A common problem during the life cycle of knowledge-based systems is that symbolically represented knowledge needs to be translated into some different form. Translation needs occur along a variety of dimensions, such as knowledge representation (KR) language syntax, KR language expressivity, modeling conventions, model coverage and granularity, representation paradigms, inference system bias, etc., and any combination thereof. Traditionally, such translations are either performed (1) manually via text or knowledge base editors which is slow, tedious, error-prone and not easily repeatable, or (2) via special-purpose translation software which is difficult to write and hard to maintain.
As a solution to the translation problem, we present the OntoMorph system. OntoMorph provides a powerful rule language to represent complex syntactic transformations and a rule interpreter to apply them to arbitrary KR language expressions. OntoMorph is fully integrated with the PowerLoom KR system to allow transformations based on any mixture of syntactic and semantic criteria. We describe OntoMorph's successful application as an input translator for a critiquing system and as the core of a translation service for agent communication. We further show how knowledge base merging can be cast as a translation problem and motivate how OntoMorph can be applied to knowledge base merging tasks.
Keywords: knowledge bases, knowledge base translation, knowledge base merging
Table of contents
OntoMorph: A Translation System for Symbolic Knowledge
The Translation Problem
Example: Syntax Differences
Example: Model Differences
Traditional Translation Methods
Need: Translation Tool
OntoMorph Rewrite Engine
Pattern Language, cont.
Named Rule Sets and Recursion
Rewrite Rule Example
Rewrite Rule Example: Turing Machine
Two-Pass Translation Scheme
Rewriting Non-Lisp-Style Expressions
OntoMorph Application: Input Translation for COA Critiquer
Input Translation for COA Critiquer
Fusion Output to EXPECT: Translation Issues
Fusion Output to EXPECT: Translation Issues cont.
Fusion Output to EXPECT: Summary
Translation between Distributed Heterogeneous Agents
Rosetta Translation Service
ForMAT to Prodigy Translation via Rosetta
Using Rosetta with CoABS TIE 1: Ontology-Based Transformations
Author: Hans Chalupsky