The Digital World

Computer Processing vs. Human/Intellectual Processing

Search engines

Expressions can be searched using tools called "search engines". Those return the locations where a given word or expression can be found. But they are unable to return locations about the same or a similar subject, if terms to describe them is even slightly different from the expression which is used for searching. The results are usually OK, in the sense that the results returned are somewhat or sometimes quite relevant to the subject searched. But there is no guarantee that other important sources, although available on the same medium, for example the Web, have not been missed by the search engine.

Chunking text into data and computability.

Technologies used to derive meaning (clustering, parsing, etc.)

Use of computers has forced a representation in a regular structure that enables computing the data (querying): grouping, counting, retrieving by criterion, filtering, etc.

Text has merged with data (that was the purpose of XML). There is no difference between data and metadata. Everything inside (was the slogan of XML).

The issue of knowing if knowledge management should be performed automatic ally or by hand (intellectually) is irrelevant here. A computer can be used to identify patterns that may be meaningful and can serve as a way to better retrieve information as computed. But regardless whether information is brought in "automatically" or not, it still has to be meaningful in order to be exploitable.

Use of computers has forced a representation in a regular structure that enables computing the data (querying): grouping, counting, retrieving by criterion, filtering, etc.

Text has merged with data (that was the purpose of XML). There is no difference between data and metadata. Everything inside (was the slogan of XML).

The issue of knowing if knowledge management should be performed automatic ally or by hand (intellectually) is irrelevant here. A computer can be used to identify patterns that may be meaningful and can serve as a way to better retrieve information as computed. But regardless whether information is brought in "automatically" or not, it still has to be meaningful in order to be exploitable.

Clustering Techniques

Clustering technique is defined as a "technique to achieve high data density". And "data density is defined as the proportion of objects within a given storage block that are accessed by a client during some scope of activation."¹

Another definition of clustering is putting things together.

Text Mining

Under the hood, the frontier of text mining relies on well-established algorithms and retrieval strategies from computer-science disciplines such as information extraction, text categorization, and natural language processing.²

Information Extraction/Information Retrieval

Performance of a search engine can be enhanced by use of an ontology.³. But there are pitfalls that necessarily accompany the transmission of knowledge between different environments. It is not enough to access tons of information. It's important to be able to find what is relevant. How do we know that we are not missing information that are extremely crucial to us? How much time are we willing to spend in order to find what we are looking for? Can we trust the information we are getting? The same applies when we are creating information that is intended to be made available for others to exploit. How do we know the information presented fits their needs?

Agents

Text Categorization

Domain ontologies enable to characterize topics while performing automatic processes of text categorization. Once a phrase it found, it is matched against a term within the ontology which enables characterization⁴.

Natural Language Processing

Bibliomics

http://www.bio-itworld.com/archive/101003/quarries.html

Artificial Intelligence

Machine Learning

Hypertext, hypermedia and the Web

Predicate Logic