 BIBFRAME: Why? What? Who? p. 9
http://www.loc.gov/aba/pcc/bibframe/bibframe-and-pcc.html
[3] 參見 Bibframe Lite 等
http://bibfra.me/
