Information extraction is a process that uses pre-defined criteria to extract relevant data from larger bodies of data. It requires programming to scan machine-readable sources of information and uses natural language processing to evaluate context. It is difficult to manage on a global scale but tools are continually being perfected to handle larger volumes of unstructured data.
Sometimes known as information retrieval, information extraction (IE) is a process used with computer systems to allow relevant data to be extracted from larger bodies of data, using a set of pre-defined criteria. The idea behind information mining is to allow you to easily identify and digest the data relevant to a particular activity, without the need to manually go through large amounts of information to find the exact data required. The process is similar to the ideas of concept mining or web scraping, as all of these approaches seek to glean actionable insights from a larger pool of available data.
The general approach to information extraction requires the use of programming capable of scanning sources of information considered machine-readable. This can include paper documents that have been scanned into some sort of electronic file, prepared documents such as spreadsheets or word processing documents, or even data contained in human-readable fields in a database. Typically, parameters are set that allow a software program to access these data sources and quickly examine them using specific criteria to prioritize and extract certain types of information from the available pool. This process is typically different from a simple search process, in that the method requires you not to match specific words or phrases per se, but instead uses a process called natural language processing, which helps not only evaluate actual words but also the context and the meaning implied by that context.
The complexities of information extraction make using this approach somewhat difficult to manage on a global scale, although there are IE tools that work very well with only a limited amount of data, such as data sources associated with electronic files hosted on a company’s server, or even a source pool involving a small number of news feeds. With this approach it is possible to identify some type of event, possibly even limit returns to include a certain number of participants in the event, and arrange the data by date.
As with many forms of technology, the tools used to engage in information mining are continually being perfected. Since the turn of the 21st century, the ability to set parameters and use ever-larger bodies of electronic data as part of the search for relevant information has increased significantly. This includes the ability to handle large volumes of unstructured data and use those parameters to bring some order or structure to that data, making it even more useful for future research.
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