1. Matching.
In data retrieval we are normally looking for an exact match, that is, we are checking to see whether an item is or is not present in the file.
Ex: Select * from Student where per >= 8.0
In information retrieval more generally we want to find those items which partially match the request and then select from those a few of the best matching ones.
Ex: Student having 8 or > 8 CGPA
2. Inference
In data retrieval is of the simple deductive kind, that is, a ∈ b and b ∈ c then a ∈ c.
In information retrieval it is of inductive inference; relations are only specified with a degree of certainty or uncertainty and hence our confidence in the inference is variable.
3.Model
Data retrieval is deterministic but information retrieval is probabilistic.
Frequently Bayes' Theorem is invoked to carry out inferences in IR, but in DR probabilities do not enter into the processing.
4 .Classification:
In DR most likely monothetic classification is used.
That is, one with classes defined by objects possessing attributes both necessary and sufficient to belong to a class.
In IR, polythetic classification is mostly used.
Each individual in a class will possess only a proportion of all the attributes possessed by all the members of that class..
5.Query Language:
The query language for DR is one with restricted syntax and vocabulary.
In IR we prefer to use natural language although there are some notable exceptions.
6.Query Specification:
In DR the query is generally a complete specification of what is wanted,
In IR it is invariably incomplete.
7.Items wanted :
In IR we are searching for relevant documents as opposed to exactly matching items in DR.
8.Error response:
DR is more sensitive to error in the sense that, an error in matching will not retrieve the wanted item which implies a total failure of the system.
In IR small errors in matching generally do not affect performance of the system significantly
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