We present Prote´ge´-OWL extensions designed to help scientists define domain-specific ontologies for describing observational data. The extensions provide high-level forms that users can fill out from within Prote´ge´ to specify classes used to describe scientific measurements. As a user fills out a form, underlying OWL-DL axioms are automatically asserted, thus allowing users to specify relatively complex OWL-DL constraints without requiring an understanding of the technical details of OWL. Encoded in the constraints generated by the extension are a set of “best practices” for enabling improved data discovery and integration of observational data.
Earth and environmental scientists often depend on data collected from multiple research efforts to address broad scientific questions. These efforts rely on discovering, interpreting, and integrating diverse and heterogeneous data sets covering a wide range of semantic concepts. Employing domain-specific terms for describing earth and environmental data has the potential to significantly improve discovery and integration, however, only a relatively small number of ontologies have been created within these domains. We see two main barriers to ontology development within these communities: (1) the breadth of (specialized) concepts and phenomena studied requires a large and diverse number of ontological terms, and (2) the high-level of expertise needed to efficiently develop ontologies using current ontology languages and tools.
The aim of this work is to help address these challenges by adding structured,
easy-to-use forms to Prote´ge´-OWL that scientists can use to quickly create
meaningful domain-specific ontologies. Our approach leverages a generic, core observation and
measurement ontology [
Our extensions to Prote´ge´-OWL allow users to create sophisticated term definitions using simple “fill-in-the-blank” forms, while automatically generating the underlying DL axioms corresponding to the user’s input. This approach provides domain-scientists ? This work supported in part through NSF grants 0743429 and 0753144.
Rela#onship
Protocol Standard
hasContext *
*
Observa#on usesProtocol 1..1 1..1 * * usesStandard * Measurement *
* 1..1 hasMeasurement * ofEn#ty 1..1 hasValue
En#ty 1..1 ofCharacteris#c 1..1
Characteris#c
with the option of creating high-quality ontologies without having to understand and
directly work with the underlying DL formalisms of OWL. The generated axioms
encode a number of OWL-DL “best practices” (e.g., as in [
1 http://ecoinformatics.org/oboe/oboe.1.0/oboe-core.owl new classes via the standard class hierarchy panel. After a class is created or selected, the plug-in displays the appropriate form on the right-side of the window. Each form consists of a comment section as well as fields that can (optionally) be filled in. Most of these fields are filled with classes that are selected using a tree-based class selection widget, which constrains the choice of classes based on the type of class to be selected and the other values of fields as appropriate (e.g., depending on the characteristic chosen, only certain unit types can be selected). The Measurement form (shown in Fig. 2) contains the largest number of fields of all the forms, and includes fields for an observed entity, characteristic, standard, protocol, and zero or more context observations. Each context observation consists of an optional relationship type and an entity class (e.g., Fig. 2 shows the FreshWater entity and the Within relationship).
Fig. 3 shows the standard Prote´ge´ view for the class of Fig. 2. As shown, defining
this class using the Measurement form results in a non-trivial DL axiom. In this case, we
assert Measurement types (such as the one in Fig. 2) using an equivalent class axiom.
A measurement type can be viewed as a combination of a number of other classes,
and users can annotate data set attributes either directly via a measurement type or by
specifying the individual components (i.e., the entity, characteristic, standard, and so
on). By using equivalence classes, attributes can be classified into measurement types
automatically using a reasoner (such as Pellet), which also allows for data discovery
searches that are based either on measurement types or the individual components of
a measurement. We note that most other classes created using the OBOE plug-in are
defined using subclass axioms. As shown in Fig. 3 we also control the use of universal
and existential property restrictions largely following the conventions defined in [
We have presented new extensions to Prote´ge´-OWL to simplify the creation of
observation and measurement ontologies. The extensions are being used to develop controlled
vocabularies within the Santa Barbara Coastal Long-Term Ecological Research Project
and within TraitNet (for managing trait-based ecological and evolutionary research
data). These ontologies consist of thousands of terms created using the form-based
approach described here. Unlike spreadsheet-based approaches (e.g., [