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Intelligent Brokering for Knowledge-Component Reuse: The IBROW Approach

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parry
38 diapositives · 532 Views · Sep 24, 2014

This document presents the IBROW project, an innovative service developed by Richard Benjamins and partners that enables the dynamic reuse of knowledge components via brokering on the web. Emphasis is placed on problem-solving methods (PSMs) and ontologies to facilitate interoperability among heterogeneous components. Key elements include a standard product description language, the use of UPML, and approaches to integrate various reasoning services. The project aims to lower costs and improve access to knowledge engineering technology, ultimately establishing a new electronic marketplace for dynamic knowledge reuse.

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  1. Problem-Solving Methodsin Perspective Richard Benjamins University of Amsterdam

  2. In perspective? • Knowledge components • PSMs and Ontologies • A standard for characterizing PSMs? • PSMs in Cyberspace • From ideas to practice: a community effort

  3. IBROW An Intelligent Brokering Service for Knowledge-Component Reuse on the World-Wide Web Richard Benjamins

  4. Objectives of IBROW • Develop an intelligent brokering service that enables third party knowledge-component reuse through the Web • Configurable reasoning services on the web • Plug & Play of PSMs and Ontologies

  5. The agents involved

  6. Innovative aspects • Most web services broker static information • Metacrawler, Searchbroker, Ontobroker • IBROW brokers dynamic (reasoning) knowledge • Opens possibility for a new electronic market place

  7. Motivation • Topdown • make KE technology on reuse more widely accessible at lower cost • Bottom up • several PSM libraries exist, but not accessible nor interoperable • WWW is here to stay

  8. IBROW project • ESPRIT, Open Long Term Research • basic research • industrial potential • unknown risk • Partners: • University of Amsterdam, SWI (nl) • University of Karlsruhe, AIFB (de) • Open University, KMI (uk) • Artificial Intelligence Research Institute (es)

  9. Approach • Standard Product Description Language • Libraries of reusable PSMs and Ontologies • Brokering problem-solving knowledge • Interoperability • User Interface (browser)

  10. Overview • Method description language (UPML) • Brokering problem-solving knowledge • Interoperability of heterogeneous components • Conclusions

  11. What is UPML? • Starting point: CML of CommonKADS • Adds: • component-based software development • component reuse • Machine processable • enables semi-automatic system development • needed for broker

  12. The Backbone of UPML

  13. Ontology • Explicit specification of a conceptualization • can be shared by multiple reasoning components • Provide definition of • signatures • axioms used by other parts of the architecture

  14. Task • Specifies problem to be solved • by knowledge-based system • Problem definition is domain independent • enables reuse of generic problem definitions for different applications. • contrary to most approaches in software engineering

  15. Domain Models • Domain knowledge required by • problem-solving method • task definition • Three elements: characterization of • properties • assumptions • domain knowledge

  16. Problem-Solving Methods • Describe reasoning steps and types of knowledge needed to perform a task. • Two different types of PSMs: • complex problem-solving methods • primitive problem-solving methods

  17. Problem-Solving Methods • Complex problem-solving methods • decompose a task into subtasks • Primitive problem-solving methods • make assumptions about domain knowledge to perform a reasoning step • do not have an internal structure

  18. Problem-solving method

  19. Bridges, Refiners

  20. Bridges • Model relationships between two different parts of an architecture • between domain and task • task and problem-solving method

  21. Refiners • Used to express the stepwise adaptation of one type of element of a specification • a task is refined into a more specific task (design --> parametric design) • a problem-solving method is refined into a more specific PSM (search --> hill climbing)

  22. Design Rules • Architectural constraints ensure well formed specifications • The individual components must fulfill certain properties (assumptions + operational-spec --> competence of PSM) • Their connection via bridges and refiners must fulfill certain properties

  23. Editor for UPML • PROTEGE-II provides an environment for generating knowledge acquisition tools • Use of PROTEGE-II for • defining UPML meta ontology • designing editor for UPML

  24. Meta ontology of UPML

  25. Editor for UPML

  26. Browser for UPML • On2broker for • browsing UPML specifications • query interface for UPML specifications • Query Interface is a JavaTMRemote Method Invocation (RMI) Serverwhich can be consulted by any software agent

  27. The IBROW broker • Acquire customer’s problem description • Configuration • Find candidate problem-solving methods • Check their applicability wrt the KB • Integrate the PSMs into a coherent reasoner • Execution • CORBA • Web standard

  28. Two problems

  29. Characterizing PSMs • UPML provides slots for • competence of PSMs • goal of tasks • pragmatics (non functional) • ease of use • rate of success • successfully used in … • assumptions of PSMs, tasks and domains

  30. Finding PSMs • Libraries have to register at the broker • Global match in case of many PSMs • pragmatics, keywords • For promising PSMs, match competence with task goal (theorem prover) • might imply mapping and renaming

  31. Checking applicability • Check assumptions of the PSM in the KB of the customer • theorem prover • If different ontologies, derive bridges to connect PSM and KB

  32. Task-PSM bridge Locating PSMs

  33. Integrating PSMs • Impose control knowledge on the selected PSMs • chain inputs and outputs • consider UPML’s operational descriptions • exploit existing task structures

  34. Execution • CORBA • the configured problem-solver is a collection of clients • each PSM is a server • the KB is a server • Lisp Server (using Web standards) • client can also query Lisp Server • Broker controls which client sends requests

  35. Overall picture

  36. Small demo

  37. Summary of Achievements • Distributed knowledge-intensive applications • Competence-oriented component libraries with explicit models based on a uniform framework • (Semi-) automated construction of bridges between components and refiners • Interoperability through CORBA and WWW standards • Powerful brokering facilities

  38. Open Issues • Formalization (pragmatics) • CORBA wrapping of (legacy) KBs • Bridges and refiners (theory) • Architectures