Title
|
Where are the Semantics in the Semantic Web? |
Journal
|
AI Magzine |
Year
|
2003 |
Author
|
M Uschold |
Level
|
Introductory |
Comment
|
这篇论文比较难翻译. 是一篇semantic的介绍性文章。读的比较粗略。 |
语义网最广为接受的特点是它的内容能够被机器理解。
根据这个定义,购物主体已经展示了语义网,它能够自动访问和使用网络资料来找到最便宜的飞机票价或者是书价。但是,“语义”体现在哪里呢?
大多数人把语义网视为未来可能发生的事情,而不是现实,so shopping agents should not “count”.(这句怎么翻译?)
为了使用网络内容,机器必须知道遇到这些内容后该如何应对。
这实际上是要求机器“知道”这些内容的含义(也就是语义)。
开发语义网的挑战就是如何把这些知识存放到机器中。
这个正是语义网研究最混乱的地方。
这篇论文的目的是要清除部分混乱。
我们从描述“语义”的各种含义开始,不同的事物可以被认为具有不同种类的语义。
我们会介绍一个包括从隐含语义(implicit semantics, 仅存在于使用者的大脑里)到规范语义(formal semantics, 能够被机器处理)的语义区间。
我们将列举为了让机器能够使用网络内容而必须满足的核心条件,并且考虑了许多问题,如手写,协定,语义规约的澄清和语义的公开声明。
把这些条件和问题和我们的语义区间一起考虑,可以共同认定购物主体是一种语义网的退化形式。
购物主体工作时完全没有考虑网络内容的语义,因为这些网络内容的含义都是程序员在编程时可以预计并且写到代码里的。
我们注意到这种方式具有很多不足,这些不足为语义网如何发展提供了一些想法。
我们认为这个演化可以按照以下方式进行:
(1) 语义区间移动:从隐含语义到规范语义
(2) 减少以hardwired方式提供网络内容的语义
(3) 增加协议和标准的数量
(4) 开发遇到不统一时具有语义映射和翻译的能力。
Introduction
Semantic: A many-splendored thing
列举了semantic的多种含义,并且把semantic分成4种
Machine processible semantics
Why do Web Shopping Agents work?
Conclusion
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).
Implicit;
Explicit and informal;
Explicit and formal for human processing;
Explicit and formal for machine processing.
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.
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 and/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 languages (e.g., the formal semantics of RDF).