2.1 Basic concepts of Knowledge Engineering

2.1.1 Data, Information, Knowledge and Wisdom 数据、信息、知识与智慧

Data数据

It is the measures and representation of the world. 世界的计量和表征。
It is expressed as fact, signal, or symbol. 表现为事实、信号、或者符号。

Information 信息

It is produced by assigning meaning to data. 信息是对数据赋予含义而生成。

• Types: Structural vs. functional, subjective vs. objective.
  信息的类型：有结构的与功能的，主观的与客观的。

Knowledge知识

It is formed by processing the information. 知识是对信息进行加工而形成的。
It is expressed as processed, procedural, or propositional.对信息表现为加工的、过程的或者命题的知识。

Wisdom 智慧

It is the experience to make decisions and judgments. 智慧是作出明智的决定和判断的经验。
It is expressed as “know-why”, “know-how”, or “why do”. 表现为“知因”、“知然”、或“因何”。

Example: in Bank 在银行

• Data 数据
  The numbers 100 or 5 (out of context) 数字100或者5（无上下文）
• Information: 信息
  Principal amount: $100, interest rate: 5%
  本金：100美元；利率：5%
• Knowledge: 知识
  At the end of year I get $105 back年底拿回105美元
• Wisdom: 智慧
  Investment need wisdom. How to invest? 投资决策需要智慧。

Concept of Knowledge

Human intelligence is mainly to acquire and use knowledge. Knowledge is
the foundation of intelligence.
人类的智能活动主要是获得知识并运用知识。知识是智能的基础。
In order for a computer to be intelligent and capable of simulating human
intelligence, it must be equipped with knowledge.
为了使计算机具有智能，能模拟人类的智能行为，就必须使它具有知识。
However, knowledge needs to be represented by appropriate patterns in
order to be stored in computers. Therefore, the representation of
knowledge has become a very important research topic in artificial
intelligence.
但知识需要用适当的模式表示出来才能存储到计算机中去，因此，知识
的表示成为人工智能中一个十分重要的研究课题。

• Knowledge is the cognition and experience of the objective world accumulated in the long life and social practice and in the scientific research and experiment.
  知识：在长期的生活及社会实践中、在科学研究及实验中积累起来的对客
  观世界的认识与经验。
• Knowledge is the information structure formed by associating relevant information together.
  知识：把有关信息关联在一起所形成的信息结构。
• Knowledge reflects the relationship between things in the objective world.
  知识反映了客观世界中事物之间的关系.
  Different things or the relationship of the same things form different knowledge.
  不同事物或者相同事物间的不同关系形成了不同的知识。

Example of Knowledge

“Snow is white. ”----------fact ( 事实知识)
“If you have a headache and a runny nose, you may have a cold” ------rule（规则知识）

2.1.2 Explicit and Tacit Knowledge 显性与隐性知识

Explicit knowledge显性知识

 It can be expressed into formal language, including grammatical statements,
etc. 可以表示为形式语言，包括语法陈述、数学表达式、等等。
 It can be readily transmitted to others. 可以快捷地转化成其它形式。
 It can be easily represented using computer languages, decision trees and
rules. 可以容易地用计算机语言、决策树和规则等表示。

Tacit knowledge隐性知识

 It can be individual experience and intangible factors, such as perspective, and belief etc. 可以是个人的经验和无形的因素、如观点、信仰等等。
 It is hard to express using formal language.难以用形式化语言来表示。
 Neural network offers a method to represent tacit knowledge.神经网络提供了表示隐形知识的方法。

2.1.3 Knowledge Types 知识的类型

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2.1.4 Knowledge Base and Knowledge Base System 知识库和知识库系统

The term ‘knowledge base (KB)’ was to distinguish from the more common widely used term ‘database (DB)’. Store data vs. store knowledge
“知识库(KB)”这个术语是用于区分更广泛使用的术语“数据库(DB)”。
KB is used to store complex structured and unstructured knowledge. It consists of a set of sentences, each one is expressed in a language called a knowledge representation language and represents some assertion about the world.
知识库被用于存储复杂的结构和非结构化知识。它由一套语句组成，每个语句都由知识表示语言来表示的，从而表示关于世界的某些断言。
A KB system (KBS) consists of a KB and an inference engine, where, KB represents facts about the world, inference engine can reason about those
facts. 一个知识库系统(KBS) 由知识库和推理引擎组成，其中，知识库表示关于世
界的事实，推理引擎则可以基于这些事实进行推理。Like DB vs. DBS

2.1.5 Knowledge Engineering (KE) 知识工程

KE refers to all technical, scientific and social aspects involved in building, maintaining and using KB systems.KE指的是指建立、维护和使用知识库系统所涉及的所有技术、科学和社会方面。
KE is essentially engineering on the basis of knowledge models, that use knowledge representation to represent the artifacts of the design process. The initial use of the KE was expert systems.
KE本质上是在知识模型基础之上的工程，它采用知识表示来表征设计过程的产品。KE最初的应用是专家系统。

2.2 Knowledge Representation

2.2.1 Overview of Knowledge Representation 知识表示概述

What is knowledge representation 什么是知识表示

 It focus on representing information about the world in a form that a computer system can utilize to solve complex tasks…
知识表示关注于表示有关世界的信息，以一种计算机系统可用于解决复杂任务的
形式表示知识。
 Knowledge representation incorporates findings from psychology about how
humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build.
知识表示结合心理学中关于人类如何解决问题的研究结果，设计表示知识的形式
化，这将使得复杂系统更容易设计和建造。
 Knowledge is stored in KB. And knowledge representation can be used in
expert systems.
知识被存放于知识库中。知识表示可被用于专家系统。

2.2.2 Typical Methods of Knowledge Representation

 Semantic Network 语义网络
 First-order logic representation 一阶逻辑表示
 Production system 产生式 系统
 Frame-based System 基于框架的系统（框架理论）
 Bayesian Network 贝叶斯网络
 Ontology 本体

2.2.3 Semantic Network

Semantic Network is network which represents semantic relations between
concepts. 语义网络是一种表示概念间语义关系的网络。
It is a directed or undirected graph consisting of nodes and arcs, where nodes
represent concepts, arcs denote semantic relationship between the concepts.
它是由节点和弧组成的有向或无向图，其中，节点：表示概念，弧：概念间的语义关系

• Example: A Semantic Network in LISP用LISP语言表示的语义网络
  Using an association list.采用关联表。
  
  Use “assoc” function with a key of “canary” to extract all information about “canary” type.
  使用“assoc”函数与关键字“canary”来提取关于“canary”类型的所有信息。

Basics of Semantic Network 语义网络的基本概念

 Semantic networks are cognitively based, organized into a taxonomic hierarchy.
语义网络是基于认知的，被组织成为一个分类层次结构。
 A semantic network is used when one has knowledge that is best understood as
a set of concepts that are related to one another.
当某种知识可以很好地理解为一组彼此相关的概念时，则采用语义网表示。
 But, it is intractable for large domains, and can not represent performance very
well.
但是，它难以驾驭大型领域，并且不能很好地表现性能。
 Some properties are not easily expressed, e.g., negation, disjunction, or general
non-taxonomic knowledge.
它也不易表达某些特性，例如：否定、析取、或者一般的非分类知识。

2.2.4 Representation Using Logic

 Procedural vs. Declarative Approaches
 Five Different Logics
 Logical Symbols
 Propositional Logic vs. First-order Logic
 Formation Rules in First Order Logic
 Prolog Language

（1）Procedural vs. Declarative Approaches 过程性与陈述性方法

Procedural approaches 过程性方法
Procedural knowledge is expressed by procedural approaches, using procedural languages, such as C/C++/C#/Java， Lisp， Python过程性知识采用过程性方法描述，即采用过程性语言描述。
Declarative approaches 陈述性方法
Declarative knowledge is expressed by declarative approaches, using declarative languages, such as 陈述性知识采用陈述性方法描述，即采用陈述性语言，例如
 Propositional logic, 命题逻辑
 First-order logic, 一阶逻辑
 Temporal logic. 时序逻辑

（2）Five Different Logics 五种不同的逻辑

（1）本体论约定

是指世界存在什么 Ontological refers to What’s there in the world?

（2）认识论约定

是指agent认为的事实是什么 Epistemological refers to What does agent think is the truth?

（3）Logical Symbols 逻辑符号

Logical Symbols is used in formal representation of Logic.

（4）Propositional Logic vs. First-order Logic 命题逻辑与一阶逻辑

• Propositional logic: 命题逻辑
   A proposition is a declarative sentence that is neither true nor false.
  命题是一个非真既假的陈述句。如：用P表示“西安是一座古老的城市”这个命题
   also known as propositional calculus, 亦被称为命题演算
   Only use logical connectives, deal with simple declarative propositions
  (if they are true or false).
  仅使用逻辑连接词，用于处理简单的陈述性命题。
• First-order logic: 一阶逻辑
   also known as first-order predicate calculus, 亦被称为一阶谓词演算
   additionally, use quantifiers, equality, and use predicates (often associated with sets).
  除了连接词，还使用限量词（ ∃、 ∀）、等量词（=）、以及谓词（通常与集合相
  关）。如：用谓词 S(x) 表示x是个学生。
• Propositional Logic Syntax with BNF 用BNF表述的命题逻辑语法结构和规则
  
  BNF is a symbolic technique used to formalize computer syntax.
  BNF是用于形式化描述计算机语法的符号技术。
• First-Order Logic Syntax with BNF 用BNF表述的一阶逻辑的语法规则
  Compared with propositional logic, first-order logic is more rich in grammar and more expressive in describing declarative knowledge. 与命题逻辑相比，一阶
  逻辑在描述陈述性知识时，语法更丰富，表现力更强。

（5）Formation Rules in First Order Logic 一阶逻辑的形式规则

 The formation rules define 该形式规则定义

• **Terms 项
• formulas 公式**
   The formation rules can be used to write a formal grammar for terms and
  formulas. 该形式规则可以用于书写项和公式的形式文法。
   Formation rules are generally context-free, i.e.,
  形式规则通常是上下文无关的，即
  each production has a single symbol on the left side.
  每个产生式左侧有一个单一的符号。

Formation Rules of First Order Logic : Terms一阶逻辑的形式规则：项

 Rule1: Variables （变量）
Any variable is a term. 任何变量都是一个项。
 Rule2: Constants （常数）
Any constant is also a term. 任何常数也都是一个项。
 Rule3: Functions （函数）
Any expression f(t1,…,tn) of n arguments is a term, where each argument ti is a term, and f is a function symbol of valence n. In particular, symbols denoting individual constants are 0-aryfunction symbols, and are thus terms.
任何n个参数的表达式f(t1,…,tn)都是一个项，其中每个参数ti是一个项，并且f是一
个价n的函数符号。尤其是，表示个体常量的符号是0元函数符号，因此也是一个
项。

Formation Rules of First Order Logic : Formulas 一阶逻辑的形式规则：公式

（6）Prolog Language （Prolog语言）

 Prolog (Program in logic) language has its roots in first-order logic.
Prolog语言起源于一阶逻辑。
 Prolog is a general purpose logic programming language, has been used for theorem proving, expert systems, natural language processing, and so on.
Prolog是一种通用的逻辑编程语言，已经被用于定理证明、专家系统、自然语言处理，等等。
 Unlike other programming languages(C, C++), Prolog is declarative: the program logic is expressed in terms of relations, represented as facts and rules.
不同于其它过程性编程语言（C，C++），Prolog是陈述性的：程序逻辑由关系来表达，表示为事实与规则。

• Prolog语句:
  likes (bill, car). 表示bill喜欢汽车，是事实性知识
  animal(X) : -cat(X). X是猫，因此X是动物，这是规则性知识
  bird(X) :-animal(X), has(X, feather).
  X是动物，X还有羽毛，因此X是鸟。这也是规则性知识。