Semantic Networks
Semantic networks are graphical representation of taxonomic knowledge of objects and their properties. The graph consist of two types of nodes.
- Nodes that represents objects of the domain.
- Nodes that represents category of the objects.
The graphic consists of three kinds of arcs that connects nodes in the graph.
- Element arcs (set membership arcs).
- Subset arcs.
- Functions arcs.
Fig: A Semantic Network
Non-monotonic Reasoning in Semantic Networks
While much human, reasoning is monotonic, some important human common-sense reasoning is not. We reach conclusions from certain premises that we would not reach if certain other sentences were included in our premises. For example, if I hire you to build me a bird cage, you conclude that it is appropriate to put a top on it, but when you learn the further fact that my bird is a penguin you no longer draw that conclusion.
Some people think it is possible to try to save monotonicity by saying that what was in your mind was not a general rule about birds flying but a probabilistic rule. So far these people have not worked out any detailed epistemology for this approach, i.e. exactly what probabilistic sentences should be used. Instead AI has moved to directly formalizing non-monotonic logical reasoning. Indeed it seems to me that when probabilistic reasoning (and not just the axiomatic basis of probability theory) has been fully formalized, it will be formally non-monotonic.
Inheritance: Categories serve to organize and simplify the knowledge base' through inheritance.
Example: If it is said that all instances of the category Food and edible, and if it is stressed that Fruit is a subclass of. Food and Apples is a subclass of Fruit, then it is known that every apple is edible. It can be said that the individual apples inherit the property of edibility, in this case from their membership in the Food category.
In case of semantic networks, the non-monotonic. reasoning cancel the inheritance due to the following problems,
- Ambiguity in inheriting same property from different parents.
- From the below Figure if the energy source of office machines is wall outlet by default, but in case of robot the energy source is-battery. This raises contradiction in representation.
Fig: A Semantic Network for Default Reasoning
Frames
A frame is a data structure to represent the taxonomic knowledge. A frame consist of name and set of attribute-value pairs. Each node in semantic network is labeled with corresponding frame name and nodes a the other ends of the arc are its attribute-value pairs. Each attribute-value pair is called as a Slot. Here attributes are slot names and values are slot fillers.
Fig: A Frame
The problem with frames is, it is difficult to express non-taxonomic knowledge, disjunctions and negations in semantic networks.
