Knowledge retrieval

Knowledge retrieval (KR) seeks to return information in a structured form, consistent with human cognitive processes as opposed to simple lists of data items. It draws on a range of fields including epistemology (theory of knowledge), cognitive psychology, cognitive neuroscience, logic and inference, machine learning and knowledge discovery, linguistics, and information technology.

In the field of retrieval systems, established approaches include:

Both approaches require a user to read and analyze often long lists of data sets or documents in order to extract meaning.

The goal of knowledge retrieval systems is to reduce the burden of those processes by improved search and representation. This improvement is needed to leverage the increasing data volumes available on the Internet.

Data Retrieval and Information Retrieval are earlier and more basic forms of information access.

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.

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