Building a Large Ontology for Machine Translation

BUILDING A LARGE ONTOLOGY FOR MACHINE TRANSLATION Kevin Knight USC/Information Sciences Institute 4676 Admiralty Way Marina del Rey, CA 90292 ABSTRACT This paper describes efforts underway to construct a large- scale ontology to support semantic processing in the PAN- GLOSS knowledge-base machine translation system. Be- cause we axe aiming at broad sem~tntic coverage, we are focus- ing on automatic and semi-automatic methods of knowledge acquisition. Here we report on algorithms for merging com- plementary online resources, in particular the LDOCE and WordNet dictionaries. We discuss empirical results, and how these results have been incorporated into the PANGLOSS ontology. 1. Introduction The PANGLOSS project is a three-site collaborative effort to build a large-scale knowledge-based machine translation system. Key components of PANGLOSS include New Mexico State University's ULTRA parser [Farwell and Wilks, 1991], Carnegie Mellon's interlingua representation format [Nirenburg and Defrise, 1991], and USC/ISI's PENMAN English generation system [Pen- man, 1989]. Another key component currently under construction at ISI is the PANGLOSS ontology, a large- scale conceptual network intended to support seman- tic processing in other PANGLOSS modules. This net- work will contain 50,000 nodes representing commonly encountered objects, entities, qualities, and relations. The upper (more abstract) region of the ontology is called the Ontology Base (OB) and contains approxi- mately 400 items that represent generalizations essential for the various PANGLOSS modules' linguistic process- ing during translation. The middle region of the ontol- ogy, approximately 50,000 items, provides a framework for a generic world model, containing items representing many English word senses. The lower (more specific) regions O f the ontology provide anchor points for differ- ent application domains. Both the middle and domain model regions of the ontology house the open-class terms of the MT interlingua. They also contain specific infor- mation used to screen unlikely semantic and anaphoric interpretations. The Ontology Base is a synthesis of USC/ISI's PEN- MAN Upper Model [Bateman, 1990] and CMU's ON- TOS concept hierarchy [Carlson and Nirenburg, 1990]. Both of these high-level ontologies were built by hand, and they were merged manually. Theoretical motiva- tions behind the OB and its current status are described in [Hovy and Knight, 1993]. The problem we focus on in this paper is the construc- tion of the large middle region of the ontology. Because large-scale knowledge resources are difficult to build by hand, we are pursuing primarily automatic methods ap- plied in several stages. During the first stage we created several tens of thousands of nodes, organized them into sub/superclass taxonomies, and subordinated those tax- onomies to the 400-node Ontology Base. This work we describe below. Later stages will address the insertion of additional semantic information such as restrictions on actors in events, domain/range constraints on relations, and so forth. For the major node creation and taxonomization stage, we have primarily used two on line sources of infor- mation: (1) the Longman Dictionary of Contempo- rary English (LDOCE)[Group, 1978], and (2) the lexical database WordNet [Miller, 1990]. 2. Merging LDOCE and WordNet LDOCE is a learner's dictionary of English …

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