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The facility is located at some lot of land that is identified by the facility address. I would say I'm sure of that but there may be farms that are identified only rural route. That exception leads me to think that the person/organization/facility name is also a part of the address. Past that, the spatial relationships between lot, city, and state are pretty well behaved although there are counter-examples to be found. ZIP codes are not so well-behaved, some span multiple states and some identify organizations more that a land area (Smokey the Bear has one, 20252). So I do think its safe to say that a facility location is a proper spatial part of a region identified by ZIP code but adding those assertions don't seem to me to strengthen the identifying relation between the full address and the facility. |
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I agree, but I guess I didn't make my point well. I want to write a SPARQL query that retrieves a residence's street, city, state, and zip code. If a state (or in my case, a city) is the object of multiple located-in relationships with a geospatial region as the subject, the query will return a result for each geospatial region. Asserting which geospatial region the residential facility is located in would resolve the ambiguity. Is there another way? Also, I just noticed that the last sentence in the first paragraph of Section 4 has some extra words:
You could drop “are intended” or “were selected”. |
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@swartik I believe the issues raised here warrant a more comprehensive review and discussion. Hence moving to discussion forum now. |
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I was using the paradigm for a person's address in Modeling Information with the Common Core Ontologies (Section 4.1). It seems to me that the pattern is incomplete. It locates the geospatial region for a zip code within a state. The thing is, many such geospatial regions are located within a state – within a city, for that matter. What is the relationship between a residential facility and its zip code? Is the picture missing an additional located_in between those two individuals?
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