語意網路與數位圖書館演講 概要與想法 bu 2010/06/10
什麼是語意  (Semantics) ? 語義指的是有意義的結構性資料,其資料能自已詮義自已的意義。 對於電腦來說,語意資料能夠讓電腦判斷資料間的關聯並推理出問題的結果 對於我們,所有生活中的資料都是有其意義的,所以我們的工作是在建立這些關聯讓...
但,如果我們生活中的資料都是有意義的? 為什麼還要去處理語意問題?
問題在於 ..... 不同需求對象所要使用的表現方式不同
比如說… 看看下面這張圖片 © 2010 劉威廷  http://album.blog.yam.com/show.php?a=weiting771229&f=7527221&i=13944427&p=89
但 ... 電腦需要的是 某人:……… 奇怪,這什麼鬼,這不是送舊晚會上那個學弟作的白痴舉動? ……… ……… 奇怪,這什麼鬼,這不是 送舊晚會 上 那個學弟 作的 白痴舉動 ? ……… {location: “送舊晚會”, main_topi...
人類可以很快從上面那段文字找到重點 但電腦卻不然 於是,這中間的過程  X  就是關鍵
再從先前那個例子中,我們可以發現… 以人類來說,我們處理的是:一段文字、圖像、視覺化過後的資料(如圖表)、影片……. 等 不同的對象,所用的表現方式真的有所不同 但電腦卻需要正式、定義明確、嚴謹的用詞
過程 X 資料萃取 Data Extraction
一段文字 嚴謹用詞 資料萃取 把一段文字找出其重點所在就是資料萃取的精神,這個過程是語意系統的基石,因為所有的資料都來自於這個過程。 資料萃取 萃取完的這些詞彙,可以被用以當作建置全文索引時的查詢結果進入點 萃取時,也要對於這些詞彙或取得的資料...
資料萃選要如何實作? 1.傳統人工進行處理 2.自動化操作(電腦進行全文索引後和關鍵字進行對映) 3.群眾的力量 (crowdsourcing)  ->網頁化的收集工具  ->方便使用者查詢以及建置的應用介面 (eg: Google Image...
從使用者的角度來說… 了解 A 了解 B 知識空間 由  KOS( 知識組織系統 ,eg: LCC,DDC)  系統所建構起來的一個虛擬的空間 對於一個問題的了解,是一個不斷在重覆的過程,使用者了解了起點問題A以後,會在知識空間裡找到另一個問題...
<ul>KOS Concept Hub </ul><ul><li>The backbone of the proposed system is a  faceted core classification of atomic concepts ...
Interoperability is achieved by  expressing concepts from all participating KOS  as a canonical representation: descriptio...
Mapping from KOS to KOS is achieved by  reasoning over these canonical representations </li></ul><ul></ul>
<ul>Method: How to get DL formulas </ul><ul>Key:  Efficient creation of canonical representations (DL formulas)  </ul><ul>...
Use  knowledge of KOS structure  for hierarchical inheritance
Use  linguistic analysis  of terms and captions
Eliminate redundant atomic concepts
Check or produce mapping results from  assignment of concepts to the same records
Get  human editors ’ input and verification where needed through a user-friendly interface
KOS “owners” may verify and edit data  pertaining to their KOS </li></ul><ul></ul>
<ul>Knowledge base </ul><ul>Requires  an ever larger classification and lexical knowledge base containing many kinds of da...
Linguistic knowledge bases such as Wordnet and mono-,bi-, and multi-langual dictionaries and thesauri
Many KOS (Knowledge Organization Systems), such as LCC, DDC, DMOZ directory, LCSH, Gene Ontology, Schlagwortnormdatei
These will over time be fused into one large multilingual knowledge base with many terminological and translation relation...
於是系統可以建置一些功能或方法來協助使用者減少這中間造成的時間或查詢成本 1. 問題分類(Problem anylaze and cataloging):使用軟體介面,協助使用者去對於自已的問題的範圍以及面向進行定義。 J01家庭問題 J02 ...
方法三:使用面向(Facet)組合法協助資料組織 比如說下面這個例子: 障礙程度 B1 輕度 B2 中度 B3 重度 B4 完全失去功能 器官 A1 大腦 A2 心臟 A3 耳朵 A4 眼睛 A5 手 輕度智障 重度聽障 失明 A1B1 A3B...
如果有一個系統,能結合以上幾點呢? 這就是由 Soergel 教授所提出的一個架構
<ul>Objective </ul><ul>Improve semantic-based search of digital content  across multiple collections in multiple languages...
Support for search, esp. facet-based search  </li></ul><ul><ul><li>for any collection indexed by a participating KOS
for free-text search </li></ul></ul><ul><li>Assistance in cataloging (metadata creation)  by catalogers or users (social t...
Long-range goal: Web service where a KOS can be uploaded  and mappings to specified target KOS are returned </li></ul><ul>...
在  Soergel  教授的設計下,這個整體的查詢模式會變成這樣 (可能有誤,這是我自已的理解,原演講這部份的介紹很鬆散) 使用面向組合法把相關的議題組合起來 使用不同面向的問題面向來把問題明確定義 這裡會進行幾個動作: <ul><li>對映
轉譯
加值 </li></ul>經過概念池的加值再進行查詢後,結果可以進行更深入的抽換或更動,可以從不同面向,不同切入點使用。 重點 介面 概念池 Hub 加值後的查詢結果
介面 查詢介面是使用者第一個接觸到的東西,於是這裡的查詢方式就需要更加的方法,才能減少使用者查找的時間。 就 Soergel 教授的想法,這裡可能會長得像這樣: 查詢 請拖拉您覺得合切的關鍵字或自行輸入 你知不知道這和什麼人有關? 你知不知道這...
概念池 取自www.dsoergel.com/MappingConceptHubBerlin.ppt  <ul>Hub Water transport Inland water transport Ocean transport Traffic...
<ul>L00 Transportation and traffic </ul><ul><ul><li>L10 Traffic system components  </li></ul></ul><ul><ul><ul><li>L13 Traf...
P27 Architecture
P43 Construction </li></ul></ul><ul>R00 Engineering </ul><ul><ul><li>R30 Acoustics
R37 Soundproofing </li></ul></ul><ul>T70 Military vs. civilian </ul><ul><ul><li>T73 Military
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語意網路與數位圖書館演講

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bu 邱柏崴的心得想法以及整理of Seorgel 教授的理論性框架

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  • 語意網路與數位圖書館演講

    1. 1. 語意網路與數位圖書館演講 概要與想法 bu 2010/06/10
    2. 2. 什麼是語意 (Semantics) ? 語義指的是有意義的結構性資料,其資料能自已詮義自已的意義。 對於電腦來說,語意資料能夠讓電腦判斷資料間的關聯並推理出問題的結果 對於我們,所有生活中的資料都是有其意義的,所以我們的工作是在建立這些關聯讓電腦能了解我們的想法
    3. 3. 但,如果我們生活中的資料都是有意義的? 為什麼還要去處理語意問題?
    4. 4. 問題在於 ..... 不同需求對象所要使用的表現方式不同
    5. 5. 比如說… 看看下面這張圖片 © 2010 劉威廷 http://album.blog.yam.com/show.php?a=weiting771229&f=7527221&i=13944427&p=89
    6. 6. 但 ... 電腦需要的是 某人:……… 奇怪,這什麼鬼,這不是送舊晚會上那個學弟作的白痴舉動? ……… ……… 奇怪,這什麼鬼,這不是 送舊晚會 上 那個學弟 作的 白痴舉動 ? ……… {location: “送舊晚會”, main_topic:{actor: '那個學弟', action: '白痴舉動'}} 過程X
    7. 7. 人類可以很快從上面那段文字找到重點 但電腦卻不然 於是,這中間的過程 X 就是關鍵
    8. 8. 再從先前那個例子中,我們可以發現… 以人類來說,我們處理的是:一段文字、圖像、視覺化過後的資料(如圖表)、影片……. 等 不同的對象,所用的表現方式真的有所不同 但電腦卻需要正式、定義明確、嚴謹的用詞
    9. 9. 過程 X 資料萃取 Data Extraction
    10. 10. 一段文字 嚴謹用詞 資料萃取 把一段文字找出其重點所在就是資料萃取的精神,這個過程是語意系統的基石,因為所有的資料都來自於這個過程。 資料萃取 萃取完的這些詞彙,可以被用以當作建置全文索引時的查詢結果進入點 萃取時,也要對於這些詞彙或取得的資料,進行結構的整理以及建立相互的關聯。 傳統我們在對於文章內容進行摘要、編索引,就是一種進行資料萃取的行為。
    11. 11. 資料萃選要如何實作? 1.傳統人工進行處理 2.自動化操作(電腦進行全文索引後和關鍵字進行對映) 3.群眾的力量 (crowdsourcing)  ->網頁化的收集工具  ->方便使用者查詢以及建置的應用介面 (eg: Google Image Labeler) 開始前的準備工作:在傳統分類中有愈多的資料,對於進行萃選就愈有幫助。
    12. 12. 從使用者的角度來說… 了解 A 了解 B 知識空間 由 KOS( 知識組織系統 ,eg: LCC,DDC) 系統所建構起來的一個虛擬的空間 對於一個問題的了解,是一個不斷在重覆的過程,使用者了解了起點問題A以後,會在知識空間裡找到另一個問題 B ,再從它找到另一個問題 C ,如此一般的不斷在有限的知識範圍內進行問題的探索
    13. 13. <ul>KOS Concept Hub </ul><ul><li>The backbone of the proposed system is a faceted core classification of atomic concepts together with a set of relationships
    14. 14. Interoperability is achieved by expressing concepts from all participating KOS as a canonical representation: description logic formula using atomic concepts and relationships
    15. 15. Mapping from KOS to KOS is achieved by reasoning over these canonical representations </li></ul><ul></ul>
    16. 16. <ul>Method: How to get DL formulas </ul><ul>Key: Efficient creation of canonical representations (DL formulas) </ul><ul><li>Apply existing knowledge : Large knowledge base ▬► less effort for processing a new KOS
    17. 17. Use knowledge of KOS structure for hierarchical inheritance
    18. 18. Use linguistic analysis of terms and captions
    19. 19. Eliminate redundant atomic concepts
    20. 20. Check or produce mapping results from assignment of concepts to the same records
    21. 21. Get human editors ’ input and verification where needed through a user-friendly interface
    22. 22. KOS “owners” may verify and edit data pertaining to their KOS </li></ul><ul></ul>
    23. 23. <ul>Knowledge base </ul><ul>Requires an ever larger classification and lexical knowledge base containing many kinds of data: </ul><ul><li>A faceted classification of atomic concepts Seeded from sources with well-developed facets such as the AOD Thesaurus, the Harvard Business Thesaurus, the Art and Architecture Thesaurus, various ontologies
    24. 24. Linguistic knowledge bases such as Wordnet and mono-,bi-, and multi-langual dictionaries and thesauri
    25. 25. Many KOS (Knowledge Organization Systems), such as LCC, DDC, DMOZ directory, LCSH, Gene Ontology, Schlagwortnormdatei
    26. 26. These will over time be fused into one large multilingual knowledge base with many terminological and translation relationships and relationships linking terms to concepts, with an increasing number of concepts semantically represented by a DL formula. </li></ul><ul></ul>
    27. 27. 於是系統可以建置一些功能或方法來協助使用者減少這中間造成的時間或查詢成本 1. 問題分類(Problem anylaze and cataloging):使用軟體介面,協助使用者去對於自已的問題的範圍以及面向進行定義。 J01家庭問題 J02 親子問題 J03  教養問題 J04 法律問題 2. 知識體系建築:從概有的分類表下手 從現有的分類表進行知識體系的建築,使得在這其中瀏覽的使用者,可以從這個主題明確平滑的轉移或找到他所想要的面向或議題。這個體系建立起後,可以讓不清楚自已明確想要的東西所在何處的使用者找到他想要的資料。 EXAMPLE: 由於 Wikipedia 從2002年發展至今,其內容已相當龐大,在查找或瀏覽上有其困難度。於是,目前有幾個由不同人發起的計畫,想用 UDC (通用圖書分類法)來把 Wikipedia 的文章進行分類,來提供一個清楚的知識體系給使用者。
    28. 28. 方法三:使用面向(Facet)組合法協助資料組織 比如說下面這個例子: 障礙程度 B1 輕度 B2 中度 B3 重度 B4 完全失去功能 器官 A1 大腦 A2 心臟 A3 耳朵 A4 眼睛 A5 手 輕度智障 重度聽障 失明 A1B1 A3B3 A4B4
    29. 29. 如果有一個系統,能結合以上幾點呢? 這就是由 Soergel 教授所提出的一個架構
    30. 30. <ul>Objective </ul><ul>Improve semantic-based search of digital content across multiple collections in multiple languages . </ul><ul><li>Interoperability between any two participating KOS (Knowledge Organization Systems)
    31. 31. Support for search, esp. facet-based search </li></ul><ul><ul><li>for any collection indexed by a participating KOS
    32. 32. for free-text search </li></ul></ul><ul><li>Assistance in cataloging (metadata creation) by catalogers or users (social tagging)
    33. 33. Long-range goal: Web service where a KOS can be uploaded and mappings to specified target KOS are returned </li></ul><ul></ul>
    34. 34. 在 Soergel 教授的設計下,這個整體的查詢模式會變成這樣 (可能有誤,這是我自已的理解,原演講這部份的介紹很鬆散) 使用面向組合法把相關的議題組合起來 使用不同面向的問題面向來把問題明確定義 這裡會進行幾個動作: <ul><li>對映
    35. 35. 轉譯
    36. 36. 加值 </li></ul>經過概念池的加值再進行查詢後,結果可以進行更深入的抽換或更動,可以從不同面向,不同切入點使用。 重點 介面 概念池 Hub 加值後的查詢結果
    37. 37. 介面 查詢介面是使用者第一個接觸到的東西,於是這裡的查詢方式就需要更加的方法,才能減少使用者查找的時間。 就 Soergel 教授的想法,這裡可能會長得像這樣: 查詢 請拖拉您覺得合切的關鍵字或自行輸入 你知不知道這和什麼人有關? 你知不知道這和什麼事有關? blue(?) Blue 這個字具有歧義,請從下面選出最切合你的選項。 Blue (adj) 一種色彩 Blue (adj) 一種心情 Blues (n) 一種音樂風格 協助使用者在不同的問題面向中填入查詢詞,在可能發生岐義的地方進行協助 概念詞 老鼠 老鼠
    38. 38. 概念池 取自www.dsoergel.com/MappingConceptHubBerlin.ppt <ul>Hub Water transport Inland water transport Ocean transport Traffic station ⊓ Water transport Traffic station ⊓ Inland water tr. Traffic station ⊓ Ocean transport </ul><ul>Dewey 387 Water, air, space transportation 386 Inland waterway & ferry transportation 387.5 Ocean transportation 386.8 Inland waterway tr. > Ports 387.1 Ports </ul><ul>LCSH Shipping Inland water transport Merchant marine Harbors </ul><ul>German Hafen </ul>
    39. 39. <ul>L00 Transportation and traffic </ul><ul><ul><li>L10 Traffic system components </li></ul></ul><ul><ul><ul><li>L13 Traffic facilities </li></ul></ul></ul><ul><ul><ul><ul><li>L15 Traffic stations </li></ul></ul></ul></ul><ul><ul><ul><li>L17 Vehicles </li></ul></ul></ul><ul><ul><li>L30 Modes of transportation </li></ul></ul><ul><ul><ul><li>L33 Air transport </li></ul></ul></ul><ul>L37 Water transport </ul><ul>P00 Buildings, construction </ul><ul><ul><li>P23 Buildings
    40. 40. P27 Architecture
    41. 41. P43 Construction </li></ul></ul><ul>R00 Engineering </ul><ul><ul><li>R30 Acoustics
    42. 42. R37 Soundproofing </li></ul></ul><ul>T70 Military vs. civilian </ul><ul><ul><li>T73 Military
    43. 43. T77 Civilian </li></ul></ul><ul></ul><ul>Underlying faceted classification </ul>
    44. 44. <ul>HE Transportation HE550-560 Ports, harbors, docks, wharves, etc. </ul><ul>L00 Transportation and traffic ⊓ T77 Civilian Inherited: L00 Transportation and traffic ⊓ T77 Civilian Added by editor : L15 Traffic stations ⊓ L37 Water transport Resolved to : L15 Traffic stations ⊓ L37 Water transport⊓ T77 Civilian </ul><ul></ul><ul>Method: Assigning atomic concepts 1 </ul>
    45. 45. <ul>NA6300-6307 Airport buildings </ul><ul>From database already established : Airport = L15 Traffic stations ⊓ L33 Air transport Buildings = P23 Buildings Added by editor T77 Civilian Resolved to L15 Traffic stations ⊓ L33 Air transport ⊓ P23 Buildings ⊓ T77 Civilian </ul><ul></ul><ul>Method: Assigning atomic concepts 2 </ul>
    46. 46. <ul>TL681.S6 Airplanes. Soundproofing </ul><ul>From database already established : Airplane = L17 Vehicles ⊓ L33 Air transport Soundproofing = R37 Soundproofing Added by editor : Nothing Resolved to L17 Vehicles ⊓ L33 Air transport ⊓ R37 Soundproofing </ul><ul></ul><ul>Method: Assigning atomic concepts 3 </ul>
    47. 47. <ul>Aeroplanes-Soundproofing </ul><ul>From database already established : Aeroplanes = Airplane [Spelling variant] Therefore Term is recognized as same as Airplanes. Soundproofing Resolved to L17 Vehicles ⊓ L33 Air transport ⊓ R37 Soundproofing </ul><ul></ul><ul>Method: Assigning atomic concepts 4 </ul>
    48. 48. <ul>Any class formed by geographical subdivision Such as NA6300-6307 Airport buildings NA6305.E3 Egypt </ul><ul>Recognized using a dictionary of geographical names Inherits from subject class above it; simply add the country L15 Traffic stations ⊓ L33 Air transport ⊓ P23 Buildings ⊓ T77 Civilian ⊓ Egypt No editor checking needed </ul><ul></ul><ul>Method: Assigning atomic concepts 5 </ul>
    49. 49. <ul>Examples from the resulting knowledge base </ul><ul></ul>
    50. 50. <ul>HE550-560 Ports, harbors, docks, wharves, etc. NA2800 Architectural acoustics NA6300-6307 Airport buildings NA6330 Dock buildings, ferry houses, etc. TC350-374 Harbor works TH1725 Soundproof construction TL681.S6 Airplanes. Soundproofing TL725-726 Airways (Routes). Airports and landing fields. Aerodromes VA67-79 Naval ports, bases, reservations, docks VM367.S6 Submarines. Soundproofing </ul><ul>= L15 Traffic stations ⊓ L37 Water transport ⊓ T77 Civilian = P27 Architecture ⊓ R30 Acoustics = L15 Traffic stations ⊓ L33 Air transport ⊓ P23 Buildings ⊓ T77 Civilian = L15 Traffic stations ⊓ L37 Water transport ⊓ P23 Buildings ⊓ T77 Civilian = L15 Traffic stations ⊓ L37 Water transport ⊓ R00 Engineering ⊓ T77 Civilian = P23 Buildings ⊓ P43 Construction ⊓ R37 Soundproofing = L17 Vehicles ⊓ L33 Air transport ⊓ R37 Soundproofing = L13 Traffic facilities ⊓ L33 Air transport ⊓ Technical aspects = L15 Traffic stations ⊓ L37 Water transport ⊓ T73 Military = L17 Vehicles ⊓ L37 Water transport ⊓ R37 Soundproofing ⊓ T73 Military ⊓ Underwater </ul><ul></ul>
    51. 51. <ul>Aeroplanes-Soundproofing Airports-Buildings Buildings-Soundproofing Ships-Soundproofing </ul><ul>= L17 Vehicles ⊓ L33 Air transport ⊓ R37 Soundproofing = P23 Buildings ⊓ L15 Traffic stations ⊓ L33 Air transport = P23 Buildings ⊓ P43 Construction ⊓ R37 Soundproofing = L17 Vehicles ⊓ L37 Water transport ⊓ R37 Soundproofing </ul><ul></ul><ul>LC subject headings with combinations of atomic concepts </ul>
    52. 52. <ul></ul><ul>Hub L17 Vehicles ⊓ L33 Air transport ⊓ R37 Soundproofing </ul><ul>L17 Vehicles ⊓ L37 Water transport ⊓ R37 Soundproofing </ul><ul>L17 Vehicles ⊓ L37 Water transport ⊓ R37 Soundproofing ⊓ T73 Military⊓ </ul><ul>Underwater </ul><ul>LCC TL681.S6 Airplanes. Soundproofing </ul><ul>VM367.S6 Submarines. Soundproofing </ul><ul>LCSH Aeroplanes-Soundproofing </ul><ul>Ships-Soundproofing </ul><ul>Mapping through a Hub </ul>
    53. 53. <ul></ul><ul>Hub Canonical form of query (DL formula) </ul><ul>User query Free text Combination of elemental concepts through facets (guided query formulation) Controlled term(s) from a KOS, possibly found through browsing a KOS </ul><ul>Final query (Enriched) free text query Query in terms of a KOS </ul><ul>Mapping user queries </ul>
    54. 54. <ul>TL681.S6 Airplanes. Soundproofing VM367.S6 Submarines. Soundproofing Aeroplanes-Soundproofing Ships-Soundproofing </ul><ul>[L17 Vehicles ⊓ L33 Air transport ⊓ R37 Soundproofing] [L17 Vehicles ⊓ L37 Water transport ⊓ R37 Soundproofing ⊓ Military]   [L17 Vehicles ⊓ L33 Air transport ⊓ R37 Soundproofing] [L17 Vehicles ⊓ L37 Water transport ⊓ R37 Soundproofing] </ul><ul></ul><ul>Query: L17 Vehicles AND R37 Soundproofing </ul>
    55. 55. <ul>Examples from NALT and LCSH </ul><ul><li>NALT National Agricultural Library Thesaurus
    56. 56. LCSH Library of Congress Subject Headings </li></ul><ul></ul>
    57. 57. <ul>Air pollution laws </ul><ul>LCSH term Air – Pollution – Laws and regulations [isa] Legal rule [appliedTo] {[isa] Condition [isConditionOf] Air [causedBy] Pollutant [property] Undesirable} NALT terms Air pollution [isa] Condition [isConditionOf] Air [causedBy] Pollutant [property] Undesirable Laws and regulations [isa] Legal rule Mapping LCSH ▬► NALT Air – Pollution – Laws and regulations ▬► Air pollution AND Laws and regulations Interpretation for indexing and searching in both directions </ul><ul></ul>
    58. 58. <ul>Soil moisture vs. Soil water </ul><ul>LCSH term Soil moisture [isa] Water [containedIn] Soil NALT term Soil water [isa] Water [containedIn] Soil Mapping LCSH ▬► NALT Soil moisture ▬► Soil water </ul><ul></ul>
    59. 59. <ul>Greenhouse gardening </ul><ul>LCSH term Greenhouse gardening [isa] Gardening [inEnvironment] Greenhouse [inEnvironment] Home NALT terms Home gardening [isa] Gardening [inEnvironment] Home Greenhouse [isa] Greenhouse Mapping LCSH ▬► NALT Greenhouse gardening ▬► Home gardening AND Greenhouse </ul><ul></ul>
    60. 60. <ul>Salad greens </ul><ul>LCSH term Salad greens [isa] Green leafy vegetable [usedFor] Salad NALT term Green leafy vegetables [isa] Green leafy vegetable Mapping LCSH ▬► NALT Salad greens ▬► BT Green leafy vegetables </ul><ul></ul>
    61. 61. <ul>Emerging diseases </ul><ul>LCSH term Emerging infectious diseases [isa] Disease [hasProperty] Infectious [hasProperty] Emerging NALT term Emerging diseases [isa] Disease [hasProperty] Infectious ??? [hasProperty] Emerging Mapping LCSH ▬► NALT Emerging infectious diseases ▬► Emerging diseases Emerging infectious diseases ▬► BT Emerging diseases </ul><ul></ul>
    62. 62. <ul>Distributed implementation </ul><ul><li>A KOS on the Web could assign DL formulas to its concepts − let's call this a semantically enhanced KOS or SEKOS
    63. 63. Could use any of a number of faceted core classifications or even several (using a unique URI for each elemental concept)
    64. 64. Core classifications could be mapped to each other
    65. 65. It is now a simple matter to map from any SEKOS to any other (somewhat dependent on the core classifications used) </li></ul><ul></ul>
    66. 66. 總結以上一句,也是原演講的結論: 語意給系統帶來了生命力

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