Knowledge Discovery, Capture and Creation

Knowledge Discovery, Capture and Creation by Linda C. Smith.

This article outlines the range of techniques being used to support knowledge discovery. Here's a summary:

Meta-analysis is a statistical procedure for integrating results of independent studies that are combinable, often to gain greater confidence in the outcome of investigations such as randomized clinical trials in medicine.

Bibliometrics is the application of mathematics and statistical methods to various forms of publications. (citation analysis, Citation counts patterns linkages

Co-citation analysis considers joint citation of earlier works and has been used to discover the intellectual structure of science and scholarship by clustering and mapping.

Visualization is an important aid in knowledge discovery.

Co-word analysis is based on the co-occurrence frequency of pairs of words or phrases in texts. It has been used to discover linkages among subjects in a research field and thus to trace the development of science.

Text mining offers possibilities for creating knowledge out of the massive amounts of unstructured information available on the Internet and corporate intranets (This approach uses techniques from data mining, machine learning, information retrieval, natural language understanding, case-based reasoning, statistics and knowledge management to help people gain new insights from large quantities of text).

Information extraction involves more focused processing of text through lexical preprocessing, parsing and semantic analysis, and discourse interpretation. (The task is to extract information about a pre-specified set of entities, relations or events from natural language texts, such as extracting details of events from news stories).

Data mining or knowledge discovery in databases (KDD) involves manipulation of data from structured databases.

Data warehouses help set the stage for data mining. They involve selection, assembly and structuring of data from disparate sources. This may require data cleaning to check for errors or missing data.

Knowledge acquisition including interviewing, protocol analysis (asking the person to talk aloud while performing a task), questionnaires and surveys, and observation and simulation.

Knowledge capture, finding ways to make tacit knowledge explicit (e.g., documenting best practices) or creating expert directories to foster knowledge sharing through human-human collaboration.

(as matter of fact, Corporate KM is likewise concerned with both points above).