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This paper presents an ontology management system and ontology processing techniques used to support a distributed event-triggered knowledge network (ETKnet), which has been developed for deployment in a national network for rapid detection and reporting of crop disease and pest outbreaks. The ontology management system, called Lyra, is improved to address issues of terminology mapping, rule discovery, and large ABox inference. A domain ontology that covers the concepts related to events, rules, roles and collaborating organizations for this application in ETKnet was developed. Terms used by different organizations can be located in the ontology by terminology searching. Services that implement knowledge rules and rule structures can be discovered through semantic matching using the concepts defined in the ontology. A tableau algorithm was extended to lazy-load only the needed instances and their relationships into main memory. With this extension, Lyra is capable of processing a large ontology database stored in secondary storage even when the ABox cannot be entirely loaded into memory.

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