semantic network

This is an updated version of an article in the Encyclopedia of Artificial Intelligence , Wiley, 1987, second edition, 1992. With some minor changes, all the text from 1992 is included. More recent material and references have been added. For a printable version, see A semantic network or net is a graph structure for representing knowledge in patterns of interconnected nodes and arcs. Computer implementations of semantic networks were first developed for artificial intelligence and machine translation, but earlier versions have long been used in philosophy, psychology, and linguistics. The Giant Global Graph of the Semantic Web is a large semantic network (Berners-Lee et al. 2001; Hendler & van Harmelen 2008). What is common to all semantic networks is a declarative graphic representation that can be used to represent knowledge and support automated systems for reasoning about the knowledge. Some versions are highly informal, but others are formally defined systems of logic. Following are six of the most common kinds of semantic networks: 2. Definitional networks emphasize the subtype or is-a relation between a concept type and a newly defined subtype. The resulting network, also called a generalization or subsumption hierarchy, supports the rule of inheritance for copying properties defined for a supertype to all of its subtypes. Since definitions are true by definition, the information in these networks is often assumed to be necessarily true. 3. Assertional networks are designed to assert propositions. Unlike definitional networks, the information in an assertional network is assumed to be contingently true, unless it is explicitly marked with a modal operator. Some assertional networks have been proposed as models of the conceptual structures underlying natural language semantics. 4. Implicational networks use implication as the primary relation for connecting nodes. They may be used to represent patterns of beliefs, causality, or inferences. 6. Learning networks build or extend their representations by acquiring knowledge from examples. The new knowledge may change the old network by adding and deleting nodes and arcs or by modifying numerical values, called weights , associated with the nodes and arcs. Some networks were explicitly designed to implement hypotheses about human cognitive mechanisms, while others have been designed primarily for computer efficiency. Sometimes, computational issues may lead to the same conclusions as psychological evidence. The distinction between definitional and assertional networks, for example, has a close parallel to Tulving’s (1972) distinction between semantic memory and episodic memory . Network notations and linear notations are capable of expressing equivalent information. But certain kinds of information are easier to express or process in one form or the other. Since the boundary lines are vague, it is impossible to state necessary and sufficient conditions that include all semantic networks while excluding other systems that are not usually called semantic networks. Section 7 of this article discusses the syntactic mechanisms used to express information in network notations and compares them to the corresponding mechanisms used in linear notations. Despite its age, the Tree of Porphyry represents the common core of all modern hierarchies that are used for defining concept types. In Figure 1, the genus Substance with the differentia material is Body and with the differentia immaterial is Spirit. The modern rule of inheritance is a special case of the Aristotelian syllogisms, which specify the conditions for inheriting properties from supertypes to subtypes: LivingThing inherits material Substance from Body and adds the differentia animate; Human inherits sensitive animate material Substance and adds the differentia rational. Aristotle, Porphyry, and the medieval logicians also distinguished the categories or universals from the individual instances or particulars , which