Where are the Semantics in the Semantic Web the requirements for a standard web ontology language [w3C 2002b] For further discussion of inference on the Semantic Web, see([Horrocks 2002], Jasper Tyler 2001]) 4 Why do Web Shopping Agents Work? We have taken some time to consider what people might mean when they talk about the Semantic Web There appears to be consensus that the key defining feature is machine usable Web content. However, we argue that by this definition there is an important sense in which the Semantic Web already exists. The best examples of this are travel and bookseller shopping agents that automatically access Web pages looking for good deals. We shall not quibble about whether or not this should"count, nor how the definition of Semantic Web might need to be modified accordingly. It is more useful to regard these examples collectively as a degenerate case of the Semantic Web. In this section, we examine why Web shopping agents work, what their limitations are, and what we can expect in the future 4.1 Requirements for Machine Usable Content The following requirements are fundamental for enabling machines to make use of Web content. Requirement 1: The machine needs to know what to do with the content that it encounters For example, it needs to recognize that it has found the content it is looking for and to execute the appropriate procedures when it has been found. Ultimately it is humans that write the programs that enable the machines to do the right thing. So Requirement 2: Humans must know what to do with the content that the program is expected to encounter. This further requires Requirement 3: Humans know the meaning of the expected content, or are able to encode a procedure that can learn that meaning In determining what makes the Web shopping agent examples work, we consider the following questions Question 1: What is hardwired and what isnt? Question 2: How much agreement is there among different Web sites in their use of terminology and n the similarity of the concepts being referred to? Question 3: To what exter clearly specified? Is t, explici and informal or formal? Question 4: Are agreements and/or semantics publicly declared? 4.1.1 Hardwiring The general case of automatically determining the meaning of Web content is somewhere between intractable and impossible. Thus, a human will always be hardwiring some of the semantics into Web applications. The question is what is hardwired and what is not? The shopping agent applications essentially hardwire the meaning of all the terms and procedures. The hardwiring enables the machine to know how to use the content. The hardwiring approach is not robust to changes in Web content The alternative to hardwiring is allowing the machine to process the semantics specifications directly. In the meaning of every term. Instead, we hardwire the semantics of the representation language inferene c our simple fuel pump example, we have an additional degree of flexibility because we need not hardwir procedures To make this work, we made many assumptions. For example, by assuming(1) that only one epresentation language is used, (2)that the conceptualizations are logically compatible, and(3)that ere Final Draft Submitted to AI MagazineWhere are the Semantics in the Semantic Web Final Draft Submitted to AI Magazine Page 10 the requirements for a standard web ontology language [W3C 2002b]. For further discussion of inference on the Semantic Web, see ([Horrocks 2002] ,[Jasper & Tyler 2001]). 4 Why do Web Shopping Agents Work? We have taken some time to consider what people might mean when they talk about the Semantic Web. There appears to be consensus that the key defining feature is machine usable Web content. However, we argue that by this definition there is an important sense in which the Semantic Web already exists. The best examples of this are travel and bookseller shopping agents that automatically access Web pages looking for good deals. We shall not quibble about whether or not this should “count,” nor how the definition of Semantic Web might need to be modified accordingly. It is more useful to regard these examples collectively as a degenerate case of the Semantic Web. In this section, we examine why Web shopping agents work, what their limitations are, and what we can expect in the future. 4.1 Requirements for Machine Usable Content The following requirements are fundamental for enabling machines to make use of Web content. Requirement 1: The machine needs to know what to do with the content that it encounters. For example, it needs to recognize that it has found the content it is looking for and to execute the appropriate procedures when it has been found. Ultimately it is humans that write the programs that enable the machines to do the right thing. So: Requirement 2: Humans must know what to do with the content that the program is expected to encounter. This further requires that: Requirement 3: Humans know the meaning of the expected content, or are able to encode a procedure that can learn that meaning. In determining what makes the Web shopping agent examples work, we consider the following questions: Question 1: What is hardwired and what isn’t? Question 2: How much agreement is there among different Web sites in their use of terminology and in the similarity of the concepts being referred to? Question 3: To what extent are the semantics of the content clearly specified? Is it implicit, explicit and informal, or formal? Question 4: Are agreements and/or semantics publicly declared? 4.1.1 Hardwiring The general case of automatically determining the meaning of Web content is somewhere between intractable and impossible. Thus, a human will always be hardwiring some of the semantics into Web applications. The question is what is hardwired and what is not? The shopping agent applications essentially hardwire the meaning of all the terms and procedures. The hardwiring enables the machine to “know” how to use the content. The hardwiring approach is not robust to changes in Web content. The alternative to hardwiring is allowing the machine to process the semantics specifications directly. In our simple fuel pump example, we have an additional degree of flexibility because we need not hardwire the meaning of every term. Instead, we hardwire the semantics of the representation language inference procedures. To make this work, we made many assumptions. For example, by assuming (1) that only one representation language is used, (2) that the conceptualizations are logically compatible, and (3) that there