How Can Associative Semantic Nets Revolutionize Business and Research?

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In summary, the conversation is discussing the differences between associative semantic nets and neural nets, with one person expressing interest in the topic and the other explaining their development of a tool to create specialized associative semantic nets. The logic of semantic nets is also briefly discussed, with one person mentioning their background in linguistics and the use of different types of relations in semantic nets. The conversation ends with one person stating their reasons for working on this project.
  • #1
fxseminar
Is anybody here interested in Associative Semantic Nets?


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  • #2
neural nets? sure why not.
 
  • #3
No, it is not neural net.

Neural net is ALGORITHM while Semantic Nets is PRESENTATION.

Presentation of semantic relations between "concepts" (=words) in the natural language.
 
  • #4
Okay, I'm interested. Now what? :cool:
 
  • #5
Now - fine.

Are you developing some project conserned?
 
  • #6
I'm intregued by general semantics which I have read a bit on. Not the same thing really I'm sure. So are you working on a project concerning this?
 
  • #7
Yes, I do. I develop a tool producing Associative Semantic Nets by statistical analysis of content.

The example you can see at SemantiCat.com where the Net is used to be a structure of a site.

While that Net is "universal" I'm preparing to produce specialized ones for any given topic.
 
  • #8
I'm not yet working on any project with them; I just looked up a basic definition. I am studying linguistics but haven't gotten to semantics yet. Semantic nets look like they use logic that I'm familiar with.
 
  • #9
Logic of semantic nets is based on logic of ANY net (graph): base unit is - two nodes and rib between them.

Then we must interpret what is nodes and what is ribs.

As semantic for us is the branch of linguistics so nodes here are words.

Particularly in ASSOCIATIVE semantic nets rib serves as fixations of the fact of associative relation between words the rib connects.
 
  • #10
Do the ribs themselves represent words? Or are there are few fixed types of relations? I saw x-->instance-->y or Instance(x, y) used a lot. Just glanced through it though.
 
  • #11
They can - in some kinds of semantic nets.
You need to assign some "text" to the rib - only if there are DIFFERENT kinds of ribs in your net.

If ALL ribs means "instance of" then you can simply title your net "instance semantic net".

The same way - in ASSOCIATIVE semantic net sense of all the ribs is "association" between words.

By the way there is another feature: VALUE of association. So in Associative Semantic Net rib is assigned with number, nor "text"!
 
  • #12
Okay, cool. So are you doing this for school, business, fun, ...?
 
  • #13
I'm doing this for a set of reasons:

1. it is interesting for me
2. I guess it's perspective
3. I want to make business on it.
 

1. What is a Q: Associative Semantic Net?

A Q: Associative Semantic Net is a type of knowledge representation system that is used in artificial intelligence and cognitive psychology. It is a graph-like structure that represents concepts and their relationships to one another.

2. How does a Q: Associative Semantic Net work?

A Q: Associative Semantic Net works by representing concepts as nodes and their relationships as directed arcs. The nodes are connected by these arcs, allowing for the representation of complex knowledge structures.

3. What are the benefits of using a Q: Associative Semantic Net?

Q: Associative Semantic Nets have several benefits, including their ability to represent complex relationships between concepts, their flexibility in representing different types of knowledge, and their ability to infer new knowledge based on existing relationships.

4. How is a Q: Associative Semantic Net different from other knowledge representation systems?

Unlike other knowledge representation systems, Q: Associative Semantic Nets do not rely on a hierarchical structure and instead use a network-like structure to represent knowledge. This allows for more flexibility in representing complex relationships between concepts.

5. What are some real-world applications of Q: Associative Semantic Nets?

Q: Associative Semantic Nets have been used in various fields, including natural language processing, information retrieval, and medical diagnosis. They have also been applied in intelligent tutoring systems and decision support systems.

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