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Figure 1. The concept of ‘near’ in different contexts of grocery shopping en PE CRS OT EER EE RULE nermnemem sent | Reweninenns © PPRLAIRIZ. The computer is expected to understand this request and compile a map that matches with the user’s conception of ‘near’. In order to process this request, the system has to understand a number of concepts. First, it has to understand that ‘SC town’ is a geographical entity with geographical location, area extent, boundary (although can be vaguely defined), and numerous other characteristics. Second, it needs to understand what it means by ‘near’. ‘Near’ is a basic spatial concept that should be part of the ontology of geographical space. However, ‘near’ does not correspond to any fixed set of geographical entities. Instead, what part of the earth surface that qualifies as “near SC town” depends on the actual context of the map request. Is this in the context of grocery shopping, or planning a vacation, or locating a new business office? If this is about grocery shopping, are we talking about driving a car, riding a bike, or walking? Figure 1 illustrates the idea that a person may well have two different senses of near (‘Near 1” and “Near 2”) for two different travel modes (drive or walk) in grocery shopping.

Figure 1 The concept of ‘near’ in different contexts of grocery shopping en PE CRS OT EER EE RULE nermnemem sent | Reweninenns © PPRLAIRIZ. The computer is expected to understand this request and compile a map that matches with the user’s conception of ‘near’. In order to process this request, the system has to understand a number of concepts. First, it has to understand that ‘SC town’ is a geographical entity with geographical location, area extent, boundary (although can be vaguely defined), and numerous other characteristics. Second, it needs to understand what it means by ‘near’. ‘Near’ is a basic spatial concept that should be part of the ontology of geographical space. However, ‘near’ does not correspond to any fixed set of geographical entities. Instead, what part of the earth surface that qualifies as “near SC town” depends on the actual context of the map request. Is this in the context of grocery shopping, or planning a vacation, or locating a new business office? If this is about grocery shopping, are we talking about driving a car, riding a bike, or walking? Figure 1 illustrates the idea that a person may well have two different senses of near (‘Near 1” and “Near 2”) for two different travel modes (drive or walk) in grocery shopping.

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COIN (Lee and Siegel 1996, Goh et al. Figure 2 GeoDialogue as a mediator of human-GIS conversation 1999), SemPro (Kashyap and Sheth 1996), and ISIS (Leclercq et al. 1999), assume that semantic interpretation of all the data objects are available a prior, and a common ontology commitment exists. This would be an unrealistic assumption in GeoDialogue, because the ontology on the human side can not be known to the system before interaction starts. The only ways to force a shared ontology are: (1) to train the user to learn the formal concepts and language of a geographical information system, or (2) to make the computer to ‘think’ about the space exactly like human by capturing human conceptions of space into explicit ontologies. The former will seriously limit the naturalness and usability of a GIS, and the later is still far from possible at the current stage of our knowledge about human conceptual systems. Another design goal for GeoDialogue is to maintain the highest degree of autonomy between human and the system, so that human conception of space and computer models of spatial concepts can maintain different views of the space and evolve independently over time. Figure 2 GeoDialogue as a mediator of human-GIS conversation
COIN (Lee and Siegel 1996, Goh et al. Figure 2 GeoDialogue as a mediator of human-GIS conversation 1999), SemPro (Kashyap and Sheth 1996), and ISIS (Leclercq et al. 1999), assume that semantic interpretation of all the data objects are available a prior, and a common ontology commitment exists. This would be an unrealistic assumption in GeoDialogue, because the ontology on the human side can not be known to the system before interaction starts. The only ways to force a shared ontology are: (1) to train the user to learn the formal concepts and language of a geographical information system, or (2) to make the computer to ‘think’ about the space exactly like human by capturing human conceptions of space into explicit ontologies. The former will seriously limit the naturalness and usability of a GIS, and the later is still far from possible at the current stage of our knowledge about human conceptual systems. Another design goal for GeoDialogue is to maintain the highest degree of autonomy between human and the system, so that human conception of space and computer models of spatial concepts can maintain different views of the space and evolve independently over time. Figure 2 GeoDialogue as a mediator of human-GIS conversation
~ONtEXtS Vary IM scope, Irom very DrOad tO VCTY ae an specific, with broader contexts often contain more efined and s shows a possible structure of context space for the scenario described in section 3.2. A specializec on 1S a proposi Cy 12/ O1.4.2: Grocery shopping by walking pecific contexts. This will result in collections of nested contexts. For example, figure 2 ext can be derived from a more general context by including more assumptions. If c is a context anc ion, then c' = assuming (p, c) is a new context (McCarthy and Buvac 1997). For example shopping’ can be a generic context where many features (what to shop, where to go, how to travel, etc of t ne contex s have to be explicitly described. A specialized context may be ‘shopping a textbook on < Iniversity campus’ where we can normally assume that there is a bookstore on campus and that the lominant me “ap hod of travel is by walking. A set of contexts will nested relations can also be used tc ure different degree of approximations of models to the modeled world, or different levels o: imbiguity / generality in the meaning of certain concepts and statements. More details on approximatior -On exts and ambiguity contexts can be found in (Guha and McCarthy 2003).
~ONtEXtS Vary IM scope, Irom very DrOad tO VCTY ae an specific, with broader contexts often contain more efined and s shows a possible structure of context space for the scenario described in section 3.2. A specializec on 1S a proposi Cy 12/ O1.4.2: Grocery shopping by walking pecific contexts. This will result in collections of nested contexts. For example, figure 2 ext can be derived from a more general context by including more assumptions. If c is a context anc ion, then c' = assuming (p, c) is a new context (McCarthy and Buvac 1997). For example shopping’ can be a generic context where many features (what to shop, where to go, how to travel, etc of t ne contex s have to be explicitly described. A specialized context may be ‘shopping a textbook on < Iniversity campus’ where we can normally assume that there is a bookstore on campus and that the lominant me “ap hod of travel is by walking. A set of contexts will nested relations can also be used tc ure different degree of approximations of models to the modeled world, or different levels o: imbiguity / generality in the meaning of certain concepts and statements. More details on approximatior -On exts and ambiguity contexts can be found in (Guha and McCarthy 2003).
Table | Context schema for C, ;
Table | Context schema for C, ;
A 3.5 Inter-ontology Bridging Rules: ®
A 3.5 Inter-ontology Bridging Rules: ®
Figure 4. Structure of a activity represented as a SharedPlan (after (Cai et al. 2005)) GeoDialogue uses a SharedPlan model to capture the discourse context of a dialogue. In the SharedPlans formalism (Grosz and Kraus 1996), a plan consists of a set of complex mental attitudes (beliefs, intentions, and commitments) towards a joint goal and its subgoals. A set of agents have a full SharedPlan (FSP) when all the mental attitudes required for successful collaboration have been established. Otherwise, a SharedPlan is considered partial. The generation of a discourse can be modeled as a process that conversational participants elaborate on a partial SharedPlan towards a full SharedPlan.
Figure 4. Structure of a activity represented as a SharedPlan (after (Cai et al. 2005)) GeoDialogue uses a SharedPlan model to capture the discourse context of a dialogue. In the SharedPlans formalism (Grosz and Kraus 1996), a plan consists of a set of complex mental attitudes (beliefs, intentions, and commitments) towards a joint goal and its subgoals. A set of agents have a full SharedPlan (FSP) when all the mental attitudes required for successful collaboration have been established. Otherwise, a SharedPlan is considered partial. The generation of a discourse can be modeled as a process that conversational participants elaborate on a partial SharedPlan towards a full SharedPlan.
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MathematicsComputer ScienceGeomatic EngineeringMathematical OptimizationConvergence AnalysisIterative Algorithm
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