(27th-June-2020)
• You can interpret the prop relation in terms of a graph, where the relation
• prop(Ind , Prop , Val)
• is depicted with Ind and Val as nodes with an arc labeled with Prop between them. Such a graph is called a semantic network. Given such a graphical representation, there is a straightforward mapping into a knowledge base using the prop relation.
Regarding semantic network figure is following.
• It shows a semantic network for the delivery robot showing the sort of knowledge that the robot may have about a particular computer. Some of the knowledge represented in the network is
• prop(comp_2347 , owned_by , fran).
• prop(comp_2347 , managed_by , sam).
• prop(comp_2347 , model , lemon_laptop_10000).
• prop(comp_2347 , brand , lemon_computer).
• prop(comp_2347 , has_logo , lemon_disc).
• prop(comp_2347 , color , green).
• prop(comp_2347 , color , yellow).
• prop(comp_2347 , weight , light).
• prop(fran , has_office , r107).
• prop(r107 , in_building , comp_sci).
• The network also shows how the knowledge is structured. For example, it is easy to see that computer number 2347 is owned by someone (Fran) whose office (r107) is in the comp_sci building. The direct indexing evident in the graph can be used by humans and machines.
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