Where are the semantics in the semantic web?

Title	Where are the semantics in the semantic web?
Journal	AI magzine
Year	2003
Author	M uschold
level	introductory
Comment	This paper is hard to translate. Is an articleSemanticIntroductionArticle. The read is rough.

1.Summary

The most widely accepted feature of semantic web is that its content can be understood by machines.

According to this definition, the shopping subject has displayed the semantic network, which can automatically access and use network information to find the cheapest airplane fare or book price. However, where is the meaning?

Most people regard semantic networks as what may happen in the future, rather than reality,So shopping agents shocould not "count ".(How to translate this sentence ?)

To use network content, the machine must know how to deal with the content.

This actually requires the machine to "know" the meaning (meaning) of the content ).

The challenge of developing Semantic networks is to store this knowledge into machines.

This is the most confusing aspect of semantic network research.

The purpose of this paper is to clear some confusion.

We start by describing the meaning of "semantics". Different things can be considered to have different types of semantics.

We will introduce (Implicit semantics,Only in the user's brain) to the standard semantics (Formal Semantics,Can be processed by machines.

We will list the core conditions that must be met to enable machines to use network content, and take into account many issues, such as handwriting, protocol, clarification of semantic conventions, and Open Semantic declarations.

Considering these conditions and problems together with our semantic intervals, we can jointly determine that the shopping subject is a semantic network.Degradation form.

When a shopping subject is working, the semantics of the network content is not taken into account, because the meaning of the network content isProgramProgrammers can predict and writeCode.

We have noticed that this method has many shortcomings, which provide some ideas for the development of semantic networks.

 We believe that this evolution can be performed in the following ways:

 (1)Semantic interval movement: from implicit semantics to standard Semantics

 (2)ReduceHardwiredProvides network content semantics.

 (3)Increase the number of protocols and standards

 (4)Developers have semantic ing and translation capabilities in case of inconsistency.

2. Article Structure

Introduction
Semantic: a consumer-splendored thing
ListSemanticAnd putSemanticSplit4Type
Machine Processible Semantics
Why do web Shopping agents work?
Conclusion

3. Types with semantics

1. terms or expressions, referring to the real world subject matter of web content (e.g., semantic markup );

 Terms or expressions, referring to the real world subject matter of web content (e.g., semantic markup );

2. terms or expressions in an agent communication language (e.g., inform );

3. A language for representing the above information (e.g., the semantics of DAML + oil or RDF ).

4. Semantic Category

 Implicit;

 Explicit and informal;

 Explicit and formal for human processing;

explicit and formal for machine processing.

5. Machine Processible Semantics

Formally represent the semantics and allow the machine to process it to dynamically discover what the content means and how to use it.

A restricted question:

How can a machine (I. e., software agent) learn something about the meaning of a term that it has never before encountered?

Assumptions:

1. All parties agree to use the same representation language;

2. The conceptualizations are logically compatible;

3. There are publicly declared concepts that different agents can use to agree on meaning.

6. Where are the semantics in the semantic web?

1. Often just in the human as unstated assumptions derived from implicit consensus (e.g., "price" on a travel or bookseller web site ).

2. In informal specification documents (e.g., the semantics of UML or RDF Schema ).

3. hardwired in implemented code (e.g., In UML and RDF tools; and in Web shopping agents)

4. In formal specifications to help humans understand/or write code. (e.g., a modal logic specification of the meaning of "inform" in an agent communication language ).

5. Formally encoded for machine processing e.g., (fuel-pump has (superclasses sho: pump ))

6. In the Axiomatic and model-theoretic semantics of representation ages (e.g., the formal semantics of RDF ).