About: Knowledge acquisition

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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.

Attributes	Values
rdf:type	Election
rdfs:label	Knowledge acquisition (en)
rdfs:comment	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. (en)
dcterms:subject	Artificial intelligence Knowledge Expert systems
Wikipage page ID	43249770 (xsd:integer)
Wikipage revision ID	1122274358 (xsd:integer)
Link from a Wikipage to another Wikipage	Natural language generation Inference engine Artificial intelligence Object-oriented programming Knowledge Stanford University Web Ontology Language Frame language Knowledge collection from volunteer contributors Knowledge engineering Knowledge domain Diagnosis Rule-based system Artificial intelligence Rosalind (education) Expert systems Expert systems Knowledge Graph Ontologies (computer science) Natural language parsing Knowledge-based system
sameAs	Knowledge acquisition Knowledge acquisition Knowledge acquisition Knowledge acquisition
dbp:wikiPageUsesTemplate	dbt:Reflist dbt:Short_description
has abstract	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 of the earlier works on the topic used Batesonian theories of learning to guide the process. 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. A more recent approach to knowledge acquisition is a re-use based approach. Knowledge can be developed in ontologies that conform to standards such as the Web Ontology Language (OWL). In this way knowledge can be standardized and shared across a broad community of knowledge workers. One example domain where this approach has been successful is bioinformatics. (en)
gold:hypernym	Process
prov:wasDerivedFrom	wikipedia-en:Knowledge_acquisition?oldid=1122274358&ns=0
page length (characters) of wiki page	5055 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Knowledge_acquisition
is Link from a Wikipage to another Wikipage of	Mexican International Conference on Artificial Intelligence Pathfinder network Ripple-down rules Dynamic decision-making Index of philosophy articles (I–Q) Inductive programming The Age of Spiritual Machines Cognitive robotics Glossary of artificial intelligence Multiple-classification ripple-down rules Comparison of different machine translation approaches Computational psychometrics Yuval Shahar Barbara Hayes-Roth Acquisition of knowledge Fallibilism Brian R. Gaines Folksonomy Knowledge-based recommender system Knowledge collection from volunteer contributors Knowledge engineering Knowledge value chain BabelNet Jeff M. Allen Surviving Death Symbolic artificial intelligence The Use of Knowledge in Society Reason maintenance Automatic acquisition of sense-tagged corpora Knowledge Engineering and Machine Learning Group Knowledge acquisition (software) Organizational memory Social cognitive theory View model Word-sense disambiguation Network governance Expert system IDEF3 Mycin Naturalized epistemology Knowledge Acquisition Seductive details Outline of knowledge
is Wikipage redirect of	Acquisition of knowledge Knowledge acquisition (software) Knowledge Acquisition
is foaf:primaryTopic of	wikipedia-en:Knowledge_acquisition

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