Example of a "semantic network"

A semantic net which has something to do with languages. Build by adding the following components.

1. PROLOG isa PROGRAMMING LANGUAGE
   
2. JAVA isa PROGRAMMING LANGUAGE
   
3. SML isa PROGRAMMING LANGUAGE
   
4. ENGLISH isa NATURAL LANGUAGE
   
5. CHINESE isa NATURAL LANGUAGE
   
6. PROGRAMMING LANGUAGE isa LANGUAGE
   
7. NATURAL LANGUAGE isa LANGUAGE
   
8. PROLOG features LOGIC
   
9. JAVA features OBJECTS
   
10. LISP features FUNCTIONS
    
11. ENGLISH features LETTERS
    
12. CHINESE features SYMBOLS
    
13. PROLOG invented by COLMERAUR
    
14. JAVA invented by GOSLING
    
15. LISP invented by MCCARTHY
    
16. COLMERAUR researches NLP
    
17. COLMERAUR citizen of FRANCE
    
18. FRANCE isa COUNTRY
    

Example of "semantic net questions"

>>> WHAT DO YOU KNOW ABOUT PROLOG?
PROLOG isa PROGRAMMING LANGUAGE features LOGIC invented by COLMERAUR
>>> WHAT DO YOU KNOW ABOUT LANGUAGE?
NOTHING TO SAY ABOUT LANGUAGE
>>> IS PROLOG A LANGUAGE?
YES
>>> WHAT DO YOU KNOW ABOUT COLMERAUR?
COLMERAUR citizen of FRANCE researches NLP
 

Note on "semantic networks"    [http://www.duke.edu/~mccann/mwb/15 semnet.htm]

Semantic networks as a representation of knowledge have been in use in artificial intelligence (AI) research in a number of different areas.  

Some of the first uses of the nodes-and-links formulation were in the work of Quillian and Winston, where the networks acted as models of associative memory.  

Quillian's work centers on how natural language is understood and how the meanings of words can be captured in a machine.  

Winston's work concentrates on machine learning and specifically on structural descriptions of an environment. Winston's work describes pedestals and arches formed from more elementary pieces such as wedges and blocks; these make up the famous "blocks world" that has been utilized by many research efforts in semantic networks.  

The other major area in which the use of semantic networks is prevalent, is in models based on linguistics. These stem in part from the work of Chomsky. This latter work is concerned with the explicit representation of grammatical structures of language. It is opposed to other systems that tried to model, in some machine-implementable fashion, the way human memory works.  

Another approach combining aspects of both the previously mentioned areas of study was taken by Schank in his conceptual dependency approach. He attempted to break down the surface features of language into a network of deeper, more primitive concepts of meaning that were universal and language independent.  

Note on "semantic networks"

Concepts have no meaning in isolation. They only exhibit meaning when viewed relative to the other concepts to which they are connected by relational arcs.  

In semantic networks, structure is everything.  

An individual node is merely a syntactic token that happens to possess a convenient English label. From a computer's perspective, this label is an arbitrary alphanumeric symbol.  

But collectively, a node and its neighbors exhibit a relational structure that can be seen as meaningful. Such a constellation (1) supports inferences that allow us to conclude additional facts about a node, and (2) contains semantic regularities that allow us to express these facts in a language such as English.  

Note on "semantic networks"    [http://www.duke.edu/~mccann/mwb/15 semnet.htm]

Such creations and uses of semantic networks have led to any number of epistemological problems. Numerous researchers have attempted to address these problems. Barr and Feigenbaum state that:  

In semantic network representations, there is no formal semantics, no agreed-upon notion of what a given representational structure means, as there is in logic, for instance.