Dr. Davoud Mougouei Decision Systems Lab SCIT, EIS, UOW Ph.D. in Software Engineering M.Sc. in Computer Science B.Eng. in Computer Engineering
Dr. Davoud Mougouei
Decision Systems Lab
SCIT, EIS, UOW
Ph.D. in Software Engineering
M.Sc. in Computer Science
B.Eng. in Computer Engineering
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Engineering Human Values in Software through Value Programming (CHASE, 2020)
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Important Points about the Subject
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Please read the subject outline carefully!
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Please discuss your technical questions in the lab!
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Please email for what cannot be discussed in the lab!
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Lecture 1
The Semantic Web Vision &
Structured Web Documents in XML
Chapters 1 &2 of
Grigoris Antoniou
Frank van Harmelen
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Chapter 1
The Semantic Web Vision
https://www.w3.org/standards/semanticweb/
A Semantic Web Primer
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Lecture Outline
Today’s Web
The Semantic Web Impact
Semantic Web Technologies
A Layered Approach
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What are the typical usages of the web?
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Today’s Web
Today’s web typical uses
seeking and making use of information,
searching for and getting in touch with other people,
reviewing catalogues of online stores ,
and ordering products by filling out forms
Most of today’s Web content is suitable for human consumption
Even Web content that is generated automatically from databases is usually presented without the original structural information found in databases
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Keyword-Based Search Engines
Current Web activities are not particularly well supported by software tools
Except for keyword-based search engines (e.g., Google)
The Web’s success relies on search engines.
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What are the problems with the current Keyword-Based Search Engines?
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Results are highly sensitive to vocabulary
High recall but low precision
Low recall
Recall = tp/(tp+fn)
Precision = tp/(tp+fp)
Problems of Keyword-Based
Search Engines
https://medium.com/
(A)
(B)
Problems of Keyword-Based
Search Engines
Human involvement is necessary to interpret and combine results
Results of Web searches are not readily accessible by other software tools
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The Key Problem of Today’s Web
The meaning of Web content is not machine-accessible: lack of semantics
It is simply difficult to distinguish the meaning between these two sentences:
I am a lecturer of sematic web.
I am a lecturer of semantic web, you may think. Well, . . .
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Three key points with Semantic Web Approach:
Representing Web content in a form that is more easily machine-processable.
Using intelligent techniques to take advantage of these representations.
Evolving out of the existing Web
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Lecture Outline
Today’s Web
The Semantic Web Impact
Semantic Web Technologies
A Layered Approach
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The Semantic Web Impact – Knowledge Management
Knowledge management concerns with (i) acquiring, (ii) accessing, and (iii) maintaining knowledge within an organization
Key activity of large businesses: internal knowledge as an intellectual asset
Most information is currently available in a weakly structured form (e.g. text, audio, video)
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Limitations of Current Knowledge Management Technologies in four dimensions:
Searching information:
Keyword-based search engines
Extracting information:
human involvement necessary for browsing, retrieving, interpreting, combining
Maintaining information:
inconsistencies in terminology, outdated information.
Viewing information:
Impossible to define views on Web knowledge
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Semantic Web Enabled Knowledge Management
i) organizing knowledge in conceptual spaces according to its meaning.
ii) having automated tools for maintenance and knowledge discovery
iii) answering any query semantically
iv) answering any query over several documents
v) defining who may view certain parts of information (even parts of documents) will be possible.
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The Semantic Web Impact –
B2C Electronic Commmerce
A typical scenario: user visits one or several online shops, browses their offers, selects and orders products.
Ideally humans would visit all, or all major online stores; but time consuming
Shopbots are a useful tool:
https://www.shopbot.com.au/
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Limitations of Shopbots
They rely on wrappers: extensive programming required
Wrappers need to be reprogrammed when an online store changes its outfit
Wrappers extract information based on textual analysis
Error-prone
Limited information extracted
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Semantic Web Enabled B2C
Electronic Commerce
Software agents that can interpret the product information and the terms of service.
Pricing and product information, delivery and privacy policies will be interpreted and compared to the user requirements.
Information about the reputation of shops
Sophisticated shopping agents will be able to conduct automated negotiations
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The Semantic Web Impact –
B2B Electronic Commerce
Currently relies mostly on EDI (Electronic Data Interchange)
Isolated technology, understood only by experts
Difficult to program and maintain, error-prone
Each B2B communication requires separate programming
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Semantic Web Enabled B2B Electronic Commerce
(Advantages)
Businesses enter partnerships without much overhead
Differences in terminology will be resolved using standard abstract domain models
Data will be interchanged using translation services.
Auctioning, negotiations, and drafting contracts will be carried out automatically (or semi-automatically) by software agents
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Wikis are the next area Semantic Web can be useful:
Collections of web pages that allow users to add content via a browser interface
Wiki systems allow for collaborative knowledge
Users are free to add and change information without ownership of content, access restrictions, or rigid workflows
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Some Uses of Wikis
Development of bodies of knowledge in a community effort, with contributions from a wide range of users (e.g. Wikipedia)
Knowledge management of an activity or a project (e.g. brainstorming and exchanging ideas, coordinating activities, exchanging records of meetings)
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Semantic Web Enabled Wikis
The inherent structure of a wiki, given by the linking between pages, gets accessible to machines beyond mere navigation
Structured text and untyped hyperlinks are enriched by semantic annotations referring to an underlying model of the knowledge captured by the wiki
e.g. a hyperlink from Knossos to Heraklion could be annotated with information is located in. This information could then be used for context-specific presentations of pages, advanced querying, and consistency verification
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Lecture Outline
Today’s Web
The Semantic Web Impact
Semantic Web Technologies
A Layered Approach
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Semantic Web Technologies
Explicit Metadata
Ontologies
Logic and Inference
Agents
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On HTML
Web content is currently formatted for human readers rather than programs.
HTML is the predominant language in which Web pages are written (directly or using tools)
Vocabulary describes presentation
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An HTML Example
Agilitas Physiotherapy Centre
Welcome to the home page of the Agilitas Physiotherapy Centre. Do
you feel pain? Have you had an injury? Let our staff Lisa Davenport,
Kelly Townsend (our lovely secretary) and Steve Matthews take care
of your body and soul.
Consultation hours
Mon 11am – 7pm
Tue 11am – 7pm
Wed 3pm – 7pm
Thu 11am – 7pm
Fri 11am – 3pm
But note that we do not offer consultation during the weeks of the
State Of Origin games
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Problems with HTML
Humans have no problem with this
Machines (software agents) do:
How distinguish therapists from the secretary,
How determine exact consultation hours
They would have to follow the link to the State Of Origin games to find when they take place.
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A Better Representation
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Explicit Metadata
This representation is far more easily processable by machines
Metadata: data about data
Metadata capture part of the meaning of data
Semantic Web does not rely on text-based manipulation, but rather on machine-processable metadata
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Ontologies
The term ontology originates from philosophy
The study of the nature of existence
Different meaning from computer science
An ontology is an explicit and formal specification of a conceptualization
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Typical Components of Ontologies
Terms denote important concepts (classes of objects) of the domain
e.g. professors, staff, students, courses, departments
Relationships between these terms: typically class hierarchies
a class C to be a subclass of another class C’ if every object in C is also included in C’
e.g. all professors are staff members
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Further Components of Ontologies
Properties:
e.g. X teaches Y
Value restrictions
e.g. only faculty members can teach courses
Disjointness statements
e.g. faculty and general staff are disjoint
Logical relationships between objects
e.g. every department must include at least 10 faculty members.
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Example of a Class Hierarchy
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The Role of Ontologies on the Web
Ontologies provide a shared understanding of a domain: semantic interoperability
overcome differences in terminology
mappings between ontologies
Ontologies are useful for the organization and navigation of Websites
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The Role of Ontologies in Web Search
Ontologies are useful for improving the accuracy of Web searches
search engines can look for pages that refer to a precise concept in an ontology
Web searches can exploit generalization/ specialization information
If a query fails to find any relevant documents, the search engine may suggest to the user a more general query.
If too many answers are retrieved, the search engine may suggest to the user some specializations.
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What is the difference between Ontology and Taxonomy?
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Web Ontology Languages
RDF (Resource Description Framework) Schema
RDF is a data model for objects and relations between them
RDF Schema is a vocabulary description language which
1) Describes properties and classes of RDF resources,
2) Provides semantics for generalization hierarchies of properties and classes
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Web Ontology Languages (2)
OWL (Web Ontology Language) OWL is built on top of RDF
A richer ontology language
relations between classes
e.g., disjointness
cardinality
e.g. “exactly one”
richer typing of properties
characteristics of properties (e.g., symmetry)
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Logic and Inference
Logic is the discipline that studies the principles of reasoning
Formal languages for expressing knowledge
Well-understood formal semantics
Declarative knowledge: we describe what holds without caring about how it can be deduced
Automated reasoners can deduce (infer) conclusions from the given knowledge
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An Inference Example
prof(X) faculty(X)
faculty(X) staff(X)
prof(michael)
We can deduce the following conclusions:
faculty(michael)
staff(michael)
prof(X) staff(X)
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Logic versus Ontologies
The previous example involves knowledge typically found in ontologies
Logic can be used to uncover ontological knowledge that is implicitly given
It can also help uncover unexpected relationships and inconsistencies
Logic is more general than ontologies
It can also be used by intelligent agents for making decisions and selecting courses of action
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Tradeoff between Expressive Power and Computational Complexity
The more expressive a logic is, the more computationally expensive it becomes to draw conclusions
Drawing certain conclusions may become impossible if non-computability barriers are encountered.
Our previous examples involved rules “If conditions, then conclusion,” and only finitely many objects
This subset of logic is tractable and is supported by efficient reasoning tools
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Inference and Explanations
An important advantage of logic is that it can provide explanations for conclusions
Explanations: a series of inference steps that can be retraced
They increase users’ confidence in Semantic Web agents.
Activities between agents: create or validate proofs
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Typical Explanation Procedure
Facts will typically be traced to some Web addresses
The trust of the Web address will be verifiable by agents
Rules may be a part of a shared commerce ontology or the policy of the online shop
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Software Agents
Software agents work autonomously and proactively
They evolved out of object oriented and compontent-based programming
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Intelligent Personal Agents
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Semantic Web Agent Technologies
Metadata
Identify and extract information from Web sources
Ontologies
Search websites and interpret retrieved information
Communicate with other agents
Logic
Process retrieved information, draw conclusions
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Semantic Web Agent Technologies (2)
Further technologies (orthogonal to the Semantic Web technologies)
Agent communication languages
Formal representation of beliefs, desires, and intentions of agents
Creation and maintenance of user models.
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What will a personal agent on the Semantic Web do? Name its operations one after another chronically (in a time-based sequence).
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A personal agent on the Semantic Web will:
receive some tasks and preferences from the person
seek information from Web sources, communicate with other agents
compare information about user requirements and preferences, make certain choices
give answers to the user
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Take a break!
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Lecture Outline
Today’s Web
The Semantic Web Impact
Semantic Web Technologies
A Layered Approach
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The Semantic Web Layer Tower
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A General Semantic Web Layer Stack
A Specific Semantic Web Layer Stack
The main differences are:
The ontology layer is instantiated with two alternatives: the current standard Web ontology language, OWL, and a rule-based language
DLP (DataLog Logical Programming) is the intersection of OWL and Horn logic, and serves as a common foundation
The Semantic Web Architecture is currently being debated and may be subject to refinements and modifications in the future.
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A specific Semantic Web Layer Stack
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Semantic Web Layers
XML layer
Syntactic basis
RDF layer
RDF basic data model for facts
RDF Schema simple ontology language
Ontology layer
More expressive languages than RDF Schema
Current Web standard: OWL
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Semantic Web Layers (2)
Logic layer
enhance ontology languages further
application-specific declarative knowledge
Proof layer
Proof generation, exchange, validation
Trust layer
Digital signatures
recommendations, rating agencies ….
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Chapter 2:
Structured Web Documents in XML
Introduction
Detailed Description of XML
Structuring
DTDs
XML Schema
Namespaces
Accessing, querying XML documents: XPath
Transformations: XSLT
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An HTML Example
Nonmonotonic Reasoning: Context-
Dependent Reasoning
by V. Marek and
M. Truszczynski
Springer 1993
ISBN 0387976892
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The Same Example in XML
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HTML versus XML: Similarities
Both use tags (e.g.
and )
Tags may be nested (tags within tags)
Human users can read and interpret both HTML and XML representations quite easily
… But how about machines?
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Problems with Automated Interpretation of HTML Documents
An intelligent agent trying to retrieve the names
of the authors of the book
Authors’ names could appear immediately after the title
or immediately after the word by
Are there two authors?
Or just one, called “V. Marek and M. Truszczynski”?
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HTML vs XML: Structural Information
HTML documents do not contain structural information: pieces of the document and their relationships.
XML more easily accessible to machines because
Every piece of information is described.
Relations are also defined through the nesting structure.
E.g., the
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HTML vs XML: Structural Information (2)
A machine processing the XML document would be able to deduce that
the author element refers to the enclosing book element
XML allows the definition of constraints on values
E.g. a year must be a number of four digits
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HTML vs XML: Formatting
The HTML representation provides more than the XML representation:
The formatting of the document is also described
Τhe main use of an HTML document is to display information: it must define formatting
XML: separation of content from display
same information can be displayed in different ways
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HTML vs XML: Another Example
In HTML
Relationship force-mass
F = M × a
In XML
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HTML vs XML: Different Use of Tags
HTML tags define display: color, lists …
XML tags not fixed: user definable tags
Note that XML is a meta markup language: a language for defining markup languages
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XML Vocabularies
Web applications must agree on common vocabularies to communicate and collaborate
Communities and business sectors are defining their specialized vocabularies
mathematics (MathML)
bioinformatics (BSML)
human resources (HRML)
…
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Lecture Outline
Introduction
Detailed Description of XML
Structuring
DTDs
XML Schema
Namespaces
Accessing, querying XML documents: XPath
Transformations: XSLT
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The XML Language
An XML document consists of:
A prolog and a number of elements
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Prolog of an XML Document
The prolog consists of
an XML declaration and
an optional reference to external structuring documents
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XML Elements
The “things” the XML document talks about
E.g. books, authors, publishers
An element consists of:
an opening tag
the content
a closing tag
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Content of XML Elements
Content may be text, or other elements, or nothing
If there is no content, then the element is called empty; it is abbreviated as follows:
Is every empty element meaningless? Why?
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ANSWER
An empty element is not necessarily meaningless
It may have some properties in terms of attributes
An attribute is a name-value pair inside the opening tag of an element
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XML Attributes: An Example
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The Same Example without Attributes
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XML Elements vs Attributes
Attributes can be replaced by elements
When to use elements and when attributes is a matter of taste
But attributes cannot be nested
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Further Components of XML Docs
Comments
A piece of text that is to be ignored by parser
Processing Instructions (PIs)
Define procedural attachments
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Well-Formed XML Documents
Syntactically correct documents
Some syntactic rules:
Only one outermost element (called root element)
Each element contains an opening and a corresponding closing tag
Tags may not overlap
Attributes within an element have unique names
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The Tree Model of XML Documents:
An Example
Grigoris, where is the draft of the paper you promised me
last week?
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The Tree Model of XML Documents:
An Example (2)
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The Tree Model of XML Docs
The tree representation of an XML document is an ordered labeled tree:
There is exactly one root
There are no cycles
Each non-root node has exactly one parent
Each node has a label.
The order of elements is important
… but the order of attributes is not important
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Lecture Outline
Introduction
Detailed Description of XML
Structuring
DTDs
XML Schema
Namespaces
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Structuring XML Documents
Define all the element and attribute names that may be used
Define the structure
what values an attribute may take
which elements may or must occur within other elements, etc.
If such structuring information exists, the document can be validated
https://www.w3schools.com/xml/xml_dtd.asp
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Lecture Outline
Introduction
Detailed Description of XML
Structuring
DTDs
XML Schema
Namespaces
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XML Schema
Significantly richer language for defining the structure of XML documents
Its syntax is based on XML itself
not necessary to write separate tools
Reuse and refinement of schemas
Expand or delete already existent schemas
Sophisticated set of data types, compared to DTDs (which only supports strings)
https://www.w3schools.com/xml/schema_intro.asp
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XML Schema (2)
An XML schema is an element with an opening tag like
Structure of schema elements
Element and attribute types using data types
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XML Schema: The Email Example (3)
Similar for bodyType
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Lecture Outline
Introduction
Detailed Description of XML
Structuring
DTDs
XML Schema
Namespaces
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Namespaces
An XML document may use more than one DTD or schema
Since each structuring document was developed independently, name clashes may appear.
The solution is to use a different prefix for each DTD or schema.
prefix:name
https://www.w3schools.com/xml/xml_namespaces.asp
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An Example
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Namespace Declarations
Namespaces are declared within an element and can be used in that element and any of its children (elements and attributes)
A namespace declaration has the form:
xmlns:prefix=”location”
location is the address of the DTD or schema
If a prefix is not specified: xmlns=”location” then the location is used by default
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