Knowledge Acquisition Task
A Knowledge Acquisition Task is a information extraction task to extract Knowledge from Information Sources.
- AKA: Knowledge Elicitation.
- Context:
- It can be subject to the Knowledge Acquisition Bottleneck.
- It can range from being a Heuristic Knowledge Acquisition Task to being a Data-Driven Knowledge Acquisition Task.
- See: Ontology Design Task, Domain Knowledge, Conceptual Knowledge, Knowledge Acquisition Bottleneck, Knowledge-Based System, Expert System.
References
2015
- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/knowledge_acquisition Retrieved:2015-3-3.
- Knowledge acquisition is the process used to define the rules and ontologies required for a knowledge-based system. The phrase was first used in conjunction with expert systems to describe the initial tasks associated with developing an expert system, namely finding and interviewing domain experts and capturing their knowledge via rules, objects, and frame-based ontologies.
Expert systems were one of the first successful applications of artificial intelligence technology to real world business problems. Researchers at Stanford and other AI laboratories worked with doctors and other highly skilled experts to develop systems that could automate complex tasks such as medical diagnosis. Until this point computers had mostly been used to automate highly data intensive tasks but not for complex reasoning. Technologies such as inference engines allowed developers for the first time to tackle more complex problems. As expert systems scaled up from demonstration prototypes to industrial strength applications it was soon realized that the acquisition of domain expert knowledge was one of if not the most critical task in the knowledge engineering process. This knowledge acquisition process became an intense area of research on its own. One approach to knowledge acquisition investigated was to use natural language parsing and generation to facilitate knowledge acquisition. Natural language parsing could be performed on manuals and other expert documents and an initial first pass at the rules and objects could be developed automatically. Text generation was also extremely useful in generating explanations for system behavior. This greatly facilitated the development and maintenance of expert systems.
- Knowledge acquisition is the process used to define the rules and ontologies required for a knowledge-based system. The phrase was first used in conjunction with expert systems to describe the initial tasks associated with developing an expert system, namely finding and interviewing domain experts and capturing their knowledge via rules, objects, and frame-based ontologies.