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.
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