Glossary

Spatial Knowledge Representation and Inference

Expert Systems for Domain-Specific Problems

Acquisition of Spatial Knowledge

Intelligent Decision Support System-An Integration

Conclusion

Spatial Knowledge Representation and Inference

Geographical knowledge may be structured or unstructured. We may organize our knowledge in a highly structured form so that problems can be solved by systematic and procedural form. Mathematical models, statistical methods, and heuristic procedures are knowledge in procedural form. This type of knowledge follows a rigid framework for the representation and analysis of structures and processes in space and time. Procedural knowledge is effective in system specification, calibration, analysis, scenario generation, and forecasting of well-specified and structured problems. Through research, we have accumulated, over the years, a wealth of procedural knowledge which can be effectively utilized for geographical analysis in spatial information systems. A majority of knowledge, however, is loosely structured. Subjective experience, valuation, intuition, and loosely structured expertise often cannot be appropriately captured by rigid procedures. They are declarative in nature and can only be represented by more flexible frameworks. Making inferences from such knowledge structures cannot be done procedurally. Problem-solving by if–then arguments is a typical example of using declarative knowledge for decision-making. This type of knowledge is effective in solving unstructured or semistructured problems. It is suitable for inference with concepts, ideas, and values. Similar to the use of procedural knowledge, declarative knowledge can be utilized in spatial decision-making, especially with spatial information systems. Declarative knowledge is, however, ineffective to solve highly structured problems. Consequently, procedural and declarative knowledge have to be used in synchrony throughout a decision-making process. Once a spatial structure or process is understood and can be specified in a formal and structured manner, we can always capture it by a mathematical model or procedure. The representation of loosely structured knowledge is, nevertheless, not as straightforward. Declarative knowledge representation and inference have thus become a main concern in building spatial reasoning systems with artificial intelligence. To be able to understand and to reason, an intelligent machine needs prior knowledge about the problem domain. To understand sentences used to describe geographical phenomena, for example, natural language understanding systems have to be equipped with prior knowledge about topics of those phenomena. To be able to see and interpret scenes, spatial vision systems need to have in store prior information about objects to be seen. This also applies to the deep learning paradigm intensively studied in recent years. Therefore, any intelligent system should possess a knowledge or training database containing facts and concepts related to a problem domain and their relationships. There should also be an inference mechanism which can process symbols in the knowledge base and derive implicit knowledge from explicitly expressed knowledge. Knowledge representation formalism consists of a structure to express domain knowledge, a knowledge representation language, and an inference mechanism. Conventionally, its duty is to select an appropriate symbolic structure to represent knowledge in the most explicit and formal manner, and an appropriate mechanism for reasoning.