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Recognition and Satisfaction of Constraints in Free-Form Task Specification

Recognition and Satisfaction of Constraints in Free-Form Task Specification. Muhammed Al-Muhammed. Motivation. Semantic web promises automated tools to do tasks The challenge: how ordinary users deliver tasks to these tools Free-form text specification is a routine practice .

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Recognition and Satisfaction of Constraints in Free-Form Task Specification

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  1. Recognition and Satisfaction of Constraints in Free-Form Task Specification Muhammed Al-Muhammed

  2. Motivation • Semantic web promises automated tools to do tasks • The challenge: how ordinary users deliver tasks to these tools • Free-form text specification is a routine practice

  3. Thesis Statement • We can recognize required information and constraints in a free-form text task specification • We can transform the satisfiability into a satisfaction of a database query

  4. Approach • Task ontology • Domain ontology • Process ontology • Characteristics • Request recognition: find best task ontology • Recognize the required information and the imposed constraints • Transform their satisfaction into a regular data base query satisfaction • The required information become SELECT part of the query • The constraints become WHERE part of the query

  5. Domain Ontology

  6. Domain Ontology • Augmented with data frames • A data frame defines information about a concept • Its internal and external representation • Its contextual keywords or phrases • Operations along with contextual keywords or phrases

  7. Data Frames

  8. Process Ontology • A domain-independent process to handle the recognition and satisfaction of the constraints • Statenet • States • Transitions, based on ECA rules • Can be specialized to a domain

  9. Task Ontology Recognition Appointment … context keywords/phrase: “appointment |want to see a |…” Dermatologist … context keywords/phrases: “([D|d]ermatologist) | …” I want to see a dermatologist next week; any day would be ok for me, at 4:00 p.m. The dermatologist must be within 20 miles from my home and must accept my insurance.

  10. Task Ontology Recognition Appointment … context keywords/phrase: “appointment |want to see a |…” Dermatologist … context keywords/phrases: “([D|d]ermatologist) | …”  I want to see a dermatologist next week; any day would be ok for me, at 4:00 p.m. The dermatologist must be within 20 miles from my home and must accept my insurance.

  11. Task Ontology Recognition Appointment … context keywords/phrase: “appointment |want to see a |…” Dermatologist … context keywords/phrases: “([D|d]ermatologist) | …”        I want to see adermatologistnext week; any day would be ok for me, at 4:00p.m. The dermatologist must be within 20 miles from my home and must accept my insurance. 

  12. Task Ontology Recognition Date … NextWeek(d1: Date, d2: Date) returns (Boolean) context keywords/phrases: next week | week from now | … Distance internal representation : real textual representation: ((\d+(\.\d+)?)|(\.\d+)) context keywords/phrases: miles | mile | kilometers | … LessThanOrEqual(d1: Distance, “20”) returns (Boolean) context keywords/phrases: within | not more than |  | … return (d1d2) … end I want to see adermatologistnext week; any day would be ok for me, at 4:00 p.m. The dermatologist must be within 20 miles from my home and must accept my insurance.

  13. Recognition of Required Information: Task View • Required information • The Mandatory concepts w.r.t. the primary concept • Marked concepts • Heuristic-baser reasoning to remove spurious objects • Conflict resolution heuristic • Isolated object heuristic         I want to see adermatologistnext week; any day would be ok for me, at 4:00 p.m. The dermatologist must be within 20 miles from my home and must accept my insurance.

  14. Recognition of Required Information: Task View

  15. Recognition of the Constraints • Potential constraints are the marked Boolean operations • NextWeek(d: Date) • LessThanOrEqual(d1: Distance, “20”) • LessThan(d1: Distance, “20”) • Time = “4:00” • … • Heuristic-based reasoning to remove the spurious constraints • Subsumption heuristic • …

  16. LessThanOrEqual d1: Distance “20” DistanceBetween a1: Address a2: Address Recognition of the Constraints • Dependency graphs to capture dependency between • Constraints • Input parameters and the task view NextWeek d: Date

  17. LessThanOrEqual d1: Distance “20” DistanceBetween a1: Address a2: Address Satisfaction of the Constraints • Querying the database SELECT D.Name, D.Insurance, D.Address, A.Date, A.Time FROM Dermatologist D, Appointment A WHERE Time=“4:00” and NextWeek(Date) • Observe that the constraint LessThanOrEqual(.,.) cannot be checked: need values from the user • The remaining values are the model of the constraints  NextWeek d: Date

  18. Contributions • Recognition required information and imposed constraints in free-form task specifications • Transform the constraints satisfaction to database query satisfaction • Recognizing and gathering missing information from databases and users

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