資訊科學

資訊科學zi1 seon3 fo1 hok6（英文：information science）泛指研究分析、儲藏同操控資訊嘅科學領域，例子有電腦科學、認知科學同圖書館學呀噉。

基礎概念

抽象啲講，資訊可以定義為「不確定性嘅減少」（reduction of uncertainty），山農佢係噉諗嘅：佢提議將資訊諗做一個包含咗多種可能性嘅集（set），傳資訊嘅人會用某啲訊號（signal）將資訊經由有雜音（noise）嘅管道傳出去；收資訊嗰個人跟手要由收到嘅訊號嗰度按照佢對嗰種訊號嘅理解重新建構返傳資訊嗰個人想表達嘅內容出嚟；同時，管道嘅雜音會搞到收資訊嗰個人唔能夠完完全全肯定佢收到嘅訊號真係完美反映傳資訊嗰個人想表達嘅內容，頂嗮櫳都只係做到盡力令出錯嘅機率有咁低得咁低^[1]。

例子

舉個例說明，想像家陣有一個黑盒，個盒裝住啲嘢，A 君唔知個盒裝咗乜（有不確定性），但佢識 B 君，而 B 君睇過個盒嘅內容；而又想像家陣 B 君用口講俾 A 君聽個盒裝咗啲乜，喺呢個過程當中，B 君要將自己嘅所思所想轉化做訊號－講嘢嘅聲（將資訊用訊號表達），而啲聲會由佢把口傳過去 A 君對耳仔嗰度，A 君個腦就會由聽到嘅聲嗰度解讀返 B 君想表達嘅內容出嚟（重新建構）；假設 B 君係靠得住嘅，A 君就可以透過收佢嘅訊號嚟到判斷嗰個盒嘅內容係乜（不確定性減少）。B 君講嘢向 A 君傳遞咗資訊，但係喺現實裏面，呢種做法好多時都係唔完全靠得住嘅（有雜音），例如可能佢哋兩個周圍嘅環境好嘈，搞到 A 君聽錯^[2]^[3]。

同樣嘅概念可以用嚟理解好多領域嘅研究：

喺做通訊嗰陣，傳訊嘅人會以某啲方法（好似係文字訊號或者摩斯碼呀噉）向收訊嘅人傳達訊號（將資訊用訊號表達），收訊嘅人收到訊號之後要按照佢對嗰種訊號嘅認識解讀傳訊嘅人想講嘅嘢（重新建構），並且知道對方想表達啲乜（不確定性減少）^[4]；
又或者用神經科學嚟做例子，對眼嘅視網膜上面每一粒感光細胞喺受到外界嘅光刺激嗰陣，會向個腦嘅視覺區域射神經訊號，而個訊號嘅物理性質（例如係頻率）會按照佢感覺到嘅光嘅屬性而有所不同－例如係光嘅強度同射訊號嘅頻率成簡單正比， $f=\beta I$ ，當中 $f$ 係訊號嘅頻率， $\beta$ 係一個固定嘅常數，而 $I$ 係光嘅強度（將資訊用訊號表達），呢啲訊號跟住會上腦，話俾個腦知佢睇到啲乜（可能有雜音嘅管道），而個腦本來唔知眼前有啲乜嘅（不確定性），但佢由感光細胞嗰度收到神經訊號，知道 $f$ ，再解讀個訊號得知 $I$ （重新建構），就知眼前有啲乜嘢（不確定性減少）^[5]。

知識表示

一個本體表達概念之間嘅關係（英文）。

知識表示（knowledge representation）係指表述知識嘅方法，喺認知心理學同人工智能上都係受注目嘅課題。

本體（ontology）就係一種知識表示方法^[6]：一個本體會好似幅附圖噉，包含大柞概念（幅附圖包括咗哺乳動物、鯨魚、同水等等），指明嗮每一對概念之間嘅關係－鯨魚「屬於」哺乳動物、鯨魚「住喺」水入面、哺乳動物「屬於」動物... 等等。本體式嘅知識表示法喺好多實用領域上都有用，例如係喺臨床醫學上幫手做決定^[7]、知識發現^[8]、同埋好多其他方面^[9]。於是學界發展咗網絡本體語言（Web Ontology Language）呢款程式語言專門攞嚟整本體^[10]。

以下呢段係一段用網絡本體語言表達嘅一個本體，用嚟描述一啲有關意大利薄餅嘅知識^[6]：

Namespace(p = <http://example.com/pizzas.owl#>)
Ontology( <http://example.com/pizzas.owl#>
   Class(p:Pizza partial
     restriction(p:hasBase someValuesFrom(p:PizzaBase)))
   DisjointClasses(p:Pizza p:PizzaBase)
   Class(p:NonVegetarianPizza complete
     intersectionOf(p:Pizza complementOf(p:VegetarianPizza)))
   ObjectProperty(p:isIngredientOf Transitive
     inverseOf(p:hasIngredient))
)

舉個例子說明，呢段碼 ObjectProperty 嗰行會同個程式指明個本體入面某啲物件嘅特性，呢行指令講咗幾樣嘢：首先，「係某某嘅原料」（isIngredientOf）呢個關係係有遞移性嘅（Transitive）－即係教部電腦，如果 A 係 B 嘅原料而 B 係 C 嘅原料，噉佢可以推斷 A 係 C 嘅原料；呢行指令仲表明咗「係某某嘅原料」係「原料包含咗」（hasIngredient）嘅反轉（inverse）－所以運用呢個本體嘅人工智能程式可以做（例如）「夏威夷薄餅原料包含咗菠蘿，所以菠蘿係夏威夷薄餅嘅原料」嘅推理。

睇埋

攷

↑ Urbany, J. E., Dickson, P. R., & Wilkie, W. L. (1989). Buyer uncertainty and information search. Journal of consumer research, 16(2), 208-215.
↑ Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4(3), 28-36.
↑ Weary, G., & Jacobson, J. A. (1997). Causal uncertainty beliefs and diagnostic information seeking. Journal of Personality and Social Psychology, 73(4), 839.
↑ Griffin, E. A. (2012). A first look at communication theory/Em Griffin. New York: McGraw-Hill.
↑ Chen, A. H., Zhou, Y., Gong, H. Q., & Liang, P. J. (2004). Firing rates and dynamic correlated activities of ganglion cells both contribute to retinal information processing. Brain research, 1017(1-2), 13-20.
↑ ^6.0 ^6.1 OWL Example with RDF Graph. Ontologies and Semantic Web.
↑ Kuperman, G. J.; Reichley, R. M.; Bailey, T. C. (1 July 2006). "Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations". Journal of the American Medical Informatics Association. 13 (4): 369–371.
↑ MCGARRY, KEN (1 December 2005). "A survey of interestingness measures for knowledge discovery". The Knowledge Engineering Review. 20 (1): 39.
↑ Bertini, M; Del Bimbo, A; Torniai, C (2006). "Automatic annotation and semantic retrieval of video sequences using multimedia ontologies". MM ‘06 Proceedings of the 14th ACM international conference on Multimedia. 14th ACM international conference on Multimedia. Santa Barbara: ACM. pp. 679–682.
↑ Sikos, Leslie F. (June 2017). Description Logics in Multimedia Reasoning. Cham: Springer.

[1] Urbany, J. E., Dickson, P. R., & Wilkie, W. L. (1989). Buyer uncertainty and information search. Journal of consumer research, 16(2), 208-215.

[2] Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4(3), 28-36.

[3] Weary, G., & Jacobson, J. A. (1997). Causal uncertainty beliefs and diagnostic information seeking. Journal of Personality and Social Psychology, 73(4), 839.

[4] Griffin, E. A. (2012). A first look at communication theory/Em Griffin. New York: McGraw-Hill.

[5] Chen, A. H., Zhou, Y., Gong, H. Q., & Liang, P. J. (2004). Firing rates and dynamic correlated activities of ganglion cells both contribute to retinal information processing. Brain research, 1017(1-2), 13-20.

[owlexample-6] 6.0 ^6.1 OWL Example with RDF Graph. Ontologies and Semantic Web.

[7] Kuperman, G. J.; Reichley, R. M.; Bailey, T. C. (1 July 2006). "Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations". Journal of the American Medical Informatics Association. 13 (4): 369–371.

[8] MCGARRY, KEN (1 December 2005). "A survey of interestingness measures for knowledge discovery". The Knowledge Engineering Review. 20 (1): 39.

[9] Bertini, M; Del Bimbo, A; Torniai, C (2006). "Automatic annotation and semantic retrieval of video sequences using multimedia ontologies". MM ‘06 Proceedings of the 14th ACM international conference on Multimedia. 14th ACM international conference on Multimedia. Santa Barbara: ACM. pp. 679–682.

[10] Sikos, Leslie F. (June 2017). Description Logics in Multimedia Reasoning. Cham: Springer.

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