An environment for merging and testing large ontologies

TLDR

Large‑scale ontologies are increasingly vital for search, e‑commerce, configuration, and intelligence, yet their rapid growth and the involvement of diverse, often under‑trained teams complicate development and integration. The authors aim to provide tools that enable broad‑range users to merge ontological terms, diagnose coverage and correctness, and maintain ontologies over time. They introduce Chimaera, a merging and diagnostic ontology environment built for the HPKB project to address these challenges. Initial tests demonstrate Chimaera’s effectiveness in merging tasks.

Abstract

Large-scale ontologies are becoming an essential component of many applications including standard search (such as Yahoo and Lycos), ecommerce (such as Amazon and eBay), configuration (such as Dell and PC-Order), and government intelligence (such as DARPA’s High Performance Knowledge Base (HPKB) program). The ontologies are becoming so large that it is not uncommon for distributed teams of people with broad ranges of training to be in charge of the ontology development, design, and maintenance. Standard ontologies (such as UNSPSC) are emerging as well which need to be integrated into large application ontologies, sometimes by people who do not have much training in knowledge representation. This process has generated needs for tools that support broad ranges of users in (1) merging of ontological terms from varied sources, (2) diagnosis of coverage and correctness of ontologies, and (3) maintaining ontologies over time. In this paper, we present a new merging and diagnostic ontology environment called Chimaera, which was developed to address these issues in the context of HPKB. We also report on some initial tests of its effectiveness in merging tasks.