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