The recent history of expert systems, for example, highlights how constricting the brittleness and knowledge acquisition bottlenecks are. Moreover, standard software methodology (e.g., working from a detailed "spec") has proven of little use in AI, a field which by definition tackles ill-structured problems. How can these bottlenecks be widened? Attractive, elegant answers have included machine learning, automatic programming, and natural language understanding. But decades of work on such systems (Green et al., 1974; Lenat et al., 1983; Lenat & Brown, 1984; Schank & Abelson, 1977) have convinced us that each of these approaches has difficulty "scaling up" for want of a substantial base of real world knowledge.
The main difficulties in knowledge acquisition from domain experts stem from the variety of forms of knowledge, the various representations of knowledge, and the problems in making these explicit and accessible. There is, at present, no systematic overall methodological framework for knowledge acquisition to guide the organization and arrangement of the appropriate application of the many manual and automated techniques and methods used for knowledge acquisition. In considering these problems it is appropriate to draw on studies in cognitive science and associated disciplines to examine the models of the expert and the demands and goals of the task. This paper develops the modeling processes involved from the perspective of the expert trying to communicate his view of a target system and transfer it into computer implementable form. It identifies the distinct processes of elicitation, analysis and implementation, the knowledge representations of the intermediate knowledge bases which can be used to help the expert review and refine his conceptual model, and the computer knowledge bases which may be unrecognizable by the expert as related to his developing models.
The International Conference on Knowledge Capture (K-CAP) is a new forum for multidisciplinary research on capturing knowledge from a variety of sources and creating representations that are useful for reasoning. This article describes the first conference series, held in October 2001, and presents an invitation to the AI community to participate in K-CAP 2003.
Indigenous land use practices have a fundamental role to play in controlling deforestation and reducing carbon dioxide emissions. Satellite imagery suggests that indigenous lands contribute substantially to maintaining carbon stocks and enhancing biodiversity relative to adjoining territory (1). Many of these sustainable land use practices are born, developed, and successfully implemented by the community without major influence from external stakeholders (2). A prerequisite for such community-owned solutions is indigenous knowledge, which is local and context-specific, transmitted orally or through imitation and demonstration, adaptive to changing environments, collectivized through a shared social memory, and situated within numerous interlinked facets of people's lives (3). Such local ecological knowledge is increasingly important given the growing global challenges of ecosystem degradation and climate change (4).
To enhance the quality and consistency of its customer- support organization, Reuters embarked on a global knowledge development and reuse project. The system supports 38 Reuter products worldwide. This article presents a case study of Reuter experience in putting a global knowledge organization in place, building knowledge bases at multiple distributed sites, deploying these knowledge bases in multiple sites around the world, and maintaining and enhancing knowledge bases within a global organizational framework. This project is the first to address issues in multicountry knowledge development and maintenance and multicountry knowledge deployment.