Comparison of Data, Information and Knowledge Retrieval
Data Retrieval and Information Retrieval are earlier and more basic forms of information access.
| Data Retrieval | Information Retrieval | Knowledge Retrieval | |
|---|---|---|---|
| Match | Boolean match | partial match, best match | partial match, best match |
| Inference | deductive inference | inductive inference | deductive inference, inductive inference, associative reasoning, analogical reasoning |
| Model | deterministic model | statistical and probabilistic model | semantic model, inference model |
| Query | artificial language | natural language | knowledge structure, natural language |
| Organization | table, index | table, index | knowledge unit, knowledge structure |
| Representation | number, rule | natural language, markup language | concept graph, predicate logic, production rule, frame, semantic network, ontology |
| Storage | database | document collections | knowledge base |
| Retrieved Results | data set | sections or documents | a set of knowledge unit |
Knowledge retrieval (KR) focuses on the knowledge level. We need to examine how to extract, represent, and use the knowledge in data and information. Knowledge retrieval systems provide knowledge to users in a structured way. Compared to data retrieval and information retrieval, they use different inference models, retrieval methods, result organization, etc. Table 1, extending van Rijsbergen’s comparison of the difference between data retrieval and information retrieval, summarizes the main characteristics of data retrieval, information retrieval, and knowledge retrieval. The core of data retrieval and information retrieval is retrieval subsystems. Data retrieval gets results through Boolean match. Information retrieval uses partial match and best match. Knowledge retrieval is also based on partial match and best match.
From an inference perspective, data retrieval uses deductive inference, and information retrieval uses inductive inference. Considering the limitations from the assumptions of different logics, traditional logic systems (e.g., Horn subset of first order logic) cannot reasoning efficiently. Associative reasoning, analogical reasoning and the idea of unifying reasoning and search may be effective methods of reasoning at the web scale.
From the retrieval perspective, knowledge retrieval systems focus on semantics and better organization of information. Data retrieval and information retrieval organize the data and documents by indexing, while knowledge retrieval organize information by indicating connections between elements in those documents.
Read more about this topic: Knowledge Retrieval
Famous quotes containing the words comparison of, comparison, information and/or knowledge:
“We teach boys to be such men as we are. We do not teach them to aspire to be all they can. We do not give them a training as if we believed in their noble nature. We scarce educate their bodies. We do not train the eye and the hand. We exercise their understandings to the apprehension and comparison of some facts, to a skill in numbers, in words; we aim to make accountants, attorneys, engineers; but not to make able, earnest, great- hearted men.”
—Ralph Waldo Emerson (1803–1882)
“He was a superior man. He did not value his bodily life in comparison with ideal things. He did not recognize unjust human laws, but resisted them as he was bid. For once we are lifted out of the trivialness and dust of politics into the region of truth and manhood.”
—Henry David Thoreau (1817–1862)
“Rejecting all organs of information ... but my senses, I rid myself of the Pyrrhonisms with which an indulgence in speculations hyperphysical and antiphysical so uselessly occupy and disquiet the mind.”
—Thomas Jefferson (1743–1826)
“To read a newspaper for the first time is like coming into a film that has been on for an hour. Newspapers are like serials. To understand them you have to take knowledge to them; the knowledge that serves best is the knowledge provided by the newspaper itself.”
—V.S. (Vidiadhar Surajprasad)