OntoMorph: A Translation System for Symbolic Knowledge

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

Click here to start

Table of contents

OntoMorph: A Translation System for Symbolic Knowledge

Overview

Motivation

Some Opinions

The Translation Problem

Translation Dimensions

Example: Syntax Differences

Example: Model Differences

Traditional Translation Methods

Need: Translation Tool

Solution: OntoMorph

OntoMorph Rewrite Engine

Pattern Language

Pattern Language, cont.

Example Pattern

Basic Operation

Slide 17

Slide 18

Named Rule Sets and Recursion

Rewrite Rule Example

Rewrite Rule Example: Turing Machine

Semantic Rewriting

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.

Slide 29

Slide 30

Slide 31

Fusion Output to EXPECT: Summary

Translation between Distributed Heterogeneous Agents

Rosetta Translation Service

Slide 35

ForMAT to Prodigy Translation via Rosetta

Using Rosetta with CoABS TIE 1: Ontology-Based Transformations

Conclusion

Author: Hans Chalupsky

Homepage: http://www.isi.edu/~hans/

Download presentation [StarOffice 5] [PowerPoint 97]

Download KR-2000 paper [pdf] [postscript]