Techniques such as
data mining,
natural language processing (NLP), and
text analytics provide different methods to
find patterns in, or otherwise interpret, this information. Common techniques for structuring text usually involve manual
tagging with metadata or
part-of-speech tagging for further
text mining-based structuring. The
Unstructured Information Management Architecture (UIMA) standard provided a common framework for processing this information to extract meaning and create structured data about the information. Software that creates machine-processable structure can utilize the linguistic, auditory, and visual structure that exist in all forms of human communication. Algorithms can infer this inherent structure from text, for instance, by examining word
morphology, sentence syntax, and other small- and large-scale patterns. Unstructured information can then be enriched and tagged to address ambiguities and relevancy-based techniques then used to facilitate search and discovery. Examples of "unstructured data" may include books, journals, documents,
metadata,
health records,
audio,
video,
analog data, images, files, and unstructured text such as the body of an
e-mail message,
Web page, or
word-processor document. While the main content being conveyed does not have a defined structure, it generally comes packaged in objects (e.g. in files or documents, ...) that themselves have structure and are thus a mix of structured and unstructured data, but collectively this is still referred to as "unstructured data". For example, an
HTML web page is tagged, but HTML mark-up typically serves solely for
rendering. It does not capture the meaning or function of tagged elements in ways that support automated processing of the information content of the page.
XHTML tagging does allow machine processing of elements, although it typically does not capture or convey the semantic meaning of tagged terms. Since unstructured data commonly occurs in
electronic documents, the use of a
content or
document management system which can categorize entire documents is often preferred over data transfer and manipulation from within the documents. Document management thus provides the means to convey structure onto
document collections.
Search engines have become popular tools for indexing and searching through such data, especially text.
Approaches in natural language processing Specific computational workflows have been developed to impose structure upon the unstructured data contained within text documents. These workflows are generally designed to handle sets of thousands or even millions of documents, or far more than manual approaches to annotation may permit. Several of these approaches are based upon the concept of
online analytical processing, or OLAP, and may be supported by data models such as text cubes. Once document metadata is available through a data model, generating summaries of subsets of documents (i.e., cells within a text cube) may be performed with phrase-based approaches.
Approaches in medicine and biomedical research Biomedical research generates one major source of unstructured data as researchers often publish their findings in scholarly journals. Though the language in these documents is challenging to derive structural elements from (e.g., due to the complicated technical vocabulary contained within and the
domain knowledge required to fully contextualize observations), the results of these activities may yield links between technical and medical studies and clues regarding new disease therapies. Recent efforts to enforce structure upon biomedical documents include
self-organizing map approaches for identifying topics among documents, general-purpose
unsupervised algorithms, and an application of the CaseOLAP workflow CaseOLAP defines phrase-category relationships in an accurate (identifies relationships), consistent (highly reproducible), and efficient manner. This platform offers enhanced accessibility and empowers the biomedical community with phrase-mining tools for widespread biomedical research applications. == The use of "unstructured" in data privacy regulations ==