Difference between revisions of "Provenir Ontology"
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+ | |title= <span style="color:#ffffff">What is Provenir Ontology (PO)?</span> | ||
+ | |title_background=#008b8b | ||
+ | |content= A reference ontology for modeling domain-specific provenance[[Image:The_provenir_ontology_schema.jpg | right | thumb | Provenir Ontology Schema]] | ||
Provenance, from the French word ‘provenir’ meaning to come from, describes the lineage of an entity. Provenance is critical information in eScience to accurately interpret scientific results. Information provenance has been recognized as a hard problem in computing science (British Computing Society, 2004), and many research issues in provenance are yet to be addressed. For example, a common provenance model to facilitate interoperability of provenance metadata and to support analysis using inferencing rules has not been defined. | Provenance, from the French word ‘provenir’ meaning to come from, describes the lineage of an entity. Provenance is critical information in eScience to accurately interpret scientific results. Information provenance has been recognized as a hard problem in computing science (British Computing Society, 2004), and many research issues in provenance are yet to be addressed. For example, a common provenance model to facilitate interoperability of provenance metadata and to support analysis using inferencing rules has not been defined. | ||
− | We introduce the provenir ontology as a common provenance model, which forms the core component of a modular approach to provenance management framework in eScience. Domain-specific details are an important component of provenance representation. But, a single monolithic provenance ontology that models all possible details from different domains (biology, marine sciences, and astronomy) is clearly not feasible. Hence, our proposed modular approach involves integrated use of multiple ontologies, each modeling provenance metadata specific to a particular domain (for example, the ProPreO ontology represents proteomics domain-specific provenance[1]). These multiple ontologies will use the provenir ontology as the common reference model, hence making it easier for their associated instances to be interoperable. This modular framework represents a scalable and flexible approach to provenance modeling that can be adapted to the specific requirement of different domains. In the next two Sections, we describe the classes and the properties in the provenir ontology (Figure 1). < | + | We introduce the provenir ontology as a common provenance model, which forms the core component of a modular approach to provenance management framework in eScience. Domain-specific details are an important component of provenance representation. But, a single monolithic provenance ontology that models all possible details from different domains (biology, marine sciences, and astronomy) is clearly not feasible. Hence, our proposed modular approach involves integrated use of multiple ontologies, each modeling provenance metadata specific to a particular domain (for example, the ProPreO ontology represents proteomics domain-specific provenance[1]). These multiple ontologies will use the provenir ontology as the common reference model, hence making it easier for their associated instances to be interoperable. This modular framework represents a scalable and flexible approach to provenance modeling that can be adapted to the specific requirement of different domains. In the next two Sections, we describe the classes and the properties in the provenir ontology (Figure 1). |
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− | To represent provenance metadata classes we use the two well defined, primitive concepts of “occurrent” and “continuant” from philosophical ontology[2]. Continuant is defined as “… entities which endure, or continue to exist, through time while undergoing different sorts of changes, including changes of place” [2]. Occurrent is defined as “…entities that unfold themselves in successive temporal phases”[2]. | + | | |
+ | {{block | ||
+ | |title= <span style="color:#ffffff">Classes of Provenir Ontology (PO)</span> | ||
+ | |title_background=#008b8b | ||
+ | |content= To represent provenance metadata classes we use the two well defined, primitive concepts of “occurrent” and “continuant” from philosophical ontology[2]. Continuant is defined as “… entities which endure, or continue to exist, through time while undergoing different sorts of changes, including changes of place” [2]. Occurrent is defined as “…entities that unfold themselves in successive temporal phases”[2]. | ||
We define three base classes in the provenir ontology representing the primary components of provenance, that is, “data”, “agent” and “process”. The two base classes, “data” and “agents” are defined as specialization (sub-class) of continuant class. The third base class “process” is a synonym of occurrent. We present the definition of each class capturing inheritance relationship: | We define three base classes in the provenir ontology representing the primary components of provenance, that is, “data”, “agent” and “process”. The two base classes, “data” and “agents” are defined as specialization (sub-class) of continuant class. The third base class “process” is a synonym of occurrent. We present the definition of each class capturing inheritance relationship: | ||
# data: This class models continuant entities that represent the starting material, intermediate material, end products of a scientific experiment, and parameters that affect the execution of a scientific process. Data inherit the properties of continuants such as enduring or existing while undergoing changes. | # data: This class models continuant entities that represent the starting material, intermediate material, end products of a scientific experiment, and parameters that affect the execution of a scientific process. Data inherit the properties of continuants such as enduring or existing while undergoing changes. | ||
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:::* spatial_parameter: The spatial metadata associated with individuals of process or agent or data_collection classes is represented by this class. The geographical location of an ocean buoy is an example of spatial parameter. | :::* spatial_parameter: The spatial metadata associated with individuals of process or agent or data_collection classes is represented by this class. The geographical location of an ocean buoy is an example of spatial parameter. | ||
:::* domain_parameter: The domain_parameter class is used to model domain-specific parameters (for example, tolerable salinity levels for ocean buoys). | :::* domain_parameter: The domain_parameter class is used to model domain-specific parameters (for example, tolerable salinity levels for ocean buoys). | ||
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− | ==Properties= | + | |- |
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− | In this section, we define a set of foundational properties in the provenir ontology. | + | {{block |
+ | |title= <span style="color:#ffffff">Properties of Provenir Ontology (PO)</span> | ||
+ | |title_background=#008b8b | ||
+ | |content= In this section, we define a set of foundational properties in the provenir ontology. | ||
Instead of defining a new set of properties, we adapt the properties defined in the Relation ontology (RO) from the Open Biomedical Ontologies (OBO) Foundry: | Instead of defining a new set of properties, we adapt the properties defined in the Relation ontology (RO) from the Open Biomedical Ontologies (OBO) Foundry: | ||
# part_of – This property is defined for each of the three base classes of provenir ontology. The restriction for this property is that the domain and range values belong to the same class. For example, if data is defined as the domain/range of the properties, the corresponding range/domain is also data. As defined in the RO [2], this property satisfies the standard axioms of mereology, that is, reflexivity, anti-symmetry, and transitivity. | # part_of – This property is defined for each of the three base classes of provenir ontology. The restriction for this property is that the domain and range values belong to the same class. For example, if data is defined as the domain/range of the properties, the corresponding range/domain is also data. As defined in the RO [2], this property satisfies the standard axioms of mereology, that is, reflexivity, anti-symmetry, and transitivity. | ||
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:::* has_temporal_value – This is a specific property to assign temporal value to individuals of data_collection, process, and agent classes. | :::* has_temporal_value – This is a specific property to assign temporal value to individuals of data_collection, process, and agent classes. | ||
:::* located_in – An instance of data or agent is associated with exactly one spatial region that is its exact location at given instance of time. In provenir ontology, this relation has two domain class agent and data_collection with spatial_parameter as range class. | :::* located_in – An instance of data or agent is associated with exactly one spatial region that is its exact location at given instance of time. In provenir ontology, this relation has two domain class agent and data_collection with spatial_parameter as range class. | ||
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+ | |title= <span style="color:#ffffff">Quick Links</span> | ||
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+ | * Applications of Provenir Ontology in different domains | ||
+ | **[[Biomedical Sciences]] | ||
+ | **[[Oceanography]] | ||
+ | **[http://knoesis.wright.edu/library/download/SPOT-Provenance_Aware_LSD.pdf Sensor] | ||
+ | **[[Health Care]] | ||
+ | * [http://knoesis1.wright.edu/library/ontologies/provenir/provenir.owl Download Provenir ontology schema (OWL file)]* | ||
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+ | |title= <span style="color:#ffffff">Citing Provenir ontology</span> | ||
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+ | Please use the following reference when citing Provenir ontology | ||
+ | * Satya S. Sahoo, Amit Sheth, 'Provenir ontology: Towards a Framework for eScience Provenance Management', Microsoft eScience Workshop, Pittsburgh, PA Oct 15-17, 2009 | ||
+ | }} | ||
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==References== | ==References== | ||
1. Sahoo SS, Thomas, C., Sheth, A., York, W. S., and Tartir, S. Knowledge modeling and its application in life sciences: a tale of two ontologies. In: Proceedings of the 15th international Conference on World Wide Web WWW '06 2006 May 23 - 26; Edinburgh, Scotland; 2006. p. 317-326. <br/> | 1. Sahoo SS, Thomas, C., Sheth, A., York, W. S., and Tartir, S. Knowledge modeling and its application in life sciences: a tale of two ontologies. In: Proceedings of the 15th international Conference on World Wide Web WWW '06 2006 May 23 - 26; Edinburgh, Scotland; 2006. p. 317-326. <br/> | ||
2. Smith B, Ceusters W, Klagges B, Kohler J, Kumar A, Lomax J, et al. Relations in biomedical ontologies. Genome Biol 2005;6(5):R46. | 2. Smith B, Ceusters W, Klagges B, Kohler J, Kumar A, Lomax J, et al. Relations in biomedical ontologies. Genome Biol 2005;6(5):R46. |
Latest revision as of 00:50, 14 May 2011
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