@article{
 title = {Knowledge Acquisition Using Linguistic-Based Knowledge Analysis},
 type = {article},
 year = {1998},
 pages = {1-20},
 volume = {12},
 id = {2e579628-cdda-3a2b-8c86-1692f628f60e},
 created = {2012-04-01T16:32:49.000Z},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Schmoldt1998},
 private_publication = {false},
 abstract = {A BSTRACT . Most knowledge-basedsystem development efforts includeacquiring knowledge from one ormore sources. Difficulties associatedwith this knowledge acquisition taskare readily acknowledged by most researchers. While a variety of knowledge acquisition methods have beenreported, little has been done to organize those different methods and tosuggest how to apply them within aconceptual framework. The linguistic-based knowledge analysis appreach described here offers a conceptual approach wherein knowledgein a subject area is analyzed as (1)lexical, (2) syntactic, and (3) semantic knowledge. Analysis of these threeseparate components of knowledgecreates a domain “language” thatcompletely describes the knowledgeof a particular subject area in logicaldetail. This “language” serves as aknowledge model that can be readilytranslated into a software implementation. The resulting knowledgebased system is able to “converse” inthe language of its domain about particular types of problems. Many previously reported knowledge acquisition techniques are effective, to varying degrees, for acquiring lexical,syntactic, and semantic knowledge.},
 bibtype = {article},
 author = {Schmoldt, Daniel L},
 journal = {Ai Applications}
}

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