Difference (from revision 7 to 8)

Changed: 3c3

'''In general''' an ontology specifies of a vocabulary of concepts predicates together with some indication of their meanings. There is a range of levels of precision with which meaning is specified, but an overall goal is that people often want to share a common understanding of the structure of information among people or software agents. Often there are twin targets to make the meaning clear to people while allowing a degree of "automated" processing. In this case an ontology is used to make explicit the semantics and knowledge contained within efforts such as software applications. {nid 2JOA}
''The following summary is a non-technical start on this topic by Dr. Gary Berg-Cross. This discussion reflects some early work by SOCoP members and their demonstrations. It is intended to be a starting point for further input from the SOCoP and broader ontology community on this topic as an aid to the Interop effort. The discussion is at the conceptual level for domain specialists and does not try to discuss technical issues such as the use of ontology languages for representing ontological models. Within the Interop effort domain experts are not expected to have to deal with this issue'' {nid 2JOF}

Changed: 5,7c5

An ontological model is made up of classes/concepts (at least partial hierarchical) along with properties & attributes for these
concepts (usually with descriptions to help humans)plus
constraints on properties and attributes. The backbone on an Ontology is made up of Classes in a formal Hierarchy. {nid 2JOB}
'''In general''' an ontology specifies a vocabulary of concepts together with some indication of their meanings. There is a range of levels of precision with which meaning is specified, but an overall goal is for the ontology to express a common understanding of the structure of information sutiable among people and-or software agents. Often there are twin targets to make the meaning clear to people while allowing a degree of "automated" processing. In this case an ontology is used to make explicit the semantics and knowledge contained within efforts such as software applications. {nid 2JOA}

Changed: 9c7

A class is a concept in the domain so we may have a class of organizations (e.g. USGS) or a class of regions (e.g. Mid-Atlantic States). A class also has a population. It is a collection of the elements with similar properties defined by the class concept. Thus there are instances of states in the Mid-Atlantic class. {nid 2JOC}
An ontological model is made up of classes/concepts (at least partial hierarchical) along with properties & attributes for these concepts (usually with descriptions to help humans)plus constraints on properties and attributes. The backbone on an Ontology is made up of Classes in a formal Hierarchy. {nid 2JOB}

Changed: 11c9

(2JDG) {nid 2JO8}
A class is a concept in the domain so we may have a class of organizations (e.g. USGS) or a class of regions (e.g. Mid-Atlantic States). A class also has a population called instances which is the collection of the elements with similar properties defined by the class concept. Thus there are instances of states such as Maryland in the Mid-Atlantic class. {nid 2JOC}

Changed: 13c11,13

As part of such an effort an ontology can be used to enable reuse of domain knowledge and to make domain assumptions explicit or to separate domain knowledge in a declarative form from the operational knowledge which can be implemented in software. (2JDH) {nid 2JO9}
We can illustrate these general ideas using a very small, informal ontology made up of a few geospatial objects and some general relations. The Figure below illustrates an ontology using 3 general relations - part, sub-type and instance describing that portion of the USGS hydrology model for "hydrologic units". The ontological model has 4 sub-types showing a range of aggregation. Thus regions or sub-regions are a type of hydrologic unit and a specific instance of a region is the Mid-Atlantic, which includes as part of it Delaware. For accounting purposes Delaware may be dividing into parts - in this case and upper and lower part. These are asserted as both part of Delaware and as an instance of an "accounting Unit"
http://ontolog.cim3.net/file/work/SOCoP/Pictures/example%20of%20an%20ontology.png
Ontology from '''Hydrologic Ontologies Framework (HOW)''' by Michael Piasecki, Bora Beran & Luis Bermudez {nid 2JOD}

Changed: 15c15

Geospatial ontologies take as their domain a range of geospatial concepts such as geospatial objects, relations and features. Relations start with seemingly basic things such as topological ideas of "near", "connected" and "around" as well as other common spatial relationships in use, equals, disjoint, intersects, touches, crosses, within, contains, and overlaps. Spatial objects include abstract spatial notions such as "place" or "locations". Geospatial objects may be abstracted to geometrical concepts like a point or polygon area concepts in order to be understood at different levels of granularity. Thus an area like DC can be presented as a small oval on a national map or a complex area when zoomed up close. Features include "size" and "volume". These provide summative information about spatial things. Besides these simple ideas it also includes more macro, aggregated and complex concepts like "river", "estuary", "pond" and "lake". Know about each of these would include relations to other things (e.g. each has boundaries with non-water objects, a river may connect to a lake etc.) Ontologies attempt to distinguish all of these concepts in explicit and precise ways to avoid confusions. {nid 2JO7}
A final sub-type of hydrologic unit is a "cataloging unit" which has even finer part of accounting units and in this case the Schuylkill is an instance of this and part of the Lower Delaware accounting unit. A more complete ontology might include features of regions and units such as its size, borders etc. {nid 2JOE}

Changed: 17,18c17

An informal example of a very small ontology is show below illustrating a 3 relations (part, sub-type and instance) of a portion of the USGS hydrology model for "hydrologic units". The ontological model has 4 sub-types showing a range of aggregation. Thus regions or sub-regions are a type of hydrologic unit and a specific instance of a region is the Mid-Atlantic, which includes as part of it Delaware. For accounting purposes Delaware may be dividing into parts - in this case and upper and lower part. These are asserted as both part of Delaware and as an instance of an "accounting Unit"
http://ontolog.cim3.net/file/work/SOCoP/Pictures/example%20of%20an%20ontology.png {nid 2JOD}
As part of such an effort an ontology can be used to enable reuse of domain knowledge and to make domain assumptions explicit or to separate domain knowledge in a declarative form from the operational knowledge which can be implemented in software. (2JDH) {nid 2JO9}

Changed: 20c19,21

A final sub-type of hydrologic unit is a "cataloguing unit" which has even finer part of accounting units and in this case the Skullkill is an instance of this and part of the Lower Delaware accounting unit. {nid 2JOE}
''' Geospatial ontologies ''' take as their domain a range of geospatial concepts such as geospatial objects (such as in the previous hydrologic example), relations (including special ones for spatial concepts) and features. {nid 2JOG}

Relations start with seemingly basic things such as topological ideas of "near", "connected" and "around" as well as other common spatial relationships in use, equals, disjoint, intersects, touches, crosses, within, contains, and overlaps. Spatial objects include abstract spatial notions such as "place" or "locations". Geospatial objects, such as a city or lake may be abstracted to geometrical concepts like a point or polygon area concepts in order to be understood at different levels of granularity. Thus an area like DC can be presented as a small oval on a national map or a complex area when zoomed up close. Features include "size" and "volume". These provide summative information about spatial things. Besides these simple ideas it also includes more macro, aggregated and complex concepts like "river", "estuary", "pond" and "lake". Know about each of these would include relations to other things (e.g. each has boundaries with non-water objects, a river may connect to a lake etc.) Ontologies attempt to distinguish all of these concepts in explicit and precise ways to avoid confusions. {nid 2JO7}