Textual case-based reasoning

Textual case-based reasoning (TCBR) is a subtopic of case-based reasoning, in short CBR, a popular area in artificial intelligence. CBR suggests the ways to use past experiences to solve future similar problems, requiring that past experiences be structured in a form similar to attribute-value pairs. This leads to the investigation of textual descriptions for knowledge exploration whose output will be, in turn, used to solve similar problems.{{Cite journal|last1=Weber|first1=R.O.|last2=K.|first2=Ashley|last3=S.|first3=Brüninghaus|date=2005|title=Textual Case-Based Reasoning|journal=Knowledge Engineering Review|volume=20|issue=3 |pages=255–260|doi=10.1017/S0269888906000713 |citeseerx=10.1.1.91.9022 |s2cid=11502038 }}

Subareas

Textual case-base reasoning research has focused on:

  • measuring similarity between textual cases
  • mapping texts into structured case representations
  • adapting textual cases for reuse
  • automatically generating representations.

References

{{Reflist}}