Ontologies of Space and Ground Systems - Revision history

Phismith at 21:41, 30 December 2019

2019-12-30T21:41:07Z

← Older revision		Revision as of 21:41, 30 December 2019
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	Barry Smith and David Limbaugh		Barry Smith and David Limbaugh

	Center for Multisource Information Fusion (CMIF), Lockwood Library B20 (entrance via basement of Baldy Hall), University at Buffalo (North Campus), January 22, 2020		Center for Multisource Information Fusion (CMIF), Lockwood Library B20 (entrance via basement of Baldy Hall), University at Buffalo (North Campus), 9:00am, January 22, 2020

	'''Background'''		'''Background'''

Phismith at 21:19, 30 December 2019

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	Barry Smith and David Limbaugh		Barry Smith and David Limbaugh

	~~Cejter~~ for Multisource Information Fusion (CMIF), Lockwood Library B20 (entrance via basement of Baldy Hall), University at Buffalo (North Campus), January 22, 2020		Center for Multisource Information Fusion (CMIF), Lockwood Library B20 (entrance via basement of Baldy Hall), University at Buffalo (North Campus), January 22, 2020

	'''Background'''		'''Background'''

Phismith at 19:20, 30 December 2019

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	Barry Smith and David Limbaugh		Barry Smith and David Limbaugh

	CMIF, Lockwood Library B20 (entrance via basement of Baldy Hall), University at Buffalo (North Campus), January 22, 2020		Cejter for Multisource Information Fusion (CMIF), Lockwood Library B20 (entrance via basement of Baldy Hall), University at Buffalo (North Campus), January 22, 2020

	'''Background'''		'''Background'''

Phismith at 19:19, 30 December 2019

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	'''Background'''		'''Background'''
	When the term ‘ontology’ was first used in AI research in the 1970s, an ontology was conceived as the formal codification of some body of knowledge. To make such knowledge shareable and analyzable in different systems, the idea of knowledge interchange formats arose. By the early 2000s the latter had evolved to become what we now know as the Ontology Web Language (OWL). Since then, OWL has been used in the development of many kinds of ontologies, typically to promote the sharing and analysis of specific, locally important bodies of data. Unfortunately, such ontologies almost always lose their usefulness with the addition of new sorts of data. Over time, therefore, ontology development efforts came to be dismissed in many circles because they were seen as being too fragile to be of long-term usefulness.		When the term ‘ontology’ was first used in AI research in the 1970s, an ontology was conceived as the formal codification of some body of knowledge. To make such knowledge shareable and analyzable in different systems, the idea of knowledge interchange formats arose. By the early 2000s the latter had evolved to become what we now know as the Ontology Web Language (OWL). Since then, OWL has been used in the development of many kinds of ontologies, typically to promote the sharing and analysis of specific, locally important bodies of data. Unfortunately, such ontologies almost always lose their usefulness with the addition of new sorts of data. Over time, therefore, ontology development efforts came to be dismissed in many circles because they were seen as being too fragile to be of long-term usefulness.

	In the wake of the Human Genome Project, however, there arose independently a new way of viewing ontologies. As massive quantities of -omics data came onstream, ways had to be found to make these data useful to clinical diagnosis. The needed connection was established by creating controlled structured vocabularies – called ‘ontologies’ – for different parts of traditional biology and medicine. Importantly, these were viewed from the beginning as part of a single suite of interoperable ontology modules, and were designed to be useful even as data, and software, and hardware, and scientific and clinical knowledge change.		In the wake of the Human Genome Project, however, there arose independently a new way of viewing ontologies. As massive quantities of -omics data came onstream, ways had to be found to make these data useful to clinical diagnosis. The needed connection was established by creating controlled structured vocabularies – called ‘ontologies’ – for different parts of traditional biology and medicine. Importantly, these were viewed from the beginning as part of a single suite of interoperable ontology modules, and were designed to be useful even as data, and software, and hardware, and scientific and clinical knowledge change.

	~~These ontologies were exploited~~ in ~~a massive exercise of data and literature tagging~~, ~~so that hitherto divergent bodies of data and information became discoverable, integratable, and analyzable using a single common modular framework of ontology terms~~. ~~This tutorial will describe~~ on~~-going initiatives to extend this modular approach into other domains~~, ~~focusing specifically on space~~ and ~~ground systems. It~~ will ~~survey existing ontologies of relevance, show~~ how ~~ontologies in these domains are created and used,~~ and ~~describe~~ how ~~individuals and groups can join these efforts~~.		'''Basic Formal Ontology'''
			The organizational hub in each case is Basic Formal Ontology (BFO), which was approved as ISO standard (ISO/IEC 21838-2) in October 2019. BFO provides the general architecture shared by domain ontologies on lower tiers, and we will describe how BFO works and how BFO serves as starting point for building domain ontologies on lower levels.

	~~'''Basic Formal Ontology'''~~		We will describe an on-going initiative to extend this modular approach into other domains, focusing specifically on space and ground systems. We will survey existing ontologies of relevance, show how ontologies in these domains are created and used, and describe how individuals and groups can join these efforts.
	~~The organizational hub in each case is Basic Formal Ontology (BFO)~~, ~~which was approved as ISO standard (ISO/IEC 21838-2) in October 2019~~. ~~BFO provides the general architecture shared by domain~~ ontologies ~~on lower tiers~~, and ~~we will~~ describe how ~~BFO works~~ and ~~how BFO serves as starting point for building domain ontologies on lower levels~~.

	'''Three types of domain ontologies'''		'''Three types of domain ontologies'''
	When data analysts work for example with source data deriving from some satellite feed, then they are interested primarily on what these data describe, for example forest fires or shipping movements. The analyst’s work requires also, however, a secondary focus, targeted to the data and information artifacts themselves – including images, reports, emails – through which such information is conveyed. These artifacts have attributes – including format, purpose, evidence, provenance, reliability, and so forth – data about which are vital to the effective exploitation of the object-level data by humans and machines. This implies that we need domain ontologies relating not only to (1) real-world objects and processes, (2) information entities such as data and images, but also to (3) the processes performed by data analysts and by the software and hardware they use.		When data analysts work for example with source data deriving from some satellite feed, then they are interested primarily on what these data describe, for example forest fires or shipping movements. The analyst’s work requires also, however, a secondary focus, targeted to the data and information artifacts themselves – including images, reports, emails – through which such information is conveyed. These artifacts have attributes – including format, purpose, evidence, provenance, reliability, and so forth – data about which are vital to the effective exploitation of the object-level data by humans and machines. This implies that we need domain ontologies relating not only to :
			:(1) real-world objects and processes,
			:(2) information entities such as data and images, but also to
			:(3) the processes performed by data analysts and by the software and hardware they use.

	'''Ontologies relevant to space and ground system domains'''		'''Ontologies relevant to space and ground system domains'''

Phismith: Created page with "Barry Smith and David Limbaugh CMIF, Lockwood Library B20 (entrance via basement of Baldy Hall), University at Buffalo (North Campus), January 22, 2020 '''Background''' When..."

2019-12-30T19:17:47Z

Created page with "Barry Smith and David Limbaugh CMIF, Lockwood Library B20 (entrance via basement of Baldy Hall), University at Buffalo (North Campus), January 22, 2020 '''Background''' When..."

New page

Barry Smith and David Limbaugh

CMIF, Lockwood Library B20 (entrance via basement of Baldy Hall), University at Buffalo (North Campus), January 22, 2020

'''Background'''
When the term ‘ontology’ was first used in AI research in the 1970s, an ontology was conceived as the formal codification of some body of knowledge. To make such knowledge shareable and analyzable in different systems, the idea of knowledge interchange formats arose. By the early 2000s the latter had evolved to become what we now know as the Ontology Web Language (OWL). Since then, OWL has been used in the development of many kinds of ontologies, typically to promote the sharing and analysis of specific, locally important bodies of data. Unfortunately, such ontologies almost always lose their usefulness with the addition of new sorts of data. Over time, therefore, ontology development efforts came to be dismissed in many circles because they were seen as being too fragile to be of long-term usefulness.
In the wake of the Human Genome Project, however, there arose independently a new way of viewing ontologies. As massive quantities of -omics data came onstream, ways had to be found to make these data useful to clinical diagnosis. The needed connection was established by creating controlled structured vocabularies – called ‘ontologies’ – for different parts of traditional biology and medicine. Importantly, these were viewed from the beginning as part of a single suite of interoperable ontology modules, and were designed to be useful even as data, and software, and hardware, and scientific and clinical knowledge change.

These ontologies were exploited in a massive exercise of data and literature tagging, so that hitherto divergent bodies of data and information became discoverable, integratable, and analyzable using a single common modular framework of ontology terms. This tutorial will describe on-going initiatives to extend this modular approach into other domains, focusing specifically on space and ground systems. It will survey existing ontologies of relevance, show how ontologies in these domains are created and used, and describe how individuals and groups can join these efforts.

'''Basic Formal Ontology'''
The organizational hub in each case is Basic Formal Ontology (BFO), which was approved as ISO standard (ISO/IEC 21838-2) in October 2019. BFO provides the general architecture shared by domain ontologies on lower tiers, and we will describe how BFO works and how BFO serves as starting point for building domain ontologies on lower levels.

'''Three types of domain ontologies'''
When data analysts work for example with source data deriving from some satellite feed, then they are interested primarily on what these data describe, for example forest fires or shipping movements. The analyst’s work requires also, however, a secondary focus, targeted to the data and information artifacts themselves – including images, reports, emails – through which such information is conveyed. These artifacts have attributes – including format, purpose, evidence, provenance, reliability, and so forth – data about which are vital to the effective exploitation of the object-level data by humans and machines. This implies that we need domain ontologies relating not only to (1) real-world objects and processes, (2) information entities such as data and images, but also to (3) the processes performed by data analysts and by the software and hardware they use.

'''Ontologies relevant to space and ground system domains'''
We will survey the Common Core Ontologies (CCO) developed under the IARPA KDD initiative, and the Space Domain Ontologies, a suite of space situational awareness ontologies which extends the CCO. Specifically we will examine the following ontology modules:

Ontologies for Ground Systems
:Agent Ontology (person and organization profiles, including identifiers, roles, employment history, skills, capabilities)
:Sensor Ontology (types of sensors, primary systems, principal components, functions and capabilities, sensor processes)
:Cyber Ontology (types of hardware and software and processes including cyber attacks)
:Cognitive Process Ontology (CPO)

Space Domain Ontologies (SDO)
:Spacecraft Mission Ontology
:Spacecraft Ontology
:Space Event Ontology
:Space Object Ontology

Background Reading: R. Arp, et al., ''Building Ontologies with Basic Formal Ontology'', MIT Press, 2015