Using an ontology to automatically generate questions for ordinary people requires a structure and concepts com- pliant with human thought. Here we present methods to develop a pragmatic, expert-based and a basic-level ontology and a framework to evaluate these ontologies. Comparing these ontologies shows that expert-based ontologies are most easy to con- struct but lack required cognitive semantic characteristics. Basic-level ontologies have structure and concepts which are better in terms of cognitive semantics but are most expensive to construct.
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In this paper we present an experiment which has been performed to validate a pragmatic-based, expert-based and basic-level ontology. These ontologies were created for use in an application which generates questions for ordinary people with the purpose to determine a crisis situation. All three ontologies have specific characteristics related to their method of creation. This experiment shows that using the basic-level ontology results in the fastest and least ambiguous determination of a crisis situation.
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Small and medium-sized businesses (SMBs) face unique challenges in developing AI-enabled products and services, with traditional innovation processes proving too resource-intensive and poorly adapted to AI's complexities. Following design science research methodology, this paper introduces Innovation Process for AI-enabled Products and Services (IPAPS), a framework specifically designed for SMBs developing AI-enabled solutions. Built on a semi-formal ontology that synthesizes literature on innovation processes, technology development frameworks, and AI-specific challenges, IPAPS guides organizations through five structured phases from use case identification to market launch. The framework integrates established innovation principles with AI-specific requirements while emphasizing iterative development through agile, lean startup, and design thinking approaches. Through polar theoretical sampling, we conducted ex-post analysis of two contrasting cases. Analysis revealed that the successful case naturally aligned with IPAPS principles, while the unsuccessful case showed significant deviations, providing preliminary evidence supporting IPAPS as a potentially valid innovation process for resource-constrained organizations.
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