Here are the key steps:
1. The retailer calls a computer a "PC for Gamers"
2. The manufacturer calls the same computer a "Prof Desktop"
3. Both the retailer and manufacturer agree the computer belongs to the class "Workstations/Desktops"
4. To determine if a "Prof Desktop" meets the requirements for a "PC for Gamers", we use semantic resolution to negotiate the semantic distance between the retailer and manufacturer terminology and mappings between their ontologies.
5. This allows determining to what extent properties like memory, CPU etc. of a "Prof Desktop" satisfy the requirements for a "PC for Gamers".
In summary, semantic resolution is used to bridge differences in
Using Architectures for Semantic Interoperability to Create Journal Clubs for...James Powell
In certain types of _slow burn_ emergencies, careful accumulation and evaluation of information can offer a crucial advantage. The SARS outbreak in the first decade of the 21st century was such an event, and ad hoc
journal clubs played a critical role in assisting scientific and technical responders in identifying and developing various strategies for halting what could have become a dangerous pandemic. This paper describes a process for leveraging emerging semantic web and digital library architectures and standards to (1) create a focused collection of bibliographic metadata, (2) extract semantic information, (3) convert it to the Resource Description Framework /Extensible Markup Language (RDF/XML),
and (4) integrate it so that scientific and technical responders can share and explore critical information in the collections.
systems.
Create Linked Open Data (LOD) Microthesauri using Art & Architecture Thesaurus (AAT) LOD. View and manage options by a non-techy person. Everyone can use, create,
derive from, & map to AAT microthesauri and make the digital collection become LOD-ready dataset.
Using Architectures for Semantic Interoperability to Create Journal Clubs for...James Powell
In certain types of _slow burn_ emergencies, careful accumulation and evaluation of information can offer a crucial advantage. The SARS outbreak in the first decade of the 21st century was such an event, and ad hoc
journal clubs played a critical role in assisting scientific and technical responders in identifying and developing various strategies for halting what could have become a dangerous pandemic. This paper describes a process for leveraging emerging semantic web and digital library architectures and standards to (1) create a focused collection of bibliographic metadata, (2) extract semantic information, (3) convert it to the Resource Description Framework /Extensible Markup Language (RDF/XML),
and (4) integrate it so that scientific and technical responders can share and explore critical information in the collections.
systems.
Create Linked Open Data (LOD) Microthesauri using Art & Architecture Thesaurus (AAT) LOD. View and manage options by a non-techy person. Everyone can use, create,
derive from, & map to AAT microthesauri and make the digital collection become LOD-ready dataset.
Dublin Core Registry to Support Multilinguality : Te Reo Māori Dublin Core Me...Karen R
11th Annual Open Forum for Metadata Registries
Metadata DownUnder - Metadata, Semantics and Interoperability in Practice. Sydney, NSW Australia 19 - 22 May 2008
Karen Coyle Keynote - R&D: Can Resource Description become Rigorous Data?eby
Work is beginning to transform the eloquent yet arcane texts called "library cataloging records" into data elements that will play well in the Web. Beginning with the upcoming revised cataloging rules, called
Resource Description and Access, a team of researchers is exploring the abstract model behind bibliographic description. Coyle will cover the
philosophy behind the project and will discuss current progress and goals, as well as fears, risks, and even some confusion.
Usage of Linked Data: Introduction and Application ScenariosEUCLID project
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This is part 2 of the ISWC 2009 tutorial on the GoodRelations ontology and RDFa for e-commerce on the Web of Linked Data.
See also
http://www.ebusiness-unibw.org/wiki/Web_of_Data_for_E-Commerce_Tutorial_ISWC2009
This is part 2 of the ISWC 2009 tutorial on the GoodRelations ontology and RDFa for e-commerce on the Web of Linked Data.
See also
http://www.ebusiness-unibw.org/wiki/Web_of_Data_for_E-Commerce_Tutorial_ISWC2009
This slides I've used on talk about Semantic Web use-case. Not all know what exactly Semantic Web is about. So I've created set of slides showing this in a simple and correct way. Use-case slides are removed on this public available slides. Animated version here goo.gl/qKoF6k . Contact me for sources!
Dublin Core Registry to Support Multilinguality : Te Reo Māori Dublin Core Me...Karen R
11th Annual Open Forum for Metadata Registries
Metadata DownUnder - Metadata, Semantics and Interoperability in Practice. Sydney, NSW Australia 19 - 22 May 2008
Karen Coyle Keynote - R&D: Can Resource Description become Rigorous Data?eby
Work is beginning to transform the eloquent yet arcane texts called "library cataloging records" into data elements that will play well in the Web. Beginning with the upcoming revised cataloging rules, called
Resource Description and Access, a team of researchers is exploring the abstract model behind bibliographic description. Coyle will cover the
philosophy behind the project and will discuss current progress and goals, as well as fears, risks, and even some confusion.
Usage of Linked Data: Introduction and Application ScenariosEUCLID project
This presentation introduces the main principles of Linked Data, the underlying technologies and background standards. It provides basic knowledge for how data can be published over the Web, how it can be queried, and what are the possible use cases and benefits. As an example, we use the development of a music portal (based on the MusicBrainz dataset), which facilitates access to a wide range of information and multimedia resources relating to music.
This is part 2 of the ISWC 2009 tutorial on the GoodRelations ontology and RDFa for e-commerce on the Web of Linked Data.
See also
http://www.ebusiness-unibw.org/wiki/Web_of_Data_for_E-Commerce_Tutorial_ISWC2009
This is part 2 of the ISWC 2009 tutorial on the GoodRelations ontology and RDFa for e-commerce on the Web of Linked Data.
See also
http://www.ebusiness-unibw.org/wiki/Web_of_Data_for_E-Commerce_Tutorial_ISWC2009
This slides I've used on talk about Semantic Web use-case. Not all know what exactly Semantic Web is about. So I've created set of slides showing this in a simple and correct way. Use-case slides are removed on this public available slides. Animated version here goo.gl/qKoF6k . Contact me for sources!
Presentation given during a tour of Australia, in May 2009. The targeted audience are people who are already familiar with the fundamentals of Semantic Web, and this presentation gives an overview of what is happening at W3C
There has been plenty of hype around the Semanic Web, but will we ever see the vision of intelligent agents working on our behalf? This talk introduces the concepts of the Semantic Web as envisioned by Tim Berners-Lee over 10 years ago and compares that vision to where we have come since then. It includes a discussion of implementations such as XML, RDF, OWL (web ontology language), and SPARQL. After reviewing the design principles and enabling technologies, I plan to show how these techniques can be implemented in WebGUI.
Open data is a crucial prerequisite for inventing and disseminating the innovative practices needed for agricultural development. To be usable, data must not just be open in principle—i.e., covered by licenses that allow re-use. Data must also be published in a technical form that allows it to be integrated into a wide range of applications. The webinar will be of interest to any institution seeking ways to publish and curate data in the Linked Data cloud.
This webinar describes the technical solutions adopted by a widely diverse global network of agricultural research institutes for publishing research results. The talk focuses on AGRIS, a central and widely-used resource linking agricultural datasets for easy consumption, and AgriDrupal, an adaptation of the popular, open-source content management system Drupal optimized for producing and consuming linked datasets.
Agricultural research institutes in developing countries share many of the constraints faced by libraries and other documentation centers, and not just in developing countries: institutions are expected to expose their information on the Web in a re-usable form with shoestring budgets and with technical staff working in local languages and continually lured by higher-paying work in the private sector. Technical solutions must be easy to adopt and freely available.
Seminar presentation for which the entire work was conducted at Technical University Kaiserslautern. The seminar work involved understanding the Semantic Web technology along with RDF and querying mechanism. It also involved looking at technologies that are used for data storage, data management and data querying.
VAT Registration Outlined In UAE: Benefits and Requirementsuae taxgpt
Vat Registration is a legal obligation for businesses meeting the threshold requirement, helping companies avoid fines and ramifications. Contact now!
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Kyiv PMDay 2024 Summer
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FB – https://www.facebook.com/pmdayconference
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A personal brand exploration presentation summarizes an individual's unique qualities and goals, covering strengths, values, passions, and target audience. It helps individuals understand what makes them stand out, their desired image, and how they aim to achieve it.
Unveiling the Secrets How Does Generative AI Work.pdfSam H
At its core, generative artificial intelligence relies on the concept of generative models, which serve as engines that churn out entirely new data resembling their training data. It is like a sculptor who has studied so many forms found in nature and then uses this knowledge to create sculptures from his imagination that have never been seen before anywhere else. If taken to cyberspace, gans work almost the same way.
Personal Brand Statement:
As an Army veteran dedicated to lifelong learning, I bring a disciplined, strategic mindset to my pursuits. I am constantly expanding my knowledge to innovate and lead effectively. My journey is driven by a commitment to excellence, and to make a meaningful impact in the world.
Memorandum Of Association Constitution of Company.pptseri bangash
www.seribangash.com
A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
Contents of Memorandum of Association:
Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
https://seribangash.com/article-of-association-is-legal-doc-of-company/
Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
Objective Clause: This clause delineates the main objectives for which the company is formed. It's important to define these objectives clearly, as the company cannot undertake activities beyond those mentioned in this clause.
www.seribangash.com
Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
https://seribangash.com/promotors-is-person-conceived-formation-company/
Capital Clause: This clause specifies the authorized capital of the company, i.e., the maximum amount of share capital the company is authorized to issue. It also mentions the division of this capital into shares and their respective nominal value.
Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
Importance of Memorandum of Association:
Legal Requirement: The MOA is a legal requirement for the formation of a company. It must be filed with the Registrar of Companies during the incorporation process.
Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
https://seribangash.com/difference-public-and-private-company-law/
Binding Authority: The company and its members are bound by the provisions of the MOA. Any action taken beyond its scope may be considered ultra vires (beyond the powers) of the company and therefore void.
Amendment of MOA:
While the MOA lays down the company's fundamental principles, it is not entirely immutable. It can be amended, but only under specific circumstances and in compliance with legal procedures. Amendments typically require shareholder
LA HUG - Video Testimonials with Chynna Morgan - June 2024Lital Barkan
Have you ever heard that user-generated content or video testimonials can take your brand to the next level? We will explore how you can effectively use video testimonials to leverage and boost your sales, content strategy, and increase your CRM data.🤯
We will dig deeper into:
1. How to capture video testimonials that convert from your audience 🎥
2. How to leverage your testimonials to boost your sales 💲
3. How you can capture more CRM data to understand your audience better through video testimonials. 📊
Digital Transformation and IT Strategy Toolkit and TemplatesAurelien Domont, MBA
This Digital Transformation and IT Strategy Toolkit was created by ex-McKinsey, Deloitte and BCG Management Consultants, after more than 5,000 hours of work. It is considered the world's best & most comprehensive Digital Transformation and IT Strategy Toolkit. It includes all the Frameworks, Best Practices & Templates required to successfully undertake the Digital Transformation of your organization and define a robust IT Strategy.
Editable Toolkit to help you reuse our content: 700 Powerpoint slides | 35 Excel sheets | 84 minutes of Video training
This PowerPoint presentation is only a small preview of our Toolkits. For more details, visit www.domontconsulting.com
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Improving profitability for small businessBen Wann
In this comprehensive presentation, we will explore strategies and practical tips for enhancing profitability in small businesses. Tailored to meet the unique challenges faced by small enterprises, this session covers various aspects that directly impact the bottom line. Attendees will learn how to optimize operational efficiency, manage expenses, and increase revenue through innovative marketing and customer engagement techniques.
Application Semantics via Rules in Open Vocabulary English
1. Application Semantics via Rules in Open
Vocabulary English
Adrian Walker
www.reengineeringllc.com
Presentation for theSci entific Discourse Meeting
July 11 2011
http://www.w3.org/wiki/HCLSIG/SWANSIOC/Actions/RhetoricalStructure/meetings/20110711
1
2. Abstract
There has been much progress assigning semantics to data.
However the meaning that resides in an application (or in a
SPARQL query) should be taken into account. Even if data
identifiers and ontologies have really fine readable meanings, an
application can change the semantics completely. And, unless
there are explanations of what the app has done, no-one will be
any the wiser unless the error is egregious (eg -- the Eiffel tower is
a dog).
This talk describes a system on the Web that combines three kinds
of semantics: (a) data -- as in SQL or RDF, (b) inference -- via a
theory of declarative knowledge, and (c) open vocabulary English.
The combination is used to answer questions over networked
databases, and to explain the results in hypertexted English. The
subject knowledge needed to do this can be acquired in social
network style, by typing executable English into browsers.
2
3. Agenda
• The World Wide Database vision
• Only experts have the skills to use the current tools
• An easier future for Semantic Technology -- combine:
– Semantics1 - Data Semantics = the current Technology
– Semantics2 - what a reasoner should do
– Semantics3 - Application Semantics = English meanings at the UI / AI
• A browser-based system for writing and running applications in English
• Examples : Semantics of ontology data, and of oil-industry SQL data
• Google finds applications that are written in executable English
• Summary
3
4. The World Wide Database vision
"If HTML and the Web made all the online documents look like one huge
book, the Semantic Web will make all the data in the world look like one
huge database”
-- Tim Berners-Lee
What is the Semantic Web?
“Data integration across application, organizational boundaries”
-- Tim Berners-Lee
4
5. The World Wide Database vision
• An advantage of RDF is that data from diverse sources can, in principle,
be freely merged and repurposed.
• Yet we cannot always expect meaningful results from simply merging
previously unseen RDF data under an existing application
• An application adds meaning to the data
5
6. The World Wide Database vision
Retailer’s English Manufacturer’s English
model of the world negotiable semantic distance
model of the world
6
7. The World Wide Database vision
Retailer’s English Manufacturer’s English
negotiable semantic distance
model of the world model of the world
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
<rdf:Alt rdf:about="http://retailer.org/node"/>
</rdf:RDF>
negotiable semantic distance
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
<rdf:Alt rdf:about="http://manuf.org/node"/>
</rdf:RDF>
7
8. The World Wide Database vision
Retailer’s English Manufacturer’s English
negotiable semantic distance
model of the world model of the world
semantic disconnects X
X
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
<rdf:Alt rdf:about="http://retailer.org/node"/>
</rdf:RDF>
negotiable semantic distance
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
<rdf:Alt rdf:about="http://manuf.org/node"/>
</rdf:RDF>
8
9. The World Wide Database vision
Retailer’s English Manufacturer’s English
negotiable semantic distance
model of the world model of the world
semantic disconnects X
X
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
<rdf:Alt rdf:about="http://retailer.org/node"/>
</rdf:RDF>
negotiable semantic distance
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
<rdf:Alt rdf:about="http://manuf.org/node"/>
</rdf:RDF>
9
10. Agenda
• The World Wide Database vision
• Only experts have the skills to use the current tools
• An easier future for Semantic Technology -- combine:
– Semantics1 - Data Semantics = the current Technology
– Semantics2 - what a reasoner should do
– Semantics3 - Application Semantics = English meanings at the UI
• A browser-based system for writing and running applications in English
• Examples : Semantics of ontology data, and of oil-industry SQL data
• Google indexes and searches applications that are written in English
• Summary
10
11. Only Experts have the Skills to
Use the Current Tools
Semantic
Web
Sub Topic
Knowledge
Discovery
Text Mining Data Mining
11
12. Only Experts have the Skills to
Use the Current Tools
Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery
Text Mining Data Mining Bob
12
13. Only Experts have the Skills to
Use the Current Tools
Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery
Text Mining Data Mining Does research on Bob
13
14. Only Experts have the Skills to
Use the Current Tools
Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery
Text Mining Data Mining Does research on Bob
New user asked: how can I use RDF and Owl to find out from the above that
“Bob does research into Semantic Web” ?
14
15. Only Experts have the Skills to
Use the Current Tools
Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery
Text Mining Data Mining Does research on Bob
New user asked: how an I use RDF and Owl to find out from the above that
“Bob does research into Semantic Web” ?
Expert replied: “You can do it by declaring subtopic to be transitive and by using a rule such as
ObjectPropertyAtom( worksIn, ?x, ?y) IF
ObjectPropertyAtom( worksIn, ?x, ?z)
AND ObjectPropertyAtom( subtopic, ?z, ?y)
Such rules can be expressed in RuleML or in SWRL, but you would have to find an
inference tool for them.”
15
16. Agenda
• The World Wide Database vision
• Only experts have the skills to use the current tools
• An easier future for Semantic Technology -- combine:
– Semantics1 - Data Semantics = the current Technology
– Semantics2 - what a reasoner should do
– Semantics3 - Application Semantics = English meanings at the UI
• A browser-based system for writing and running applications in English
• Examples : Semantics of ontology data, and of oil-industry SQL data
• Google indexes and searches applications that are written in English
• Summary
16
17. An Easier Future for Semantic Technology
Semantic
Web
Sub Topic
Knowledge
Discovery
Text Mining Data Mining
Facts:
this-item is a sub topic of this-topic
===================================
Data Mining Knowledge Discovery
Text Mining Knowledge Discovery
Knowledge Discovery Semantic Web
17
18. An Easier Future for Semantic Technology
Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Discovery Adrian Claire
Text Mining Data Mining Bob
Facts:
this-item is a sub topic of this-topic this-person is a researcher
=================================== ===================
Data Mining Knowledge Discovery Adrian
Text Mining Knowledge Discovery Bob
Knowledge Discovery Semantic Web Claire
18
19. An Easier Future for Semantic Technology
Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery
Text Mining Data Mining Does research on Bob
this-item is a sub topic of this-topic this-person is a researcher
=================================== ===================
Data Mining Knowledge Discovery Adrian
Facts: Bob
Text Mining Knowledge Discovery
Knowledge Discovery Semantic Web Claire
this-person does research into this-topic
==============================
Adrian Knowledge Discovery
Bob Data Mining
Claire Text Mining
19
20. An Easier Future for Semantic Technology
Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery
Text Mining Data Mining Does research on Bob
A rule:
some-subject is a sub topic of some-subject1
that-subject1 is a sub topic of some-topic
-----------------------------------------------------
that-subject is a sub topic of that-topic
20
21. An Easier Future for Semantic Technology
Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery
Text Mining Data Mining Does research on Bob
Another rule:
some-subject is a sub topic of some-subject1 some-person does research into some-subject
that-subject1 is a sub topic of some-topic that-subject is a sub topic of some-topic
----------------------------------------------------- ------------------------------------------------------
that-subject is a sub topic of that-topic that-person does research into that-topic
-- To run or change this example, please point Firefox or IE to the demo OwlResearchOnt at www.reengineeringllc.com
21
22. An Easier Future for Semantic Technology
Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery
Text Mining Data Mining Does research on Bob
Question: Bob does research into some-topic?
22
23. An Easier Future for Semantic Technology
Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery
Text Mining Data Mining Does research on Bob
Question: Bob does research into some-topic?
Bob does research into this-topic
Answer:
===========================
Data Mining
Knowledge Discovery
Semantic Web
-- To run or change this example, please point Firefox or IE to the demo OwlResearchOnt at www.reengineeringllc.com
23
24. An Easier Future for Semantic Technology
Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery
Text Mining Data Mining Does research on Bob
Explanation:
Bob does research into Data Mining
Data Mining is a sub topic of Semantic Web
--------------------------------------------------------
Bob does research into Semantic Web
24
25. An Easier Future for Semantic Technology
Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery
Text Mining Data Mining Does research on Bob
Explanation:
Bob does research into Data Mining
Data Mining is a sub topic of Semantic Web
--------------------------------------------------------
Bob does research into Semantic Web
Data Mining is a sub topic of Knowledge Discovery
Knowledge Discovery is a sub topic of Semantic Web
------------------------------------------------------------------
Data Mining is a sub topic of Semantic Web
25
26. An Easier Future for Semantic Technology
• Combine, in one system for non-expert authors and users
26
27. An Easier Future for Semantic Technology
• Combine, in one system for non-expert authors and users
• Semantics1 - Data Semantics
• the current technology
27
28. An Easier Future for Semantic Technology
• Combine, in one system for non-expert authors and users
• Semantics1 - Data Semantics
• the current technology
• Semantics2 -Mathematical Theory of Declarative Knowledge
• specifies what a reasoner should do
28
29. An Easier Future for Semantic Technology
• Combine, in one system for non-expert authors and users
• Semantics1 - Data Semantics
• the current technology
• Semantics2 -Mathematical Theory of Declarative Knowledge
• specifies what a reasoner should do
• Semantics3 – Natural Language Application Semantics
• English meanings at the Author/User Interface
29
30. Agenda
• The World Wide Database vision
• Only experts have the skills to use the current tools
• An easier future for Semantic Technology -- combine:
– Semantics1 - Data Semantics = the current Technology
– Semantics2 - what a reasoner should do
– Semantics3 - Application Semantics = English meanings at the UI
• A browser-based system for writing and running applications in English
• Examples : Semantics of ontology data, and of oil-industry SQL data
• Google indexes and searches applications that are written in English
• Summary
30
31. A browser-based system for writing and
running applications in English
Semantics3
Who does research
Into the Semantic Web? Business Policy Agents
Writes Business Rules
in open vocabulary
English Directly into a
browser
Runs the Rules Using
the browser
Sees English
explanations of the
Results
End User /
Author
31
32. A browser-based system for writing and
running applications in English
Semantics3
Who does research
Into the Semantic Web?
Writes Business Rules
in open vocabulary
English Directly into a
browser
Runs the Rules Using
the browser
Sees English
explanations
End User / of the Results
Author
Semantics2
Theory of
Declarative
Knowledge
Programmer
32
33. A browser-based system for writing and
running applications in English
Semantics3
Who does research.
Into the Semantic Web?
Writes Business Rules
in open vocabulary
Internet
English Directly into a Business
browser Business Policy Agents
Logic
Runs the Rules Using
the browser
Sees English
Application
explanations Independent
End User / of the Results
Author
Semantics2
Theory of
Declarative
Knowledge
Programmer
33
34. A browser-based system for writing and
running applications in English
Semantics3
Who does research.
Into the Semantic Web?
Writes Business Rules
in open vocabulary
Internet SQL
English Directly into a Business
browser Business Policy Agents
Logic Semantics1
Runs the Rules Using
the browser
Sees English
Application
explanations Independent RDF
End User / of the Results
Author
Semantics2
Theory of
Declarative
Knowledge
Programmer
34
35. Agenda
• The World Wide Database vision
• Only experts have the skills to use the current tools
• An easier future for Semantic Technology -- combine:
– Semantics1 - Data Semantics = the current Technology
– Semantics2 - what a reasoner should do
– Semantics3 - Application Semantics = English meanings at the UI
• A browser-based system for writing and running applications in English
• Examples : Semantics of ontology data, and of oil-industry SQL data
• Google indexes and searches applications that are written in English
• Summary
35
36. Ex 1: English semantics of ontology data
A retailer orders computers from a manufacturer
In the retailer's terminology, a computer is called a PC for Gamers,
while in the manufacturer's terminology, it is called a Prof Desktop.
The retailer and the manufacturer agree that both belong to the class
Worksts/Desktops
Use semantic resolution to find out to what extent a Prof Desktop has
the required memory, CPU and so forth for a PC for Gamers
-- Example based on “Semantic Resolution for E-Commerce”,
by Yun Peng, Youyong Zou, Xiaocheng Luan ( UMBC ) and
Nenad Ivezic, Michael Gruninger and Albert Jones ( NIST )
36
37. Ex 1: English semantics of ontology data
Retailer’s English Manufacturer’s English
negotiable semantic distance
model of the world model of the world
semantic disconnects X
X
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
<rdf:Alt rdf:about="http://retailer.org/node"/>
</rdf:RDF>
negotiable semantic distance
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
<rdf:Alt rdf:about="http://manuf.org/node"/>
</rdf:RDF>
37
38. Ex 1: English semantics of ontology data
A retailer orders computers from a manufacturer -- facts
for the retailer the term PC for Gamers has super-class this-class in the this-ns namespace
==================================================================
Computers to order retailer
Worksts/Desktops shared
Computers shared
38
39. Ex 1: English semantics of ontology data
A retailer orders computers from a manufacturer -- facts
for the retailer the term PC for Gamers has super-class this-class in the this-ns namespace
==================================================================
Computers to order retailer
Worksts/Desktops shared
Computers shared
for the manufacturer the term Prof Desktop has super-class this-class in the this-ns namespace
=====================================================================
Desktop manufacturer
Worksts/Desktops shared
Computer Systems manufacturer
Computers shared
39
40. Ex 1: English semantics of ontology data
A retailer orders computers from a manufacturer -- facts and a rule
for the retailer the term PC for Gamers has super-class this-class in the this-ns namespace
==================================================================
Computers to order retailer
Worksts/Desktops shared
Computers shared
for the manufacturer the term Prof Desktop has super-class this-class in the this-ns namespace
=====================================================================
Desktop manufacturer
Worksts/Desktops shared
Computer Systems manufacturer
Computers shared
for the retailer the term some-item1 has super-class some-class in the some-ns namespace
for the manufacturer the term some-item2 has super-class that-class in the that-ns namespace
----------------------------------------------------------------------------------------------------------------------
the retailer term that-item1 and the manufacturer term that-item2 agree - they are of type that-class
-- To run or change this example, please point Firefox or IE to the demo SemanticResolution1 at www.reengineeringllc.com
40
41. Ex 1: English semantics of ontology data
A retailer orders computers from a manufacturer -- answer table
this-result : retailer this-item1 is matched by manufacturer this-item2 on the property this-prop for part this-comp
====================================================================================
NEED PC for Gamers *missing-item* Size Graphics Card
OK PC for Gamers Prof Desktop Size CPU
OK PC for Gamers Prof Desktop Size Memory
OK PC for Gamers Prof Desktop Size Sound Card
-- To run or change this example, please point Firefox or IE to the demo SemanticResolution1 at www.reengineeringllc.com
41
42. Ex 1: English semantics of ontology data
A retailer orders computers from a manufacturer -- explanation/proof of an answer
retailer PC for Gamers is matched by manufacturer Prof Desktop on the property Size for part Memory
------------------------------------------------------------------------------------------------------------------------------------
OK : retailer PC for Gamers is matched by manufacturer Prof Desktop on the property Size for part Memory
-- To run or change this example, please point Firefox or IE to the demo SemanticResolution1 at www.reengineeringllc.com
42
43. Ex 1: English semantics of ontology data
A retailer orders computers from a manufacturer -- explanation/proof of an answer
retailer PC for Gamers is matched by manufacturer Prof Desktop on the property Size for part Memory
------------------------------------------------------------------------------------------------------------------------------------
OK : retailer PC for Gamers is matched by manufacturer Prof Desktop on the property Size for part Memory
the retailer term PC for Gamers and the manufacturer term Prof Desktop agree - they are of type Worksts/Desktops
for the retailer the term PC for Gamers has part Memory with property Size >= 256 in the shared namespace
for the manufacturer the term Prof Desktop has part Memory with property Size = 512 in the shared namespace
= 512 meets the requirement >= 256
----------------------------------------------------------------------------------------------------------------------------------------------
retailer PC for Gamers is matched by manufacturer Prof Desktop on the property Size for part Memory
-- To run or change this example, please point Firefox or IE to the demo SemanticResolution1 at www.reengineeringllc.com
43
44. Ex 1: English semantics of ontology data
A retailer orders computers from a manufacturer -- explanation/proof of an answer
retailer PC for Gamers is matched by manufacturer Prof Desktop on the property Size for part Memory
------------------------------------------------------------------------------------------------------------------------------------
OK : retailer PC for Gamers is matched by manufacturer Prof Desktop on the property Size for part Memory
the retailer term PC for Gamers and the manufacturer term Prof Desktop agree - they are of type Worksts/Desktops
for the retailer the term PC for Gamers has part Memory with property Size >= 256 in the shared namespace
for the manufacturer the term Prof Desktop has part Memory with property Size = 512 in the shared namespace
= 512 meets the requirement >= 256
----------------------------------------------------------------------------------------------------------------------------------------------
retailer PC for Gamers is matched by manufacturer Prof Desktop on the property Size for part Memory
for the retailer the term PC for Gamers has super-class Worksts/Desktops in the shared namespace
for the manufacturer the term Prof Desktop has super-class Worksts/Desktops in the shared namespace
--------------------------------------------------------------------------------------------------------------------------------------------
the retailer term PC for Gamers and the manufacturer term Prof Desktop agree - they are of type Worksts/Desktops
-- To run or change this example, please point Firefox or IE to the demo SemanticResolution1 at www.reengineeringllc.com
44
45. Ex 1: English semantics of ontology data
Retailer’s English Manufacturer’s English
negotiable semantic distance
model of the world model of the world
semantic disconnects X
X
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
<rdf:Alt rdf:about="http://retailer.org/node"/>
</rdf:RDF>
negotiable semantic distance
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
<rdf:Alt rdf:about="http://manuf.org/node"/>
</rdf:RDF>
45
46. Ex 1: English semantics of ontology data
Retailer’s English Manufacturer’s English
negotiable semantic distance
model of the world model of the world
English explanations bridge the
semantic gap between people
and machines
the retailer term PC for Gamers and for the manufacturer the term Prof Desktop
the manufacturer term Prof Desktop agree - has part Memory with property Size = 512 in
they are of type Worksts/Desktops the shared namespace
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
<rdf:Alt rdf:about="http://retailer.org/node"/>
</rdf:RDF>
negotiable semantic distance
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
<rdf:Alt rdf:about="http://manuf.org/node"/>
</rdf:RDF>
46
47. Agenda
• The World Wide Database vision
• Only experts have the skills to use the current tools
• An easier future for Semantic Technology -- combine:
– Semantics1 - Data Semantics = the current Technology
– Semantics2 - what a reasoner should do
– Semantics3 - Application Semantics = English meanings at the UI
• A browser-based system for writing and running applications in English
• Examples : Semantics of ontology data, and of oil-industry SQL data
• Google indexes and searches applications that are written in English
• Summary
47
48. Ex 2: English semantics of oil-industry SQL data
• A customer needs 1000 gallons of product y in October
• Products x and z can be substituted for product y, but only in the Fall
• Combine products x, y and z to fill the order
• Combination depends on:
• How much of each product is available from each refinery
• Available transportation from each refinery to the customer area
-- Example based on
“Oil Industry Supply Chain Management Using English Business Rules Over SQL”
by Ted Kowalski and Adrian Walker,
www.reengineeringllc.com/Oil_Industry_Supply_Chain_by_Kowalski_and_Walker.pdf
48
49. Ex 2: English semantics of oil-industry SQL data
Facts:
estimated demand this-id in this-region is for this-quantity gallons of this-finished-product in this-month of this-year
===================================================================================
523 NJ 1000 product-y October 2005
49
50. Ex 2: English semantics of oil-industry SQL data
Facts:
estimated demand this-id in this-region is for this-quantity gallons of this-finished-product in this-month of this-year
===================================================================================
523 NJ 1000 product-y October 2005
in this-season an order for this-product1 can be filled with the alternative this-product2
==============================================================
Fall product-y product-x
Fall product-y product-z
50
51. Ex 2: English semantics of oil-industry SQL data
Facts:
estimated demand this-id in this-region is for this-quantity gallons of this-finished-product in this-month of this-year
===================================================================================
523 NJ 1000 product-y October 2005
in this-season an order for this-product1 can be filled with the alternative this-product2
==============================================================
Fall product-y product-x
Fall product-y product-z
in this-month the refinery this-name has committed to schedule this-amount gallons of this-product
=======================================================================
October Shell Canada One 500 product-y
October Shell Canada One 300 product-x
October Shell Canada One 800 product-z
October Shell Canada One 10000 product-w
51
52. Ex 2: English semantics of oil-industry SQL data
Facts:
estimated demand this-id in this-region is for this-quantity gallons of this-finished-product in this-month of this-year
===================================================================================
523 NJ 1000 product-y October 2005
in this-season an order for this-product1 can be filled with the alternative this-product2
==============================================================
Fall product-y product-x
Fall product-y product-z
in this-month the refinery this-name has committed to schedule this-amount gallons of this-product
=======================================================================
October Shell Canada One 500 product-y
October Shell Canada One 300 product-x
October Shell Canada One 800 product-z
October Shell Canada One 10000 product-w
we have this-method transportation from refinery this-name to region this-region
==========================================================
truck Shell Canada One NJ
rail Shell Canada One NJ
52
53. Ex 2: English semantics of oil-industry SQL data
Rules:
estimated demand some-id in some-region is for some-quantity gallons of some-finished-product
in some-month of some-year
for estimated demand that-id some-fraction of the order will be some-product from some-refinery
that-quantity * that-fraction = some-amount
------------------------------------------------------------------------------------------------------------------------------------------------
for demand that-id that-region for that-quantity that-finished-product we use that-amount that-product from that-refinery
53
54. Ex 2: English semantics of oil-industry SQL data
Rules:
estimated demand some-id in some-region is for some-quantity gallons of some-finished-product
in some-month of some-year
for estimated demand that-id some-fraction of the order will be some-product from some-refinery
that-quantity * that-fraction = some-amount
------------------------------------------------------------------------------------------------------------------------------------------------
for demand that-id that-region for that-quantity that-finished-product we use that-amount that-product from that-refinery
estimated demand some-id in some-region is for some-quantity gallons of some-finished-product
in some-month of some-year
for demand that-id for that-finished-product refinery some-refinery can supply some-amount gallons of some-product
for demand that-id the refineries have altogether some-total gallons of acceptable base products
that-amount / that-total = some-long-fraction
that-long-fraction rounded to 2 places after the decimal point is some-fraction
----------------------------------------------------------------------------------------------------------------
for estimated demand that-id that-fraction of the order will be that-product from that-refinery
54
55. Ex 2: English semantics of oil-industry SQL data
Rules:
estimated demand some-id in some-region is for some-quantity gallons of some-finished-product
in some-month of some-year
for estimated demand that-id some-fraction of the order will be some-product from some-refinery
that-quantity * that-fraction = some-amount
------------------------------------------------------------------------------------------------------------------------------------------------
for demand that-id that-region for that-quantity that-finished-product we use that-amount that-product from that-refinery
estimated demand some-id in some-region is for some-quantity gallons of some-finished-product
in some-month of some-year
for demand that-id for that-finished-product refinery some-refinery can supply some-amount gallons of some-product
for demand that-id the refineries have altogether some-total gallons of acceptable base products
that-amount / that-total = some-long-fraction
that-long-fraction rounded to 2 places after the decimal point is some-fraction
----------------------------------------------------------------------------------------------------------------
for estimated demand that-id that-fraction of the order will be that-product from that-refinery
estimated demand some-id in some-region is for some-amount gallons of some-product in some-month of some-year
sum a-num :
for demand that-id for that-product refinery some-name can supply some-num gallons of some-product1 = a-total
-------------------------------------------------------------------------------------------------------------------------
for demand that-id the refineries have altogether that-total gallons of acceptable base products
55
56. Ex 2: English semantics of oil-industry SQL data
An answer table:
for demand this-id this-region for this-quantity this-finished-product we use this-amount this-product from this-refinery
======================================================================================
523 NJ 1000 product-y 190.0 product-x Shell Canada One
523 NJ 1000 product-y 310.0 product-y Shell Canada One
523 NJ 1000 product-y 500.0 product-z Shell Canada One
To run or change this example, please point Firefox or IE to the demo Oil-IndustrySupplyChain1 at www.reengineeringllc.com
56
57. Ex 2: English semantics of oil-industry SQL data
An explanation:
estimated demand 523 in NJ is for 1000 gallons of product-y in October of 2005
for estimated demand 523 0.19 of the order will be product-x from Shell Canada One
1000 * 0.19 = 190
------------------------------------------------------------------------------------------------------
for demand 523 NJ for 1000 product-y we use 190 product-x from Shell Canada One
57
58. Ex 2: English semantics of oil-industry SQL data
An explanation:
estimated demand 523 in NJ is for 1000 gallons of product-y in October of 2005
for estimated demand 523 0.19 of the order will be product-x from Shell Canada One
1000 * 0.19 = 190
------------------------------------------------------------------------------------------------------
for demand 523 NJ for 1000 product-y we use 190 product-x from Shell Canada One
estimated demand 523 in NJ is for 1000 gallons of product-y in October of 2005
for demand 523 for product-y refinery Shell Canada One can supply 300 gallons of product-x
for demand 523 the refineries have altogether 1600 gallons of acceptable base products
300 / 1600 = 0.1875
0.1875 rounded to 2 places after the decimal point is 0.19
------------------------------------------------------------------------------------------------------------------
for estimated demand 523 0.19 of the order will be product-x from Shell Canada One
58
59. Ex 2: English semantics of oil-industry SQL data
An explanation:
estimated demand 523 in NJ is for 1000 gallons of product-y in October of 2005
for estimated demand 523 0.19 of the order will be product-x from Shell Canada One
1000 * 0.19 = 190
------------------------------------------------------------------------------------------------------
for demand 523 NJ for 1000 product-y we use 190 product-x from Shell Canada One
estimated demand 523 in NJ is for 1000 gallons of product-y in October of 2005
for demand 523 for product-y refinery Shell Canada One can supply 300 gallons of product-x
for demand 523 the refineries have altogether 1600 gallons of acceptable base products
300 / 1600 = 0.1875
0.1875 rounded to 2 places after the decimal point is 0.19
------------------------------------------------------------------------------------------------------------------
for estimated demand 523 0.19 of the order will be product-x from Shell Canada One
estimated demand 523 in NJ is for 1000 gallons of product-y in October of 2005
sum eg-amount :
for demand 523 for product-y refinery eg-refinery can supply eg-amount gallons of eg-product1 = 1600
---------------------------------------------------------------------------------------------------------------------------------
for demand 523 the refineries have altogether 1600 gallons of acceptable base products
To run or change this example, please point Firefox or IE to the demo Oil-IndustrySupplyChain1 at www.reengineeringllc.com
59
60. Ex 2: English semantics of oil-industry SQL data
Rules for finding SQL data on the Internet:
A data table
we have this-method transportation from refinery this-name to region this-region
==========================================================
truck Shell Canada One NJ
rail Shell Canada One NJ
A rule that says how to find the table on the internet
url:www.example.com dbms:9i dbname:ibldb tablename:T1 port:1521 id:anonymous password:oracle
-----------------------------------------------------------------------------------------------------------------------------------
we have this-method transportation from refinery this-name to region this-region
To run or change this example, please point Firefox or IE to the demo Oil-IndustrySupplyChain1 at www.reengineeringllc.com
60
61. Ex 2: English semantics of oil-industry SQL data
Semantics3
Who does research.
Into the Semantic Web?
Writes Business Rules Internet SQL
in open vocabulary
English Directly into a Business
browser
Logic Semantics1
Runs the Rules Using
the browser
Application
Sees English Independent
explanations
RDF
End User / of the Results
Author
Semantics2
Theory of
Declarative
Knowledge
Programmer
61
62. Ex 2: English semantics of oil-industry SQL data
A SQL query generated automatically from the business rules:
select distinct x6,T2.PRODUCT,T1.NAME,T2.AMOUNT,x5 from
T6 tt1,T6 tt2,T5,T4,T3,T2,T1,T6,
(select x3 x6,T6.FINISHED_PRODUCT x7,T6.ID x8,tt1.ID x9,tt2.ID x10,sum(x4) x5 from
T6,T6 tt1,T6 tt2,
((select T6.ID x3,T3.PRODUCT1,T1.NAME,T2.AMOUNT x4,T2.PRODUCT from
T1,T2,T3,T4,T5,T6,T6 tt1,T6 tt2 where
T1.NAME=T2.NAME and T1.REGION=T6.REGION and T2.MONTH1=T4.MONTH1 and
T2.MONTH1=T6.MONTH1 and T2.PRODUCT=T3.PRODUCT2 and T4.MONTH1=T6.MONTH1 and
T3.PRODUCT1=T6.FINISHED_PRODUCT and T3.SEASON=T4.SEASON and T3.SEASON=T5.SEASON and
T4.SEASON=T5.SEASON and T6.ID=tt1.ID and T6.ID=tt2.ID and tt1.ID=tt2.ID)
union
(select T6.ID x3,T2.PRODUCT,T1.NAME,T2.AMOUNT x4,T2.PRODUCT from
T1,T2,T3,T4,T5,T6,T6 tt1,T6 tt2 where
T1.NAME=T2.NAME and T1.REGION=T6.REGION and T2.MONTH1=T6.MONTH1 and
T2.PRODUCT=T6.FINISHED_PRODUCT and T6.ID=tt1.ID and T6.ID=tt2.ID and tt1.ID=tt2.ID)
) group by T6.FINISHED_PRODUCT,T6.ID,tt1.ID,tt2.ID,x3) where
T6.ID=tt2.ID and tt1.ID=T6.ID and T6.FINISHED_PRODUCT=x7 and T6.ID=x8 and tt1.ID=x8 and
tt2.ID=x8 and T1.NAME=T2.NAME and T1.REGION=tt2.REGION and T2.MONTH1=T4.MONTH1 and
T2.MONTH1=tt2.MONTH1 and T2.PRODUCT=T3.PRODUCT2 and
T3.PRODUCT1=tt1.FINISHED_PRODUCT and T3.PRODUCT1=tt2.FINISHED_PRODUCT and
T3.SEASON=T4.SEASON and T3.SEASON=T5.SEASON and T4.MONTH1=tt2.MONTH1 and
T4.SEASON=T5.SEASON and T6.ID=x6 and tt1.FINISHED_PRODUCT=tt2.FINISHED_PRODUCT and
tt1.ID=tt2.ID and tt1.ID=x6 and tt2.ID=x6
order by x6,T2.PRODUCT,T1.NAME,T2.AMOUNT,x5;
62
63. Ex 2: English semantics of oil-industry SQL data
• It would be difficult to write the SQL query on the previous slide by hand, or
to manually reconcile it with the business knowledge specified in the rules.
• How do we know that the automatically generated SQL yields results
that are correct with respect to the business rules ?
The concern is eased by the fact that we can get step-by-step
business level English explanations
63
64. Ex 2: English semantics of oil-industry SQL data
• Could a programmer write more readable SQL by hand ?
Yes, but we would need to add comments in English to help people to
reconcile the hand-written query with the business knowledge
By their nature, the comments would not be used during machine processing,
so the correctness of the hand written-SQL would rely on lengthy,
and perhaps error prone, manual verification
Comments are sometimes not kept up to date when the code that they
supposedly document is changed
• The situation with SPARQL is similar
64
65. Agenda
• The World Wide Database vision
• Only experts have the skills to use the current tools
• An easier future for Semantic Technology -- combine:
– Semantics1 - Data Semantics = the current Technology
– Semantics2 - what a reasoner should do
– Semantics3 - Application Semantics = English meanings at the UI
• A browser-based system for writing and running applications in English
• Examples : Semantics of ontology data, and of oil-industry SQL data
• Google indexes and searches applications that are written in English
• Summary
65
66. Google indexes and searches
applications that are written in English
Search: for estimated demand that-id fraction of the order
66
67. Google indexes and searches
applications that are written in English
Search: for estimated demand that-id fraction of the order
Search: for estimated demand that-id fraction of the order
Result:
67
68. Google indexes and searches
applications that are written in English
Search: for estimated demand that-id fraction of the order
Search: for estimated demand that-id fraction of the order
Result:
The executable English rules
and facts that define the application
A paper that describes
the application
68
69. Summary
• The World Wide Database vision
– all the data in the world as one database
• Only experts have the skills to use the current tools
– OwlResearchOnt -- Bob does research into Semantic Web
• An easier future for Semantic Technology -- combine:
– Semantics1 - Data Semantics = the current Technology
– Semantics2 - what a reasoner should do
– Semantics3 - Application Semantics = English meanings at the UI
• A browser-based system for writing and running applications in English
• Examples : Semantics of ontology data, and of oil-industry SQL data
• Google indexes and searches applications that are written in English
69
70. Links
1. The NIST / UMBC paper listed in the presentation can be downloaded from :
http://www.mel.nist.gov/msidlibrary/publications.html
2. What a reasoner should do:
Backchain Iteration: Towards a Practical Inference Method that is Simple Enough to be Proved
Terminating, Sound and Complete. Journal of Automated Reasoning, 11:1-22.
3 . Video about interactions between drugs www.reengineeringllc.com/ibldrugdbdemo1.htm
4. Video about energy independence www.reengineeringllc.com/EnergyIndependence1Video.htm
5. The English inferencing examples
OwlResearchOnt SemanticResolution1 Oil-IndustrySupplyChain1 Oil-IndustrySupplyChain1MySql1
(and many other examples provided) can be run, changed, and re-run as follows:
1. Point Firefox or IE to www.reengineeringllc.com
2. Click on Internet Business Logic
3. Click on the GO button
4. Click on the Help button to see how to navigate through the pages
5. Select OwlResearchOnt
6. You are cordially invited to write and run your own examples. Shared use of the system is free.
70