A lthough the IT community widely acknowledges the usefulness of domain ontologies, especially in relation to the Semantic Web, 1,2 we must overcome several barriers before they become practical and useful tools. Thus far, only a few specific research environments have ontologies. (The "What Is an Ontology?" sidebar on page 24 provides a definition and some background.) Many in the computational-linguistics research community use WordNet, 3 but large-scale IT applications based on it require heavy customization. Thus, a critical issue is ontology constructionidentifying, defining, and entering concept definitions. In large, complex application domains, this task can be lengthy, costly, and controversial, because people can have different points of view about the same concept. Two main approaches aid large-scale ontology construction. The first one facilitates manual ontology engineering by providing natural language processing tools, including editors, consistency checkers, mediators to support shared decisions, and ontology import tools. The second approach relies on machine learning and automated language-processing techniques to extract concepts and ontological relations from structured and unstructured data such as databases and text. Few systems exploit both approaches. The first approach predominates in most development toolsets such as Kaon, Protégé, Chimaera, and WebOnto, but some systems also implement machine learning techniques. 1 Our OntoLearn system is an infrastructure for automated ontology learning from domain text. It is the only system, as far as we know, that uses natural language processing and machine learning techniques, and is part of a more general ontology engineering architecture. 4,5 Here, we describe the system and an experiment in which we used a machine-learned tourism ontology to automatically translate multiword terms from English to Italian. The method can apply to other domains without manual adaptation. Figure 1 shows the elements of the architecture. Using the Ariosto language processor, 6 OntoLearn extracts terminology from a corpus of domain text, such as specialized Web sites and warehouses or documents exchanged among members of a virtual community. It then filters the terminology using natural language processing and statistical techniques that perform comparative analysis across different domains, or contrastive corpora. Next, we use the WordNet and SemCor 3 lexical knowledge bases to semantically interpret the terms. We then relate concepts according to taxonomic (kind-of) and other semantic relations, generating a domain concept forest. We use WordNet and our rule-based inductive-learning method to extract such relations. Finally, OntoLearn integrates the domain concept forest with WordNet to create a pruned and specialized view of the domain ontology. We then use ontology editing, validation, and management tools 4,5
Navigli et al. REF proposed an ontology learning method for acquiring terminology translations from English to Italian.
Ontology Learning and its Application to Automated Terminology Translation
IEEE Intelligent Systems
Computer Science