Principles of Information Science Chapter 6 Information processing cognition Knowledge Theory
Principles of Information Science Chapter 6 Information Processing & Cognition: Knowledge Theory
1. Information Processing Definition of Information processing: All operations exerted to the information itself for its better utilization Information processing is necessary because most of the information in its original form may not be good for use. Thus information processing is the basis of later operations Two categories of information processing 1)Shallow Level of Information Processing 2)Deeper Level of Information Processing
1. Information Processing Definition of Information processing: All operations exerted to the information itself for its better utilization. Information processing is necessary because most of the information in its original form may not be good for use. Thus information processing is the basis of later operations. Two categories of information processing: 1) Shallow Level of Information Processing 2) Deeper Level of Information Processing
Shallow level of Information Processing 1. Processing for better operation information transformation expression 2. Processing for better transferring: communication exchange 3. Processing for better maintaining: record storage 4. Processing for better sharing copying reproduction 5. Processing for better retrieval classification, ordering, indexing
Shallow Level of Information Processing 1. Processing for better operation: information transformation & expression 2. Processing for better transferring: communication & exchange 3. Processing for better maintaining: record & storage 4. Processing for better sharing: copying & reproduction 5. Processing for better retrieval: classification, ordering, indexing
Deeper level of Information processing 1. Processing for higher efficiency: compression based on syntax and semantics 2. Processing for improving noise immunity error correction based on syntax semantics 3. Processing for purifying recognition filtering 4. Processing for security improving cryptography based on syntax semantics 5. Processing for better utility prediction, search, inference, computation
Deeper Level of Information Processing 1. Processing for higher efficiency: compression based on syntax and semantics 2. Processing for improving noise immunity: error correction based on syntax & semantics 3. Processing for purifying: recognition & filtering 4. Processing for security improving cryptography based on syntax & semantics 5. Processing for better utility: prediction, search, inference, computation
Information Cognition The purpose of information cognition is to produce knowledge from information refining. Information: The state its varying manner. Knowledge: The states their varying laws Cognition Information Knowledge
Information Cognition The purpose of information cognition is to produce knowledge from information refining. Cognition Information Knowledge Information: The state & its varying manner. Knowledge: The states & their varying laws
2. Knowledge Theory Knowledge can only be the product of epistemological information Classification of Knowledge Formal Knowledge deals with the morphological relation of the object Content Knowledge deals with the logical relation Utility Knowledge deals with the value relation to the subject
2. Knowledge Theory Classification of Knowledge Formal Knowledge -- deals with the morphological relation of the object Content Knowledge -- deals with the logical relation Utility Knowledge -- deals with the value relation to the subject Knowledge can only be the product of epistemological information
Representation of Knowledge The elements of representation States Manner of the states varying States: Numbering description Manner. Deterministic: functions, equations Random: probability, stochastic process Fuzy: grade of membership Chaotic: non-linear differential equation
Representation of Knowledge The elements of representation: -- States -- Manner of the states varying States: -- Numbering & description Manner: -- Deterministic: functions, equations -- Random: probability, stochastic process -- Fuzzy: grade of membership -- Chaotic: non-linear differential equation
Measures of Knowledge Unit Defining--al(奥特) The amount of a standard X 1/2 unit problem the amount of a balanced X alternative problem. X The relation between alt and bit bit - information unit alt -- knowledge unit
Measures of Knowledge Unit Defining -- alt (奥特) X X X 1 2 1/2 1/2 The amount of a standard unit problem -- the amount of a balanced alternative problem. The relation between alt and bit: bit -- information unit alt -- knowledge unit
Descriptive parameters Formal Knowledge Certainty: C=cnl nE(1, Ni Probability (pn) Possibility(gn Fuziness(μn) Content Knowledge Logic Truth: t=tnlne(l, ni Utility Knowledge Utility:U={unn∈(1,N)
Formal Knowledge Descriptive Parameters Certainty: C = {c | n (1, N)} Probability (p ) Possibility (q ) Fuzziness (m ) Content Knowledge Logic Truth: T = {t | n (1, N)} n n n n n Utility Knowledge Utility: U = {u |n (1, N)} n
Integrative Parameters s=(a cn) (B tn Integrative truth s=(acn). (Btn) (un) Integrative utility simpl olest cases, 5n=aCnβtn =aCnβtny
z = (a c )·(b t ) Integrative truth Integrative Parameters n n n x = (ac )·(bt )·(gu ) Integrative utility n n n n In simplest cases, z = ac bt n n n x = ac bt gu n n n n