Where are the Semantics in the Semantic Web 3. Many axioms and definitions in the Enterprise Ontology [Uschold et al. 1998] were created without the expectation that they would be used for automated inferencing(although that remained a possibility). The primary purpose was to help communicate the intended meaning to people Formal semantics for human processing can go a long way to eliminating ambiguity, but because there is still a human in the loop, there is ample scope for errors 2.1.4 Formal Semantics for Machine Processing Finally, there is the possibility of explicit, formally specified semantics that are intended for machines to directly process using automated inference. The idea is that when new terms are encountered, it is possible to automatically infer something about their meaning and thus how to use them. Inference engines can be used to derive new information for a wide variety of purposes. We will explore this topic in depth in the next section 3 Machine Processible Semantics The defining feature of the Semantic Web is machine usable content. This implies that the machine knows what to do with the Web content it encounters. This does not imply that there is any explicit account of the mantics. Instead, the semantics( whether implicit, informal, or formal)can be hardwired into the Web applications. A more robust approach is to formally represent the semantics and allow the machine to process it to dynamically discover what the content means and how to use it-we call this machine this discussion to the following specific question: How can a machine(i.e, sofhvare agent) leam estrict processible semantics. This may be an impossible goal to achieve in its full generality, so we will something about the meaning of a term that it has never before encountered? One way to look at this is from a procedural perspective. For example, how does a compiler know how to interpret a symbol like"+ in a computer language? Or, how does an agent system know what to do when it encounters the performative inform"? The possibly informal semantics of these symbols are hardwired into a procedure by a human beforehand, and it is intended for machine processing. When the compiler encounters the symbol, it places a call to the appropriate procedure. The meaning of the symbol is: what happens when the procedure is executed. The"agent"determines the meaning of the symbol by calling the appropriate procedure. So, in some sense this may be viewed as machine processible semantics We are instead focusing on a declarative view. From this perspective, we ask how an agent can learn the meaning of a new term from a formal, declarative specification of its semantics. Ideally, we would like to do this without making any assumptions at all. In this case, all symbols might as well be in a never-before seen script from a long-extinct intelligent species on Mars. We have no knowledge of the meaning of the symbols, the rules of syntax for the language, nor do we have any information on the semantics of the language. This general case is the most challenging kind of cryptography. It is extremely difficult for humans, never mind machines. So, we have to start making some assumptions 3.1 Issues and Assumptions 3.1.1 Language Heterogeneity Different ontology languages are often based on different underlying paradigms(e.g, description logic, first-order logic, frame-based representation, taxonomy, semantic net, and thesaurus). Some ontology languages are very expressive and some are not. Some ontology languages have a formally defined semantics and some do not. Some ontology languages have inference support and some do not. If we are to allow all these different languages, then we are faced with the very challenging problem of tras yaiofrom between them. For simplicity then, we will assume that the expressions encountered by our agent are a single language whose syntax and semantics are already known to the agent, e.g, RDF Schema, DAML+OIL Final Draft Submitted to AI MagazineWhere are the Semantics in the Semantic Web Final Draft Submitted to AI Magazine Page 7 3. Many axioms and definitions in the Enterprise Ontology [Uschold et al. 1998] were created without the expectation that they would be used for automated inferencing (although that remained a possibility). The primary purpose was to help communicate the intended meaning to people. Formal semantics for human processing can go a long way to eliminating ambiguity, but because there is still a human in the loop, there is ample scope for errors. 2.1.4 Formal Semantics for Machine Processing Finally, there is the possibility of explicit, formally specified semantics that are intended for machines to directly process using automated inference. The idea is that when new terms are encountered, it is possible to automatically infer something about their meaning and thus how to use them. Inference engines can be used to derive new information for a wide variety of purposes. We will explore this topic in depth in the next section. 3 Machine Processible Semantics The defining feature of the Semantic Web is machine usable content. This implies that the machine knows what to do with the Web content it encounters. This does not imply that there is any explicit account of the semantics. Instead, the semantics (whether implicit, informal, or formal) can be hardwired into the Web applications. A more robust approach is to formally represent the semantics and allow the machine to process it to dynamically discover what the content means and how to use it—we call this machine processible semantics. This may be an impossible goal to achieve in its full generality, so we will restrict this discussion to the following specific question: How can a machine (i.e., software agent) learn something about the meaning of a term that it has never before encountered? One way to look at this is from a procedural perspective. For example, how does a compiler know how to interpret a symbol like “+” in a computer language? Or, how does an agent system know what to do when it encounters the perfomative “inform”? The possibly informal semantics of these symbols are hardwired into a procedure by a human beforehand, and it is intended for machine processing. When the compiler encounters the symbol, it places a call to the appropriate procedure. The meaning of the symbol is: what happens when the procedure is executed. The “agent” determines the meaning of the symbol by calling the appropriate procedure. So, in some sense this may be viewed as machine processible semantics. We are instead focusing on a declarative view. From this perspective, we ask how an agent can learn the meaning of a new term from a formal, declarative specification of its semantics. Ideally, we would like to do this without making any assumptions at all. In this case, all symbols might as well be in a never-before seen script from a long-extinct intelligent species on Mars. We have no knowledge of the meaning of the symbols, the rules of syntax for the language, nor do we have any information on the semantics of the language. This general case is the most challenging kind of cryptography. It is extremely difficult for humans, never mind machines. So, we have to start making some assumptions. 3.1 Issues and Assumptions 3.1.1 Language Heterogeneity Different ontology languages are often based on different underlying paradigms (e.g., description logic, first-order logic, frame-based representation, taxonomy, semantic net, and thesaurus). Some ontology languages are very expressive and some are not. Some ontology languages have a formally defined semantics and some do not. Some ontology languages have inference support and some do not. If we are to allow all these different languages, then we are faced with the very challenging problem of translating between them. For simplicity then, we will assume that the expressions encountered by our agent are from a single language whose syntax and semantics are already known to the agent, e.g., RDF Schema, or DAML+OIL