Semantic networks Knowledge Representation

Semantic networks Knowledge Representation. Semantic networks are graphs, with nodes representing objects and arcs representing relationships between objects. Various types of relationships may be defined using semantic networks. The two most common types of relationships are • –IS-A (Inheritance relation) • –HAS (Ownership relation) Semantic networks(Cont..) • Let’s consider an example semantic network to demonstrate how knowledge in a semantic network can be used. Semantic networks(Cont..) Network Operation • To infer new information from semantic networks, we can ask questions from nodes. • Ask node vehicle: ‘How do you travel?’ – This node looks at arc and replies: road - Asks node Car (because of IS-A relationship) – Asks node Vehicle (IS-A relationship) – Node Vehicle Replies: road Semantic networks(Cont..) Problems with Semantic Networks • Semantic networks are computationally expensive at run-time as we need to traverse the network to answer some question. In the worst case, we may need to traverse the entire network and then discover that the requested info does not exist. • They try to model human associative memory (store information using associations), but in the human brain the number of neurons and links are in the order of 1015. It is not practical to build such a large semantic network, hence this scheme is not feasible for this type of problems. Semantic networks(Cont..) Problems with Semantic Networks(cont) • Semantic networks are logically inadequate as they do not have any equivalent q