  Knowledge-based machine translation (KBMT) systems have achieved excellent
results in constrained domains, but have not yet scaled up to newspaper text.
The reason is that knowledge resources (lexicons, grammar rules, world models)
must be painstakingly handcrafted from scratch. One of the hypotheses being
tested in the PANGLOSS machine translation project is whether or not these
resources can be semi-automatically acquired on a very large scale. This paper
focuses on the construction of a large ontology (or knowledge base, or world
model) for supporting KBMT. It contains representations for some 70,000
commonly encountered objects, processes, qualities, and relations. The ontology
was constructed by merging various online dictionaries, semantic networks, and
bilingual resources, through semi-automatic methods. Some of these methods
(e.g., conceptual matching of semantic taxonomies) are broadly applicable to
problems of importing/exporting knowledge from one KB to another. Other methods
(e.g., bilingual matching) allow a knowledge engineer to build up an index to a
KB in a second language, such as Spanish or Japanese.
