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Enhancing Task Specification Satisfaction through Free-Form Text Recognition

This research addresses the challenges of enabling ordinary users to effectively deliver tasks to automated tools using free-form text specifications. We propose methods to recognize required information and constraints within these specifications and transform the satisfiability into database query satisfaction. By utilizing task and domain ontologies, we aim to facilitate better requests for appointments and services while ensuring that user-defined constraints are met. Our findings demonstrate how contextual keywords can optimize query processing and improve user interaction with automated tools.

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Enhancing Task Specification Satisfaction through Free-Form Text Recognition

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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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