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| - The KR working group met for three days in San Antonio, TX, with the aim of producing a core ontology for ecological observation. The proposed scope of the observation ontology for ecology (OBOE) was to capture the information/concepts needed to combine or determine the differences/similarities between two data-sets. The proposed uses for such as ontology are consistency checking, data annotation and data integration. This scope and uses of OBOE will be elaborated in a formal document that will be appended __here__ in the near future. |
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| - During the first day discussion centered on what information is necessary to distinguish between two data, and resulted in the development of a model that captured the "entity" observed, the "trait" measured, and the "measurement" taken in terms of value and dimensionality. The first day model can be found [here|KRMeetingJan06Summary#Day 1], but will not be discussed any further due to a fundamental changes to this model in following days. |
| + The Knowledge Representation working group met for three days in San Antonio, TX, with the aim to produce a core ontology for ecological observation. The proposed scope of the observation ontology for ecology (OBOE) was to capture the information/concepts needed to combine or determine the differences/similarities between two data-sets. The proposed uses for such as ontology are consistency checking, data annotation and data integration. This scope and uses of OBOE will be elaborated in a formal document that will be appended __here__ in the near future. |
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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| KRMeetingJan06Summary#Day 2]. 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. |
| + During the first day, discussion centered on what information is necessary to distinguish between two data, and resulted in the development of a model that captured the "entity" observed, the "trait" measured, and the "measurement" taken in terms of value and dimensionality. The first day model can be found [here|KRMeetingJan06Summary#Day 1], but will not be discussed in any detail due to fundamental changes in subsequent days of the meeting. |
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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| KRMeetingJan06Summary#Day 3.1]. 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| KRMeetingJan06Summary#Day 3.2]. |
| + The second day focusing on capturing "measurement" of an observed trait (e.g., biomass or height). This notorious issue raised much debate about dimensionality, units, standards and culminated in the idea of dimension components (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 Day 2 model including the dimension component construct can be found [here|KRMeetingJan06Summary#Day 2]. 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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| - 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| KRMeetingJan06Summary#Day 4]. 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. |
| + 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 contextual constructs. 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, i.e., entity, trait or measurement. A variant of this model can be found [here|KRMeetingJan06Summary#Day 3.1]. 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|KRMeetingJan06Summary#Day 3.2]. |
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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 [here|KRMeetingJan06Summary#Upper Level Structure]. Definitions for the main classes and properties are also given [here|KRMeetingJan06Summary#Upper Level Definitions]. The next step is to build use case scenarios which are being outlined [here|KRMeetingJan06]. 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 and the ecological concepts ontology. The "units" ontology will be plugged into the measurement construct. We divided these tasks, which are outlined [here|KRMeetingJan06Summary#Tasks]. |
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