Application Semantics via Rules in Open Vocabulary English

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

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

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

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


• 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

Retailer’s English Manufacturer’s English
model of the world negotiable semantic distance
model of the world

6


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>

<rdf:Alt rdf:about="http://manuf.org/node"/>
</rdf:RDF>

7



semantic disconnects X
X

</rdf:RDF>

</rdf:RDF>

8



X

</rdf:RDF>

</rdf:RDF>

9

Agenda





– Semantics3 - Application Semantics = English meanings at the UI



• Google indexes and searches applications that are written in English

• Summary

10

Only Experts have the Skills to
Use the Current Tools
Semantic
Web
Sub Topic
Knowledge
Discovery

Text Mining Data Mining

11

Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery

Text Mining Data Mining Bob

12

Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery

Text Mining Data Mining Does research on Bob

13

Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery


New user asked: how can I use RDF and Owl to find out from the above that
“Bob does research into Semantic Web” ?

14

Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery


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

Agenda









• Summary

16

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


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


Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery


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


Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery


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


Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery


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

Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery


Question: Bob does research into some-topic?

22

Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery


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


Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery


Explanation:
Bob does research into Data Mining
Data Mining is a sub topic of Semantic Web
--------------------------------------------------------
Bob does research into Semantic Web

24


Semantic Researcher
Web
Instance
Sub Topic
Knowledge
Adrian Claire
Discovery


Explanation:
Bob does research into Data Mining
--------------------------------------------------------
Bob does research into Semantic Web

Data Mining is a sub topic of Knowledge Discovery
Knowledge Discovery is a sub topic of Semantic Web
------------------------------------------------------------------
25


• Combine, in one system for non-expert authors and users

26



• Semantics1 - Data Semantics

• the current technology

27





• Semantics2 -Mathematical Theory of Declarative Knowledge

• specifies what a reasoner should do

28





• 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

Agenda









• Summary

30

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

Semantics3
Who does research
Into the Semantic Web?
in open vocabulary
English Directly into a
browser

the browser

Sees English
explanations
End User / of the Results
Author

Semantics2

Theory of
Declarative
Knowledge

Programmer
32

Semantics3
Who does research.
in open vocabulary
Internet
English Directly into a Business
browser Business Policy Agents
Logic
the browser

Sees English
Application
explanations Independent
Author

Semantics2

Theory of
Declarative
Knowledge

Programmer
33

Semantics3
Who does research.
in open vocabulary
Internet SQL
browser Business Policy Agents
Logic Semantics1
the browser

Sees English
Application
explanations Independent RDF
Author

Semantics2

Theory of
Declarative
Knowledge

Programmer
34

Agenda









• Summary

35

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



X

</rdf:RDF>

</rdf:RDF>

37

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

A retailer orders computers from a manufacturer -- facts

==================================================================
Computers shared

for the manufacturer the term Prof Desktop has super-class this-class in the this-ns namespace
=====================================================================
Desktop manufacturer
Computer Systems manufacturer
Computers shared

39

A retailer orders computers from a manufacturer -- facts and a rule

==================================================================
Computers shared

for the manufacturer the term Prof Desktop has super-class this-class in the this-ns namespace
=====================================================================
Desktop manufacturer
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


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


41

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


42


------------------------------------------------------------------------------------------------------------------------------------

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
----------------------------------------------------------------------------------------------------------------------------------------------


43


------------------------------------------------------------------------------------------------------------------------------------

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
----------------------------------------------------------------------------------------------------------------------------------------------

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
--------------------------------------------------------------------------------------------------------------------------------------------


44



X

</rdf:RDF>

</rdf:RDF>

45



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>

</rdf:RDF>

46

Agenda









• Summary

47

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

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

Facts:
===================================================================================

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

Facts:
===================================================================================

==============================================================

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

Facts:
===================================================================================

==============================================================

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

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

Rules:
------------------------------------------------------------------------------------------------------------------------------------------------

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

Rules:
------------------------------------------------------------------------------------------------------------------------------------------------

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


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

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

An explanation:

1000 * 0.19 = 190
------------------------------------------------------------------------------------------------------

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
------------------------------------------------------------------------------------------------------------------

58

An explanation:

1000 * 0.19 = 190
------------------------------------------------------------------------------------------------------

for demand 523 for product-y refinery Shell Canada One can supply 300 gallons of product-x
300 / 1600 = 0.1875
0.1875 rounded to 2 places after the decimal point is 0.19
------------------------------------------------------------------------------------------------------------------

sum eg-amount :
for demand 523 for product-y refinery eg-refinery can supply eg-amount gallons of eg-product1 = 1600
---------------------------------------------------------------------------------------------------------------------------------

59

Rules for finding SQL data on the Internet:

A data table

==========================================================
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
-----------------------------------------------------------------------------------------------------------------------------------


60

Semantics3
Who does research.
Writes Business Rules Internet SQL
in open vocabulary
browser
Logic Semantics1
the browser
Application
Sees English Independent
explanations
RDF
Author

Semantics2

Theory of
Declarative
Knowledge

Programmer
61

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


• 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

• 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

Agenda









• Summary

65

Google indexes and searches
applications that are written in English
Search: for estimated demand that-id fraction of the order

66



Result:

67



Result:

The executable English rules
and facts that define the application

A paper that describes
the application

68

Summary

– all the data in the world as one database


– OwlResearchOnt -- Bob does research into Semantic Web







69

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

Application Semantics via Rules in Open Vocabulary English

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