Science Environment for Ecological Knowledge
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Science Environment for Ecological Knowledge




KR Meeting Jan 06 Summary


Meeting Summary

The Knowledge Representation 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 datasets. 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.

During the first day, discussion centered on what information is necessary to distinguish among 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, but will not be discussed in any detail due to fundamental changes in subsequent days of the meeting.

The second day focused on capturing "measurement" of an observed trait (e.g., biomass or height). This notorious issue raised much debate about dimensions, units and standards and culminated in the idea of dimension components (the ability to build an observed dimensionality using 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. An observed dimensionality is a "standard" that is mapped to the observation via a value (e.g., an observation of 2 m 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 (i.e., an observed entity such as 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. Also included in the figure is a preliminary model for observation context that was originally framed in terms of an observation window (the contextual window through which an observation was made) which is termed an observation domain.

The third day primarily focused on observation context and prompted several changes to the "entity/trait" model developed on the first day. The day’s debate centered on 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. 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.

During the final half-day, the model changed significantly again. Continuing debate about the difference between entities and traits, and whether they need to be treated separately, 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, an observation can have a trait observation that uses the same construct. Further, an observation has context, and this context is also an observation with an observable. This model greatly simplifies the concept of observation and renders it flexible to a range of different observation structures (datasets). This final model was the main product of the working group and can be viewed here. Definitions for the main classes and properties are also given here. An example annotation using this version of OBOE can be found here.

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 and the ecological concepts ontology. The "units" ontology will be plugged into the measurement construct. We divided these tasks, which are outlined here.

Upper Level Structure

Upper Level Definitions


  • Observation: An assertion that an entity (observable) was observed. The observable is the subject of the observation. An observation may be composed of other observations serving as traits that are required to define (observe) the subject entity. An observation may also contribute to defining the context for another observation.

  • Observable: A particular entity that can be observed to have one or more characteristics, or traits.

  • Measurement: An observation that assigns a value and dimension to the observable.


  • hasObservation: To observe a observable your statement of what the subject is may require observing other subjects. This relationship is the way you connect the observation of those traits to the overall observation. The traits further define the overall observable.

  • hasSubject: Relates the observable to the observation. States the overall subject of the observation.

  • hasContext: States that one observation serves as the context for another observation. Defines a semantic relationship between two observables that is a fundamental aspect of the observations, but not of the observables themselves. For example, most measurements are accomplished in a spatio-temporal framework that might be valuable context.

OWL files

Example annotation



  • Definitions of classes and properties in ObsOnt

  • Write technical note/tutorial that explain how to use OBOE
    • E.g., how to represent dimensions
    • How to extend ontology with domain-specific modules

  • Publish OBOE with examples (case studies) of its utility
    • Ecological Applications, Ecological Informatics, Ecological Modeling?

  • Ontology devlopment
    • Development of Ecological Entity Ontology
      • focus on DOLCE, over SUMO -- DOLCE has clearer formal approach
      • Carefully look at SWEET so that can say why we didn't use it (if we don't)
      • Look at Ontology (by B. Smith)
    • Development of Ecological Trait Ontology
    • Development of Units Ontology
    • Development of Value Type Ontology

  • Produce list of conference announcements and potential journal for publishing

  • Produce a number of case-studies






Daily Models

Day 1

Day 2

Day 3.1

Day 3.2

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This page last changed on 28-Jul-2006 16:30:20 PDT by uid=madin,o=NCEAS.