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Unit 3: Representation of Knowledge

However, these theories are all fundamentally based on the concept of semantic networks. A Notes
semantic network is a method of representing knowledge as a system of connections between
concepts in memory.

Semantic Networks

According to semantic network models, knowledge is organized based on meaning, such that
semantically related concepts are interconnected. Knowledge networks are typically represented
as diagrams of nodes (i.e., concepts) and links (i.e., relations). The nodes and links are given
numerical weights to represent their strengths in memory. The node representing DOCTOR is
strongly related to SCALPEL, whereas NURSE is weakly related to SCALPEL. These link strengths
are represented here in terms of line width. Concepts such as DOCTOR and BREAD are more
memorable because they are more frequently encountered than concepts such as SCALPEL and
CRUST.

Mental excitation, or activation, spreads automatically from one concept to another related
concept. For example, thinking of BREAD spreads activation to related concepts, such as BUTTER
and CRUST. These concepts are primed, and thus more easily recognized or retrieved from
memory. For example, in David Meyer and Roger Schvaneveldt’s 1976 study (a typical semantic
priming study), a series of words (e.g., BUTTER) and non-words (e.g., BOTTOR) are presented,
and participants deter mine whether each item is a word. A word is more quickly recognized if
it follows a semantically related word. For example, BUTTER is more quickly recognized as a
word if BREAD precedes it, rather than NURSE. This result supports the assumption that
semantically related concepts are more strongly connected than unrelated concepts.
A good knowledge representation covers six basic characteristics:
Coverage, which means the KR covers a breadth and depth of information. Without a
wide coverage, the KR cannot determine anything or resolve ambiguities.
Understandable by humans. KR is viewed as a natural language, so the logic should flow
freely. It should support modularity and hierarchies of classes (Polar bears are bears,
which are animals). It should also have simple primitives that combine in complex forms.

Consistency. If John closed the door, it can also be interpreted as the door was closed by
John. By being consistent, the KR can eliminate redundant or conflicting knowledge.

Efficient
Easiness for modifying and updating.
Supports the intelligent activity which uses the knowledge base
To gain a better understanding of why these characteristics represent a good knowledge
representation, think about how an encyclopedia (e.g. Wikipedia) is structured. There are millions
of articles (coverage), and they are sorted into categories, content types, and similar topics
(understandable). It redirects different titles but same content to the same article (consistency).
It is efficient, easy to add new pages or update existing ones, and allows users on their mobile
phones and desktops to view its knowledge base.

3.2.5 Participants

Rules of inference are syntactical transform rules which one can use to infer a conclusion from a
premise to create an argument. A set of rules can be used to infer any valid conclusion if it is
complete, while never inferring an invalid conclusion, if it is sound. A sound and complete set
of rules need not include every rule in the following list, as in logic, a rule of inference, inference

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