569 Winter 2007 Dr. J. Lu “Predicting How Ontology for the Semantic Web will Evolve“,by Henry...

569 Winter 2007 Dr. J. Lu “Predicting How Ontology for the Semantic Web will Evolve“ ,by Henry Kim

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Predicting How Ontology for the Semantic Web will Evolve

60-569 Semantic WebWinter 2007

Dr. J. Lu

Presented by: Elamsy, Tarik & Dissanayake, Aqila

By: Henry Kim


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Agenda

1. Introduction

2. Problem & Motivations

3. Prediction Methodology

4. History of Paper-based business

5. XML vs. Ontology

6. When would be able to adopt Ontology (Examples)

7. Conclusion


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Introduction The Web has developed as a medium of documents for

people rather than of information that can be manipulated automatically.

Humans are capable of using the Web to carry out tasks such as

–Reserve a room at your favorite hotel.–Reserve a library book–Search for the cheapest DVD and buy it.

However, a computer cannot accomplish the same tasks without human direction because web pages are designed to be read by people, not machines.

By augmenting Web pages with data targeted at computers and by adding documents solely for computers, we will transform the Web into the semantic Web.

Computers will find the meaning of semantic data by following hyperlinks to definitions of key terms and rules for reasoning about them logically.


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Problems & Motivations

What if not enough people represent machine process-able information at all, or not richly enough?

The “information” that appears to be the bottleneck for the adoption of the semantic Web is not data.

It is the rules and meanings about data defined precisely enough so that machines can correctly interpret and quickly process that data.


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Aims :Predicting the future of Semantic Web

For the semantic Web, Ontology is encouraged to codify information.

The future of the semantic Web is linked with the future of ontologies on the semantic Web.

How can someone predict the future of something ?

We might predict the future of Semantic web by looking at the history of something similar.


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Prediction Methodology

The aim of this work is to examine the evolution of paper-based systems.

Analyze it using a conceptual model of system evolution.

Apply the model to Web-based systems equivalent to paper-based systems.

Finally, project what happened with paper-based systems to make predictions about how ontologies will evolve, if at all,


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The History of

Business Forms Evolution


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Evolution of Business Forms

In simple paper (such as memos), the author is responsible for authoring

The reader responsible for interpretation and processing. printing press responsible for paper distribution and

dissemination . Technological Inventions (such as punch card , typing machine ,

organizational restructuring, photocopiers..etc )

How does the paper based system is compared to semantic web ?

HTML document equivalent to paper Machine process web documents Internet infrastructure distribute and share the document. Technological Inventions (XML ,Ontologies ..ect)


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Complexity reduction Information is considered too complex when it requires

more processing than available in order to be properly analyzed and understood.

This complexity can be reduced by omission and abstraction.

An omission strategy forces the author to submit only sets of information required for processing.

The abstraction strategy allows sets of information to be abstracted from one document so that processing can be performed on a set rather than the whole document.

For paper documents, this strategy is executed using business forms, which separate and remove the structure of the document from its contents.


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How form-based business evolved

Authors responsibility is decomposed into forms design, and forms data entry.

Forms designers develop standard operating procedures that

data entry clerks could use.

When the volume of actions necessary to accomplish a task becomes too great, the complexity of the task must be reduced through division of labour.

Forms and task design are centralized and performed by professionals.

Data entry and task execution, de-centralized and performed by clerks.


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Technological Innovation effects Many Innovations enabled further division of labor for

reducing complexity and increasing efficiency

For example :

– Counting punch cards machines sped processing

– Carbon paper eliminated unnecessary task steps


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Near Decomposability

Later, near decomposability were used to reduce complexity of coordination for division of labor.

“Construct the units so that interactions between units are minimal”

One way was contracting and out sourcing, wherein:

Price

Informational complexity

Contractual termsTask complexity

ContractorNear decomposable unit


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Near Decomposability (cont’) Low-cost office typesetting changed this.

A more near decomposable unit was created by many businesses, equipped with typesetters, and staffed by forms and task designers.

Hence, a specialized functional division arose.

Coordination complexity can be reduced by organizational sub-structuring towards functional or product orientation


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Xerographic photocopying

Last significant innovations were xerographic photocopying.

Enabled inexpensive, high quality, large volume replication.

As photocopiers became available outside the organization, forms users reduced their dependency on the department by copying extra legitimate and customized -“bootleg”- forms.


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Xerographic photocopying (cont’)

This lead to the use of bootleg forms “those forms which can be produced outside of the control of the organization forms department” is a third coordination complexity reduction strategy.

However, these “bootleg” forms also introduced uncertainty.

For example, a data processing clerk could not process a “bootleg” form that seemingly contained required information, though expressed unclearly.

A system tuned to process a certain volume of completed forms could not cope with additional volumes of user-copied forms.

Uncertainty introduced by “bootleg” forms to an efficient forms processing system led to efficiency loss


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XML vs. Ontology Analysis XML & Ontology are two ways of explicitly representing

information.

XML use is similar to business form use since information structure (DTD) is separated from contents represented as XML data.

XML more mature technology than Ontology in term of size of user and viability of business model relying on it.

Example <foo> 7 </foo>


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XML vs. Ontology (cont’) Ontology has more precise definitions of the meaning of

the terms (vocabulary) than XML.

It also has set of formal axioms that constrain the use of these terms.

Ontology use for the Semantic Web then is similar to use of business forms with standard operating procedures where :-

- Informational structure is represented as terminology.- Rules governing proper interpretation of the structure, as formal definitions and constraints. - Content, as ontology ground terms

Example : foo(7)


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XML vs. Ontology (cont’) Shared understanding about a community is always

applied in solving problems in that community. The terminology used by community members can be codified as the community’s DTD’s.

Ontologies, as “explicit representations of shared understanding” , can be used to also codify the terminology’s semantics.

For example, it must be assumed in using XML that the author and reader of<foo>7</foo> have the same understanding of what ‘foo’ means.

On the other hand ,This assumption need not be made in ontology use, since ‘foo’ can be explicitly defined.


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What to adopt ? Ontology or XML ?

XML for the WWW is a much more mature technology than ontologies for the Semantic Web in terms of :– Size of user community– Availability of support tools– Viability of business models relying on the technology.

Therefore, ontologies can be adopted in situations where the capability to represent semantics is important enough to overcome XML’s maturity advantages.

What are the characteristic of these situations?

Pros and cons of XML and ontology uses must be analyzed in terms of ( bounded rationality, Division of labor and Near decomposability)


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1) Bounded rationality XML use is less complex since semantics are not represented.

But?

While many people can identify and classify terms, only some can systematically express meanings of these terms.

There is increased uncertainty that crucial information for interpreting shared data is not represented.

I.e XML reduces complex but also less certain and efficient than ontology.


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2) Division of labor

There is a clearer separation of responsibilities in XML use. DTD and data sharing task designs are done by professionals Data entry and data sharing done by computers with some

manual intervention.

It may not be possible to automate data entry or even apply clerical skills for entering ontology data because users may need to be sophisticated enough to understand what semantics is.

Therefore, tasks for manipulating XML data are likely more efficient.

However, because it is more automated, and not capable of applying semantics, an XML based system will be more sensitive to un-interpretable data than a semi-automated ontology based system that is able to apply semantics for interpretation

i.e XML decreases complexity but increases uncertainty


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3) Near decomposability If interactions between near decomposable units are

minimal, such a unit can then be organize to reduce complexity of interactions, guided by principles of bounded

rationality and division of labor.

As long as a unit can be considered nearly decomposable, bounded rationality and division of labor provide reasons

for why XML use reduces complexity.

However, if near decomposability cannot be assumed, ontology use increases the likelihood that data can still be

shared.


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XML vs. Ontology summarized

A unit is nearly decomposable for purposes of data sharing if it is reasonable to assume that shared understanding can be implicitly or informally applied to interpret data within that unit (a community).

Within a near decomposable unit, it is possible to reduce complexity in data sharing.

If near decomposability cannot be assumed, reducing uncertainty of data sharing by explicitly and formally defining semantics in ontologies may be warranted.

Unless reducing uncertainty is more important than reducing complexity XML will be a better or more proven data sharing platform than ontologies.


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Example: XML for Complexity reduction

Consider a models in which shared understanding is codified. Complexity can be reduced by organization restructuring In the contracting model, the business network can be

considered a near decomposable unit, since data is greatly shared between its companies and mediating service

According to the “XML vs. Ontologies” analysis, XML use for data sharing within the network then is appropriate.


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Example: XML for Complexity reduction

In the functional oriented model, the enterprise is more near decomposable than its departments and function.

XML use within the enterprise is quite appropriate.

For example, WebMethods provides XML based tools to enable companies to perform the data integration function.


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Example: Ontology for Uncertainty Reduction

Data modeling tool is similar to Photocopier. Knowledge worker (not specialized data modeler) codify

shared understanding which is used to translate data for use by external entity. <uncertainty introduced ?>


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What can we predict

The author claims that

“ Ontologies may be widely adopted, if there are ontology development tools that can be practically

used by knowledge workers, not necessarily by ontologists, to codify information.

Designing an ontology development tool demonstrated to be useful and useable to a knowledge worker, who is not a knowledge representation expert.

Development of de-centralized, and adaptive ontologies, which have value in of themselves, but whose full potential will only be realized if they are used in combination with other ontologies in the future to enable data sharing.


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Conclusion The first phase in the evolution of the Semantic Web may

be the development of de-centralized, adaptive ontologies for software specification.

These predictions are not founded on a precise analytical or experiential model.

They are argued using analogies and a theoretical model, and hence much further research is required to strengthen their validity.

It provided reasonable systematic analysis, and as such hopefully will provoke thought and motivate concrete research questions about the promising semantic Web.

How does the ontology development tool work? How are decentralized, adaptive ontologies constructed? How are such ontologies organized for data sharing in the

future?


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Questions ?

569 Winter 2007 Dr. J. Lu “Predicting How Ontology for the Semantic Web will Evolve“,by Henry...

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