Is it a good thing to keep down the datasize for an Mt?
Keeping the datasize for an Mt down makes it easier for a person to review the material in the Mt. Reasoning over sets of similar object types (rivers, cities, etc.) is sometimes slowed down the larger the set that is examined. So if you ask a question in a microtheory that inherits from SwedenNaturalGeographyMt but not from FinlandNaturalGeographyMt (or the others) about rivers and the bodies of water into which they flow, the inference engine would not have to consider the 100,000 lakes in Finland (or however many you enter) and the rivers in the other Scandinavian countries (or the US).With natural language input, this becomes more important. For example, if you include data about all the named farmsteads in Norway and Sweden in the knowledge base and refer to a farm in Sweden by name, the system could reject parses involving Norwegian farms of the same name if you are asking the question in a microtheory that inherits from SwedenGeographyMt and not from NorwayGeographyMt.
Keeping the datasize for an Mt down makes it easier for a person to review the material in the Mt. Reasoning over sets of similar object types (rivers, cities, etc.) is sometimes slowed down the larger the set that is examined. So if you ask a question in a microtheory that inherits from SwedenNaturalGeographyMt but not from FinlandNaturalGeographyMt (or the others) about rivers and the bodies of water into which they flow, the inference engine would not have to consider the 100,000 lakes in Finland (or however many you enter) and the rivers in the other Scandinavian countries (or the US). With natural language input, this becomes more important. For example, if you include data about all the named farmsteads in Norway and Sweden in the knowledge base and refer to a farm in Sweden by name, the system could reject parses involving Norwegian farms of the same name if you are asking the question in a microtheory that inherits from SwedenGeographyMt and not from NorwayGeographyMt.
