程序代写代做代考 database A Discussion of Some Intuitions of Defeasible Reasoning

A Discussion of Some Intuitions of Defeasible Reasoning

Chapter 7
A Semantic Web Primer
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Chapter 7
Ontology Engineering

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The Representation of Knowledge
knowledge has many meanings.
Data, facts, information are often used to indicate knowledge.

linked documents vs linked data

Web 1 was about linked documents, Web 2 is about social interactions and Web 3 will be about linked data!
In the process of linked data, performing effective logic and knowledge processing with computers is gaining prime importance.
Noise, data, information, and knowledge can be considered as a hierarchy, on which data sits on the top of noise, and information sits on the top of data, and knowledge on the top of information.

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Lecture Outline
Introduction
Constructing Ontologies Manually
Reusing Existing Ontologies
Semiautomatic Ontology Acquisition
Ontology Mapping
Architecture

Categories are the base of semantic Web and are called (i) domains (in databases), (ii) types (in Artificial Intelligence), (iii) classes (in object oriented programming), and (iv) concepts (in logic). Sets can show Categories. For instance, subclasses can be shown with:
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Methodological Questions

Which languages and tools should be used in which circumstances, and in which order?
What about issues of quality control and resource management?
Many of these questions for the Semantic Web have been studied in other contexts
E.g. (i) software engineering, (ii) object-oriented design, and (iii) knowledge engineering

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Lecture Outline
Introduction
Constructing Ontologies Manually
Reusing Existing Ontologies
Semiautomatic Ontology Acquisition
Ontology Mapping
On-To-Knowledge SW Architecture

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8 main Stages in Ontology Development:

Determine scope
Consider reuse
Enumerate terms
Define taxonomy
Define properties
Define facets (cardinality, symmetry, transitivity,…………)
Define instances
Check for anomalies
Not a linear process!

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A Semantic Web Primer
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Lecture Outline
Introduction
Constructing Ontologies Manually
Reusing Existing Ontologies
Semiautomatic Ontology Acquisition
Ontology Mapping
On-To-Knowledge SW Architecture

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Existing Domain-Specific Ontologies

DBPedia is a great source of Knowledge with all people around the world contributing to improving its status

There are many domains for ontology, for instance:
Medical domain: Cancer ontology from the National Cancer Institute in the United States
Cultural domain:
Art and Architecture Thesaurus (AAT) with 125,000 terms in the cultural domain
Union List of Artist Names (ULAN), with 220,000 entries on artists
Iconclass vocabulary of 28,000 terms for describing cultural images
Geographical domain: Getty Thesaurus of Geographic Names (TGN), containing over 1 million entries

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Integrated Vocabularies
Merge independently developed vocabularies into a single large resource
E.g. Unified Medical Language System integrating 100 biomedical vocabularies
The UMLS metathesaurus contains 750,000 concepts, with over 10 million links between them
The semantics of a resource that integrates many independently developed vocabularies is rather low
But very useful in many applications as starting point

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Upper-Level Ontologies
Some attempts have been made to define generally applicable ontologies
Not domain-specific
Cyc, with 60,000 assertions on 6,000 concepts
Standard Upperlevel Ontology (SUO)

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Topic Hierarchies
Some “ontologies” do not deserve this name:
simply sets of terms, loosely organized in a hierarchy
This hierarchy is typically not a strict taxonomy but rather mixes different specialization relations (e.g. is-a, part-of, contained-in)
Such resources are often very useful as starting point

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Linguistic Resources
Some resources were originally built not as abstractions of a particular domain, but rather as linguistic resources
These have been shown to be useful as starting places for ontology development
E.g. WordNet, with over 90,000 word senses

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Ontology Libraries
Attempts are currently underway to construct highly sophisticated online libraries of valuable online ontologies
1) Rarely existing ontologies can be reused without changes
2) Existing concepts and properties must be refined using: rdfs:subClassOf and rdfs:subPropertyOf
Alternative names must be introduced which are better suited to the particular domain using owl:equivalentClass and owl:equivalentProperty

Ontology Repositories
https://www.w3.org/wiki/Ontology_repositories

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Design your own ontology

https://protege.stanford.edu/publications/ontology_development/ontology101.pdf

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Lecture Outline
Introduction
Constructing Ontologies Manually
Reusing Existing Ontologies
Semi-automatic Ontology Acquisition
Ontology Mapping
On-To-Knowledge SW Architecture

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The Knowledge Acquisition Bottleneck
Manual ontology acquisition remains a (i) time-consuming, (ii) expensive, (iii) highly skilled, and sometimes (iv) cumbersome task.
In fact, Machine Learning techniques may be used to alleviate
knowledge acquisition or extraction
knowledge revision or maintenance

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Tasks Supported by Machine Learning :

Extraction of ontologies from existing data on the Web
Extraction of relational data and metadata from existing data on the Web
Merging and mapping ontologies by analysing extensions of concepts
Maintaining ontologies by analysing instance data

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Useful Machine Learning Techniques for Ontology Engineering:
Clustering
Incremental ontology updating
Support for the knowledge engineering
Improving large natural language ontologies
Ontology learning

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Lecture Outline
Introduction
Constructing Ontologies Manually
Reusing Existing Ontologies
Semi-automatic Ontology Acquisition
Ontology Mapping
Architecture

Ontology Learning

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Ontology Mapping
A single ontology will rarely fulfill the needs of a particular application; multiple ontologies will have to be combined
This raises the problem of ontology integration (also called ontology mapping)
Current major approaches in ontology mapping are:
(i) linguistic,
(ii) statistical,
(iii) structural, and
(iv) logical methods
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(i) Linguistic methods
The most basic methods try to exploit the linguistic labels attached to the concepts in source and target ontology in order to discover potential matches
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(ii) Statistical Methods
A significant statistical correlation between the instances of a source concept and a target concept, gives us reason to believe that these concepts are strongly related
These approaches rely on the availability of a sufficiently large amount of instances that are classified in both the source and the target ontologies
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(iii) Structural Methods
Since ontologies have internal structure, it makes sense to exploit the graph structure of the source and the target ontologies and try to determine similarities, often in coordination with other methods
If a source target and a target concept have similar linguistic labels, then the dissimilarity of their graph neighborhoods could be used to detect homonym problems where purely linguistic methods would falsely declare a potential mapping
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(iv) Logical Methods
The most specific to mapping ontologies
A serious limitation of this approach is that many practical ontologies don’t carry much logical formalism with them
In any case, if an ontology carries heavy logical formalism, logical methods can be effectively used for its mapping.

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Ontology-Mapping Techniques Conclusion
Although there is much potential, and indeed need, for these techniques to be deployed for Semantic Web engineering, this is far from a well-understood area
For Ontology-Mapping , no off-the-shelf techniques are currently available, and it is not clear that this is likely to change in the near future.
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Chapter 7
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Lecture Outline
Introduction
Constructing Ontologies Manually
Reusing Existing Ontologies
Semi-automatic Ontology Acquisition
Ontology Mapping
Architecture

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Architecture
Building the Semantic Web or in fact its architecture involves :

Knowledge Acquisition
Knowledge Storage
Query Languages, for processing the knowledge stored, and
Knowledge Maintenance

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Knowledge Acquisition
Initially, tools must exist that use surface analysis techniques to obtain content from documents
Unstructured natural language documents: statistical techniques and shallow natural language technology
Structured and semi-structured documents: pattern recognition

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Knowledge Storage
The output of the analysis tools is sets of concepts, organized in a shallow concept hierarchy with at best very few cross-taxonomical relationships
RDF/RDF Schema are sufficiently expressive to represent the extracted info
Store the knowledge produced by the extraction tools
Retrieve this knowledge, preferably using a structured query language

Query Languages
Without query languages, questions cannot be answered and since, Semantic Web is involved with answering questions, these query languages like (i) SPARQL, (ii) DL, and (iii) SWQRL play key roles in Web Semantic.

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Knowledge Maintenance and Use
A practical Semantic Web repository must provide functionality for managing and maintaining the ontology:
change management
access and ownership rights
transaction management
There must be support for both
Lightweight ontologies that are automatically generated from unstructured and semi-structured data
Human engineering of much more knowledge-intensive ontologies

More ontology design methodologies

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Ontology Evaluation

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