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| - After a successful first day, the second day focusing on capturing "measurement" of an observed trait (such as biomass). This notoriously difficult concept raised much debate about dimensionality, units, standards and the idea of a dimension component (the ability to build an observed dimensionality via a simple component construct in OWL). For example, an ecologist may want to capture a measurement with units of rabbits per fox as one observation or grams phosphorus per grams carbon. To deal with this in a generic framework, a measurement would have a value and an observed dimensionality, which is like a "standard" that is mapped to the observation via a value (e.g., an observation of 2 meters would be mapped to the standard "meter" and the value "2"). An observed dimensionality is built from one or more dimension components and each component has a base dimension (e.g., gram), a power, and a categorical dimension, which is just an observed entity (e.g., carbon). Therefore, grams phosphorus per grams carbon can be built using two dimension components: base dimension = gram, power = 1, categorical dimension = phosphorus; base dimension = gram, power = -1, categorical dimension = carbon. The model including the dimension component construct can be found here. Also included in the figure is a preliminary model for observation context that was originally frame in terms of an observation window (the contextual window through which an observation was made), which is termed observation domain. |
| + After a successful first day, the second day focusing on capturing "measurement" of an observed trait (such as biomass). This notoriously difficult concept raised much debate about dimensionality, units, standards and the idea of a dimension component (the ability to build an observed dimensionality via a simple component construct in OWL). For example, an ecologist may want to capture a measurement with units of rabbits per fox as one observation or grams phosphorus per grams carbon. To deal with this in a generic framework, a measurement would have a value and an observed dimensionality, which is like a "standard" that is mapped to the observation via a value (e.g., an observation of 2 meters would be mapped to the standard "meter" and the value "2"). An observed dimensionality is built from one or more dimension components and each component has a base dimension (e.g., gram), a power, and a categorical dimension, which is just an observed entity (e.g., carbon). Therefore, grams phosphorus per grams carbon can be built using two dimension components: base dimension = gram, power = 1, categorical dimension = phosphorus; base dimension = gram, power = -1, categorical dimension = carbon. The model including the dimension component construct can be found [here|KRMeetingJan06Models]. Also included in the figure is a preliminary model for observation context that was originally frame in terms of an observation window (the contextual window through which an observation was made), which is termed observation domain. |
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| - The third day primarily focused on observation context and prompted several changes to the "entity/trait" model developed on the first day. The days debate centered about how context should be incorporated into the observation ontology and resulted in several models. The first introduced the idea of using associative and mereological properties to relate observations (e.g., biomass isAssociatedWith Species, Species isContainedIn Plot, and so forth), and debate focused on the level at which such a contextual "builder" should be placed -- entity, trait or measurement. A variant of this model can be found __here__. A second variant was then discussed in which some class names were changed (e.g., observed entity became observable, and observed trait became observable trait), but the main difference was that the contextual construct was moved to act upon the trait. This model can be found __here__. |
| + The third day primarily focused on observation context and prompted several changes to the "entity/trait" model developed on the first day. The days debate centered about how context should be incorporated into the observation ontology and resulted in several models. The first introduced the idea of using associative and mereological properties to relate observations (e.g., biomass isAssociatedWith Species, Species isContainedIn Plot, and so forth), and debate focused on the level at which such a contextual "builder" should be placed -- entity, trait or measurement. A variant of this model can be found [here|KRMeetingJan06Models]. A second variant was then discussed in which some class names were changed (e.g., observed entity became observable, and observed trait became observable trait), but the main difference was that the contextual construct was moved to act upon the trait. This model can be found [here|KRMeetingJan06Models]. |
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| - During the final half-day, the model changed fundamentally again. Continuing debate about the difference between entities and traits culminated in modeling them as the same thing, observables. This move greatly simplified the ontology (below). Therefore, an observation has a subject (an observable) and subsumes measurement (also an observation). Because traits are also observable, then an observation can have a trait observation that uses the same construct. Further, an observation has context also being an observation with an observable. This model greatly simplifies the concept of observation and renders it flexible to a range of different observation structures (data-sets). This final model was the final product of the working group and can be viewed below. Definitions for the main classes and properties are also given below. The next step is to build use case scenarios which are being outlined __here__. The final model also allows for modularization of domain specific concepts that can be termed observables. Therefore, domain models of observables can be plugged in to the observable class when annotating data. Such domain models include spatial and temporal concept ontologies, the ecological concepts ontology. The "units" ontology will be plugged into the measurement construct. We divided these tasks, which are outlined at the bottom of this document. |
| + During the final half-day, the model changed fundamentally again. Continuing debate about the difference between entities and traits culminated in modeling them as the same thing, observables. This move greatly simplified the ontology (below). Therefore, an observation has a subject (an observable) and subsumes measurement (also an observation). Because traits are also observable, then an observation can have a trait observation that uses the same construct. Further, an observation has context also being an observation with an observable. This model greatly simplifies the concept of observation and renders it flexible to a range of different observation structures (data-sets). This final model was the final product of the working group and can be viewed below. Definitions for the main classes and properties are also given below. The next step is to build use case scenarios which are being outlined [here|KRMeetingJan06Models]. The final model also allows for modularization of domain specific concepts that can be termed observables. Therefore, domain models of observables can be plugged in to the observable class when annotating data. Such domain models include spatial and temporal concept ontologies, the ecological concepts ontology. The "units" ontology will be plugged into the measurement construct. We divided these tasks, which are outlined at the bottom of this document. |
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