Where are the semantics in the Semantic Web defined in terms of the term pump that in turn is defined in an external Shared Hydraulics Ontology. The agent can learn that fuel-pump is a subclass of pump, which in turn is a subclass of mechanical-device. The hared Hydraulics Onto SHO superclasses(mechanical-deyice)) ( text-d( A device for…) (e (physteai-parts Riston, valve cylinder) reviw i to device-purpose (Numping-A Fluid) 如 the Operatio i<concept id=fuel-pump FUEL PUMP</concept> What does it mean? Figure 2: Formal Semantics for Machine Processing -An agent is searching for information about mechanical devices, as defined in a public ontology (SHO). A document contains the term "FUEL PUMP,"which the agent has never encountered. Semantic markup reveals that it refers to the concept fuel-pump, which is a kind of pump, "which is in turn defined in SHO as a kind of mechanical device. The agent infers that the document is agent now knows that fuel-pump is not a typewriter or a space ship, because they are not kinds of pumps The agent has no knowledge of what kind of pump it is, only that it is some kind of pump. However, this is sufficient to allow the agent to return this document as being relevant to mechanical devices, even though it has never before heard of the term fuel-pump. It is possible to do this with todays technology using research tools that have been developed [Decker et al. 99, Jasper Uschold 2001. There are also attempts to commercialize this technology, e.g., [Ontoprise 2001. Scale remains a huge barrier to commercia This example illustrates the importance of semantic markup and the sharing of ontologies. It also demonstrates the importance of formal ontologies and automated inference. Inference engines can be used to derive new information for a wide variety of purposes; in particular, a formally specified ontology allows agents to use theorem proving and consistency checking techniques to determine whether or not they have agreement on the semantics of their terminology he ability of the agent to infer something about the meaning of fuel-pump depends on the existence of a formal semantics for ontology language such as DAML+OIL. The language semantics also allow the agent to infer the meaning of complex expressions built up using language primitives. The semantics of the language are not machine processible; they are written for humans only. People use them to write inference engines or other software to correctly interpret and manipulate expressions in the language Note that todays spectacularly impressive search engines by and large do not use formal semantics approaches. Overall it remains an unproven conjecture that such approaches will enhance search capabilities, or have significant impact anywhere else on the Web. For example, there appear to be sufficient business drivers to motivate venture capitalists to heavily invest in Semantic Web companies Fortunately, the w3C is moving forward on this issue by identifying a wide variety of use cases to drive Final Draft Submitted to AI MagazineWhere are the Semantics in the Semantic Web Final Draft Submitted to AI Magazine Page 9 defined in terms of the term pump that in turn is defined in an external Shared Hydraulics Ontology. The agent can learn that fuel-pump is a subclass of pump, which in turn is a subclass of mechanical-device. The has (superclasses SHO: pump)) ( Semantic Markup has (superclasses SHO: pump)) ( has (superclasses SHO: pump)) ( fuel-pump The purpose of this review is to remind operators of the existence of the Operations Manual Bulletin 80-1, which provides information regarding flight operations with low fuel quantities, and to provide supplementary information regarding main tank boost pump low pressure indications. 797 FUEL PUMP LOW PRESSURE INDICATIONS When operating 797 airplanes with low fuel quantities for short Shared Hydraulics Ontology (SHO) (pump has (superclasses (mechanical-device)) (text-def (“A device for …”))) (every pump has (physical-parts (piston, valve, cylinder)) (device-purpose (Pumping-A-Fluid))) What does it mean? Hey, I know this ontology Hey, I know <concept id=fuel-pump>FUEL PUMP</concept> has (superclasses SHO: pump)) ( Semantic Markup has (superclasses SHO: pump)) ( has (superclasses SHO: pump)) ( fuel-pump The purpose of this review is to remind operators of the existence of the Operations Manual Bulletin 80-1, which provides information regarding flight operations with low fuel quantities, and to provide supplementary information regarding main tank boost pump low pressure indications. 797 FUEL PUMP LOW PRESSURE INDICATIONS When operating 797 airplanes with low fuel quantities for short Shared Hydraulics Ontology (SHO) (pump has (superclasses (mechanical-device)) (text-def (“A device for …”))) (every pump has (physical-parts (piston, valve, cylinder)) (device-purpose (Pumping-A-Fluid))) What does it mean? Hey, I know this ontology Hey, I know <concept id=fuel-pump>FUEL PUMP</concept> Figure 2: Formal Semantics for Machine Processing —An agent is searching for information about mechanical devices, as defined in a public ontology (SHO). A document contains the term “FUEL PUMP,” which the agent has never encountered. Semantic markup reveals that it refers to the concept fuel-pump, which is a kind of “pump,” which is in turn defined in SHO as a kind of mechanical device. The agent infers that the document is relevant. agent now knows that fuel-pump is not a typewriter or a space ship, because they are not kinds of pumps. The agent has no knowledge of what kind of pump it is, only that it is some kind of pump. However, this is sufficient to allow the agent to return this document as being relevant to mechanical devices, even though it has never before heard of the term fuel-pump. It is possible to do this with today’s technology using research tools that have been developed [Decker et al. 99; Jasper & Uschold 2001]. There are also attempts to commercialize this technology, e.g., [Ontoprise 2001]. Scale remains a huge barrier to commercial success. This example illustrates the importance of semantic markup and the sharing of ontologies. It also demonstrates the importance of formal ontologies and automated inference. Inference engines can be used to derive new information for a wide variety of purposes; in particular, a formally specified ontology allows agents to use theorem proving and consistency checking techniques to determine whether or not they have agreement on the semantics of their terminology. The ability of the agent to infer something about the meaning of fuel-pump depends on the existence of a formal semantics for ontology language such as DAML+OIL. The language semantics also allow the agent to infer the meaning of complex expressions built up using language primitives. The semantics of the language are not machine processible; they are written for humans only. People use them to write inference engines or other software to correctly interpret and manipulate expressions in the language. Note that today’s spectacularly impressive search engines by and large do not use formal semantics approaches. Overall it remains an unproven conjecture that such approaches will enhance search capabilities, or have significant impact anywhere else on the Web. For example, there appear to be insufficient business drivers to motivate venture capitalists to heavily invest in Semantic Web companies. Fortunately, the W3C is moving forward on this issue by identifying a wide variety of use cases to drive