Ontology aware software service agents meeting ordinary user needs on the semantic web
Download
1 / 47

Ontology Aware Software Service Agents: Meeting Ordinary User Needs on the Semantic Web - PowerPoint PPT Presentation


  • 96 Views
  • Uploaded on

Ontology Aware Software Service Agents: Meeting Ordinary User Needs on the Semantic Web. Muhammed J. Al-Muhammed Brigham Young University. Supported by:. July 23, 2007. A Challenge for Semantic Web Services. Help users find and use services Reduce requirements for service specification.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Ontology Aware Software Service Agents: Meeting Ordinary User Needs on the Semantic Web' - knut


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Ontology aware software service agents meeting ordinary user needs on the semantic web

Ontology Aware Software Service Agents: Meeting Ordinary User Needs on the Semantic Web

Muhammed J. Al-Muhammed

Brigham Young University

Supported by:

July 23, 2007


A challenge for semantic web services
A Challenge for Semantic Web Services User Needs on the Semantic Web

  • Help users find and use services

  • Reduce requirements for service specification

What’s the weather forecast for Springfield, Illinois, between the 24th and 27th?


Weather forecasting service
Weather Forecasting Service User Needs on the Semantic Web

Access to the National Digital Forecast Database


Problems with web services

Know how to use it User Needs on the Semantic Web

39.78

-89.66

2007-04-21

4

Problems with Web Services

Access to the National Digital Forecast Database

Discover the required service

Coupling problem

Communicate: keep the communication until the service responds

What is the weather forecast for Springfield, Illinois, between the 24th and 27th?

What is the weather forecast for Springfield, Illinois, between the 24th and 27th?

What is the weather forecast for Springfield, Illinois, between the 24th and 27th?

Data heterogeneity problem


Better invoke services only by specifying requests
Better: Invoke Services only by Specifying Requests User Needs on the Semantic Web

The service recognizes constraints

And services the request


Resolution ontology based web services
Resolution: User Needs on the Semantic WebOntology-Based Web Services

  • Domain ontology

    • Declares concepts, relationships, and constraints

    • Has a central concept of interest

    • Has extensional recognizers

  • Process ontology

    • Matches a request with a domain ontology

    • Obtains information (from DB and from user)

    • Satisfies constraints

    • Negotiates (if necessary)


Domain ontology
Domain Ontology User Needs on the Semantic Web


Domain ontology1
Domain Ontology User Needs on the Semantic Web

Formal predicates:

object sets,relationship sets,& constraints

NumDays(x)

WeatherReport(x) is for Latitude(y)

x(WeatherReport(x) 

1y(WeatherReport(x) is for Latitude(y))


Extensional semantics
Extensional Semantics User Needs on the Semantic Web

  • Ontology augmented with data frames

  • A data frame specifies semantics for a concept

    • Its internal and external representation

    • Its contextual keywords or phrases

    • Operations along with contextual keywords or phrases


Data frames
Data Frames User Needs on the Semantic Web

NumDays

internal representation: integer

default value: (1) …

Data frames: instance recognition;

operation recognition

StartDate

internal representation: date -- format yyyy-mm-dd

default value: (today)

text representation: {monthName}\s+([0]?[1-9] |

[12]\d|3[01])(\s*\,)?\s+\d{4} | (the\s+)?([0]?[1-9] |

[12]\d|3[01])\s*(th|...)|...

toInternalRepresentation(x:string) returns (StartDate)

NrDaysBetween(x1:StartDate, x2:EndDate)

returns (NumDays)

context keywords/phrases:

between the \s+{x1}\s+and\s+{x2} | ...


Process ontology
Process Ontology User Needs on the Semantic Web

  • Create service-request view

  • Generate constraints

  • Obtain information

    • From system

    • From user

  • Satisfy constraints

  • Negotiate

  • Finalize service request


Ontology based constraint recognition
Ontology-Based Constraint Recognition User Needs on the Semantic Web

What’s the weather forecast for Springfield, Illinois, between the 24th and 27th?


Ontology based constraint recognition1
Ontology-Based Constraint Recognition User Needs on the Semantic Web

WeatherReport

text representation:

weather\s+forecast|…

What’s the weather forecast for Springfield, Illinois, between the 24th and 27th?


Ontology based constraint recognition2
Ontology-Based Constraint Recognition User Needs on the Semantic Web

Longitude

...

getLongitude(x1:State, x2:City)

returns (Longitude)

What’s the weather forecast for Springfield, Illinois, between the 24th and 27th?


Ontology based constraint recognition3
Ontology-Based Constraint Recognition User Needs on the Semantic Web

StartDate ...

NrDaysBetween(x1:StartDate, x2:EndDate)

returns (NumDays)

context keywords/phrases:

between the \s+{x1}\s+and\s+{x2} | ...

What’s the weather forecast for Springfield, Illinois, between the 24th and 27th?


Ontology based constraint recognition4
Ontology-Based Constraint Recognition User Needs on the Semantic Web

Format …

Default value:

“24 Hourly” …

What’s the weather forecast for Springfield, Illinois, between the 24th and 27th?


Ontology based constraint recognition5
Ontology-Based Constraint Recognition User Needs on the Semantic Web

What’s the weather forecast for Springfield, Illinois, between the 24th and 27th?


Generated rel calculus query
Generated Rel. Calculus Query User Needs on the Semantic Web

What’s the weather forecast for Springfield, Illinois, between the 21st and 24th?

  • { <x2, x3, x4> |

  • WeatherReport(x0) is for Latitude(getLatitude(“Illinois”, “Springfield”))

  • WeatherReport(x0) is for Longitude(getLongitude(“Illinois”, “Springfield”))

  • WeatherReport(x0) starts on StartDate(NextDate(“24th”))

  • WeatherReport(x0) is for NumDays(NrDaysBetween(NextDate(“24th”), NextDate(“27th”)))

  • WeatherReport(x0) has Format(“24 Hourly”)

  • WeatherReport(x0) produces ReportPeriod(x1)

  • ReportPeriod(x1) has MaximumTemperature(x2)

  • ReportPeriod(x1) has MinimumTemperature(x3)

  • ReportPeriod(x1) has PercentChanceOfPrecipitation(x4) }


Service request result
Service Request Result User Needs on the Semantic Web


Weather service demo
Weather Service Demo User Needs on the Semantic Web

Demo


Too many cars
Too Many Cars User Needs on the Semantic Web

“I want a dodge, a 2000 or newer. The Mileage should be less

than80,000 and the price shouldnotexceed$15,000.”

www.cars.com, November 2005


No car
No Car User Needs on the Semantic Web

“I want a dodge, a2000 or newer. The Mileage should be less

than 80,000 and the price should not exceed $4,000.”

www.cars.com, November 2005


Constraint satisfaction
Constraint Satisfaction User Needs on the Semantic Web

  • Exactly one solution: return it as the result

  • A few solutions: return all and ask the user to select one

  • Too many solutions: resolve

  • No solution: resolve


Key observations
Key Observations User Needs on the Semantic Web

  • Some (near) solutions are better than others

  • People specify constraints on some concepts in a domain more often than on other concepts


Fundamental concepts reward penalty and expectation
Fundamental Concepts: User Needs on the Semantic WebReward, Penalty, and Expectation

  • Reward: non-negative real number denoting a degree of satisfaction

  • Penalty: negative real number denoting a degree of violation

  • Expectation: probability of specifying a constraint on a concept


Fundamental concepts pareto optimality
Fundamental Concepts: User Needs on the Semantic WebPareto Optimality

  • Based on dominance relations

    • The reward for S1 is as high as the reward for S2

    • For at least one reward S1 has a higher reward

  • Dominating solutions are Pareto optimal


Too many solutions reward based ordering
Too Many Solutions: User Needs on the Semantic WebReward-Based Ordering

  • Calculate rewards and combine them

  • Order solutions, highest combined reward first

  • Select the top-m Pareto optimal solutions


Too many solutions expectation based constraint elicitation
Too Many Solutions: Expectation-Based Constraint Elicitation User Needs on the Semantic Web

  • Associate expectations with domain concepts

  • Order the concepts in a domain based on their expectations

    • Example: make > price > model > …

  • Elicit additional constraints over unconstrained concepts

    • Example: if no preferred make provided, ask for make; if no price, ask for price; …


No solution penalty based ordering
No Solution: User Needs on the Semantic WebPenalty-Based Ordering

  • Calculate penalties and combine them

  • Order near solutions, lowest combined penalty first

  • Select the top-m Pareto optimal near solutions


No solution expectation based constraint relaxation
No Solution: Expectation-Based Constraint Relaxation User Needs on the Semantic Web

  • Associate expectations with constraints

  • Order constraints based on their expectation, lowest expectation first

  • Trade-off: amount of violation vs. expectation


No solution expectation based constraint relaxation1
No Solution: Expectation-Based Constraint Relaxation User Needs on the Semantic Web

“I want a dodge, a 2000 or newer. The Mileage should be less

than 80,000 and the price should not exceed $4,000.”

Can this constraint “shouldnotexceed$4,000” be relaxed to “$4,100”?


Free form ontology based web service demo
Free-form Ontology-Based Web Service Demo User Needs on the Semantic Web

Demo

Demo


Experiments
Experiments User Needs on the Semantic Web

  • Constraint recognition

  • Constraint resolution

    • Solution ordering

    • Near solution ordering

  • System usability


Experiment constraint recognition
Experiment: Constraint Recognition User Needs on the Semantic Web

  • Subjects: BYU students

  • Domains:

    • Appointment scheduling

    • Car purchase

    • Apartment rental

  • Requests:

Requests Predicates Arguments

Appointment 10 126 34

Car 15 315 98

Apartment 6 107 38

Totals 31 548 170


Results constraint recognition
Results: Constraint Recognition User Needs on the Semantic Web

Recall Precision

Appointment predicates 0.98 1.00

arguments 0.94 1.00

Car predicates 0.99 0.99

arguments 0.98 0.99

Apartment predicates 0.97 1.00

arguments 0.92 1.00

All predicates 0.98 0.99

arguments 0.95 0.99


Results constraint recognition1
Results: Constraint Recognition User Needs on the Semantic Web

Recall Precision

Appointment predicates 0.98 1.00

arguments 0.94 1.00

Car predicates 0.99 0.99

arguments 0.98 0.99

Apartment predicates 0.97 1.00

arguments 0.92 1.00

All predicates 0.98 0.99

arguments 0.95 0.99

e.g. missed:

“any Monday of this month”

“most days of the week”

“a nook”

“extra storage”


Results constraint recognition2
Results: Constraint Recognition User Needs on the Semantic Web

Recall Precision

Appointment predicates 0.98 1.00

arguments 0.94 1.00

Car predicates 0.99 0.99

arguments 0.98 0.99

Apartment predicates 0.97 1.00

arguments 0.92 1.00

All predicates 0.98 0.99

arguments 0.95 0.99

e.g. missed:

“I want a Toyota with a cheap price, 2000

would be great …”

The system incorrectly concluded that

“2000” was a price.


Experiment constraint resolution
Experiment: Constraint Resolution User Needs on the Semantic Web

  • Tested appointment and car-purchase domains

  • 16 human subjects

    • The best-5 near solutions from 19 appointments

    • The best-5 solutions from 32 cars

  • Compared human selection with system selection


Results constraint resolution
Results: Constraint Resolution User Needs on the Semantic Web

(appointment domain)

 = 0. 74 (“substantial agreement”)

Observer-agreement test:


Results constraint resolution1
Results: Constraint Resolution User Needs on the Semantic Web

(car-purchase domain)

 = 0.67 (“substantial agreement”)

Observer-agreement test:


Experiment system usability
Experiment: User Needs on the Semantic WebSystem Usability

  • 12 subjects: 8 in the senior-database class and 4 in the master’s program

  • Evaluated functionalities

    • Regular specification (only AND constraints)

    • Advanced specification (AND, OR, & Negation)

    • Constraint elicitation suggestions

    • Constraint relaxation suggestions

    • Best-k solutions

    • Best-k near solutions

    • Usefulness of the system


Results request specification
Results: User Needs on the Semantic WebRequest Specification

Regular specification is enough to specify all my requests.

Without disjunctions and negations I was not able to specify my requests


Results constraint elicitation and relaxation
Results: User Needs on the Semantic WebConstraint Elicitation and Relaxation


Results solution and near solution ordering
Results: User Needs on the Semantic WebSolution and Near Solution Ordering


Results system usefulness
Results: User Needs on the Semantic WebSystem Usefulness


Contributions
Contributions User Needs on the Semantic Web

  • Free-form service specification

  • Effective constraint resolution

  • Knowledge-based service creation

    • Only static knowledge

    • No code required

  • Ontology-based web services

    • Service decoupling resolution

    • Data heterogeneity resolution

  • Fully working prototypes


Future work
Future Work User Needs on the Semantic Web

  • Conditional constraints

    Example: if the appointment can be scheduled this week, schedule with Dr. Carter; otherwise schedule with Dr. Adams

  • Composite service requests

    Example: coordinated multi-ontology instantiations (vacation planning)

  • Trust: predictability, transparency, etc.

  • Security: secure information exchange


ad