Knowledge Representation Approach

Knowledge representation is simply organizing knowledge related to some area of knowledge into some physical form.  A knowledge representation provides an explanation of or description of the nature and structure of something.

There are a number of different approaches that a knowledge representation might take, each approach having a different level of expressivity and reasoning capability.  These different knowledge representation approaches form a spectrum.  The following is a graphic which shows the expressivity and reasoning capability of different knowledge representation approaches:

The following is a brief description of each of the different knowledge representation approaches which will help you understand the relative expressivity and reasoning capability of each approach:

• Name authority: A name authority provides what amounts to a dictionary or list which amounts to a flat inventory of terms with no relations expressed between the terms. This can be thought of as a controlled vocabulary.
• Thesaurus: A thesaurus provides a dictionary similar to a name authority but then also adds information about the relations between the list of terms enabling a user to do things like distinguish broader terms from narrower terms.
• Taxonomy: A taxonomy tends to provide descriptions and a limited amount of structure generally in the form of one hierarchy into which some list of terms is categorized.. A taxonomy is a tree of categories of things with only one type of relation expressed so terms appear in only one location in a taxonomy. A creator of a taxonomy creates concepts, creates coherent definitions for those concepts, and puts concepts into “buckets” or categories.
• Ontology: An ontology tends to provide descriptions and multiple structures and therefore tends to have more than one hierarchy into which terms are categorized.  So, an ontology can be thought of as a set of taxonomies for explicitly differentiate types of relations or associations between terms.  An ontology is less like a tree and more like a graph  (network theory).
• Theory: A theory tends to provide all of the sorts of information provided by a name authority, thesaurus, taxonomy, and an ontology; but also provides information about restrictions, assertions, constraints, an other such information to provide a complete logical conceptualization.

The fundamental purpose of a knowledge representation is effective communication and understanding of the assumptions provided by the representation. The focus should be on the content represented, not the representation approach. How effective is the approach with respect to achieving desired goals and objectives? Testing answers that question.

A theory provides the most expressivity and therefore the most reasoning capability of different knowledge representation approaches. Note that there are many different technical approaches to providing a machine readable knowledge representation.

Note that knowledge representation involves the orchestration of efforts from many different parties who need to coordinate their efforts. Further, knowledge does not exist in only one silo or area of knowledge.  One area of knowledge often interacts with one or more other areas of knowledge.

One can understand that there is overlap between accountants, lawyers, and human resource professionals. If the knowledge representations were not interoperable; then it would cause unnecessary friction in the flow of information:

Knowledge representation involves structuring information which has typically been represented in documents, spreadsheets, and other means that are not understandable to computer based processes and can contain inconsistencies and other errors. 

Knowledge acquisition is the process of acquiring this knowledge.  There tends to be three approaches to acquiring knowledge: (a) humans create the representation which can be very high quality but expensive, (b) machines create the representation which can be significantly cheaper but the quality is a lot lower many times, or (c) human and machine teaming which can make knowledge acquisition more cost effective in many cases.

Additional Information:

• Knowledge
• Area of Knowledge
• Complexity
• Overview