Where are the Semantics in the Semantic Web are consistent with the axioms. The goal is to create a set of axioms such that the actual models only include the intended model(s) We believe that the idea of real world semantics, as described above captures the essence of the main use of the term"semantics" in a Semantic Web context. However, it is only loosely defined. The ideas of axiomatic and model-theoretic semantics are being used to make the idea of real world semantics for the Semantic Web more concrete From this discussion, it is clear that several things have semantics 1. 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 2.1 A semantic continuum We ask three questions about how semantics may be specified 1. Are the semantics explicit or implicit? 2. Are the semantics expressed informally or formally? 3. Are the semantics intended for human processing, or machine processing? These give rise to four kinds of semantics 2. Explicit and informal, 3. Explicit and formal for human processing 4. Explicit and formal for machine processing We define these to be four somewhat arbitrary points along a semantic continuum(see Figure 1).At one extreme, there are no semantics at all, except what is in the minds of the people who use the terms. At the other extreme, we have formal and explicit semantics that are fully automated. The further we move along the continuum, the less ambiguity there is and the more likely we are to have robust, correctly functioning and easy to maintain Web applications. We consider these four points on our semantic continuum, in turn Note that there are likely to be many cases that are not clear cut and thus arguably may fall somewhere between 2.1.1 Implicit Semantics In the simplest case, the semantics are implicit only. Meaning is conveyed based on a shared understanding derived from human consensus. A common example of this case is the typical use of XML tags, such as tags mean[Cover 98]. However, if there is an implicit shared consensus about what the tags mean, then ese price, address, or delivery date. Nowhere in an XML document, or DTD or Schema, does it say what these people can hardwire this implicit semantics into web application programs, using screen-scrapers and wrappers. This is how one implements shopping agents that search Web sites for the best deals. From th perspective of mature ial applications that automatically use Web content as conceived by Semantic Web visionaries, this is at or near the current state of the art. The disadvantage of implicit semantics is that they are rife with ambiguity. People often do disagree about the meaning of a term. For example, prices come in different currencies and they may or may not include various taxes or shipping costs. The removal of ambiguity is the major motivation for the use of specialized language used in legal contracts. The costs of identifying and removing ambiguity are very high 2.1.2 Informal Semantics At a further point along the continuum, the semantics are explicit and are expressed in an informal manner, e.g., a glossary or a text specification document. Given the complexities of natural language, machines have an extremely limited ability to make direct use of informally expressed semantics. This is mainly for humans. There are many examples of informal semantics, usually found in text specification documents The meaning of tags in hTml such as <h2> which means second level header Final Draft Submitted to AI MagazineWhere are the Semantics in the Semantic Web Final Draft Submitted to AI Magazine Page 5 are consistent with the axioms. The goal is to create a set of axioms such that the actual models only include the intended model(s). We believe that the idea of real world semantics, as described above captures the essence of the main use of the term “semantics” in a Semantic Web context. However, it is only loosely defined. The ideas of axiomatic and model-theoretic semantics are being used to make the idea of real world semantics for the Semantic Web more concrete. From this discussion, it is clear that several things have semantics: 1. 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). 2.1 A semantic continuum We ask three questions about how semantics may be specified: 1. Are the semantics explicit or implicit? 2. Are the semantics expressed informally or formally? 3. Are the semantics intended for human processing, or machine processing? These give rise to four kinds of semantics: 1. Implicit; 2. Explicit and informal; 3. Explicit and formal for human processing; 4. Explicit and formal for machine processing. We define these to be four somewhat arbitrary points along a semantic continuum (see Figure 1). At one extreme, there are no semantics at all, except what is in the minds of the people who use the terms. At the other extreme, we have formal and explicit semantics that are fully automated. The further we move along the continuum, the less ambiguity there is and the more likely we are to have robust, correctly functioning and easy to maintain Web applications. We consider these four points on our semantic continuum, in turn. Note that there are likely to be many cases that are not clear cut and thus arguably may fall somewhere in between. 2.1.1 Implicit Semantics In the simplest case, the semantics are implicit only. Meaning is conveyed based on a shared understanding derived from human consensus. A common example of this case is the typical use of XML tags, such as price, address, or delivery date. Nowhere in an XML document, or DTD or Schema, does it say what these tags mean [Cover 98]. However, if there is an implicit shared consensus about what the tags mean, then people can hardwire this implicit semantics into web application programs, using screen-scrapers and wrappers. This is how one implements shopping agents that search Web sites for the best deals. From the perspective of mature commercial applications that automatically use Web content as conceived by Semantic Web visionaries, this is at or near the current state of the art. The disadvantage of implicit semantics is that they are rife with ambiguity. People often do disagree about the meaning of a term. For example, prices come in different currencies and they may or may not include various taxes or shipping costs. The removal of ambiguity is the major motivation for the use of specialized language used in legal contracts. The costs of identifying and removing ambiguity are very high. 2.1.2 Informal Semantics At a further point along the continuum, the semantics are explicit and are expressed in an informal manner, e.g., a glossary or a text specification document. Given the complexities of natural language, machines have an extremely limited ability to make direct use of informally expressed semantics. This is mainly for humans. There are many examples of informal semantics, usually found in text specification documents. • The meaning of tags in HTML such as <h2>, which means second level header;