Quentin,
Thanks. The practitioner's observations you share have good company throughout the history of AI. For example, knowledge engineers in the 80s expressed similar concerns when building expert systems. Similarly, the goal of accelerating the process of and driving down the cost to extract and model (executable) knowledge was one of the motivations that lead Paul Allen to fund research at Vulcan.
Question: what has been the experience at Wolters Kluwer with using natural language based tools to gain leverage on the process of knowledge extraction? (For example, capturing sources that an expert would consult, as well as eliciting knowledge directly from the SME...)
Mills Davis Hi Matthew,
I work as a Content Semantics architect at Wolters Kluwer, where we are developing ontologies to represent documents and their metadata. As part of this work, I have experienced difficulties (or bottlenecks) in the application of ontologies.
Please find attached my answers to your questions: - What is it that takes a lot of time and effort? There are 2 tasks that are rather time-consuming; (i) the extraction of the knowledge from Subject-Matter experts (SMEs), and (ii) the explanation of the model to developers using it.
- What is it that is very expensive? The extraction of the knowledge is expensive as most SMEs have a "ideal" vision of what their knowledge is and tend to differ from the actions / reasoning they actually use. For instance, doctors will answer questions theoretically when their reasoning while consulting patients tend to be different.
- What is it that is held up because of a lack of scarce resources? I am not quite sure what you refer to as "scarce resources"
- Why is it that ontological approaches are not taken when they could/should be? Time constraint on the delivery of the ontological artifacts mean that the model and its implementation are generally not separated.
Kind regards,
Quentin Reul
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