Difference (from prior revision)

Changed: 28c28

This year's Ontology Summit is titled "Ontology for Big Systems" and seeks to explore, identify and articulate how ontological methods can benefit the various disciplines required to engineer a "big system." The term "big system" is intended to cover a large scope that includes many of the terms encountered in the media such as big data, complex techno-socio-economic systems, intelligent or smart systems, cloud computing, net-centricity and collective intelligence. Established disciplines that fall within the summit scope include (but not limited to) systems engineering, software engineering, information systems modelling, and data mining. {nid 39R0}
This year's Ontology Summit is titled "Ontology for Big Systems" and seeks to explore, identify and articulate how ontological methods can benefit the various disciplines required to engineer a "big system." The term "big system" is intended to cover a large scope that includes many of the terms encountered in the media such as big data, complex techno-socio-economic systems, intelligent or smart systems, cloud computing, net-centricity and collective intelligence. Established disciplines that fall within the summit scope include (but not limited to) systems engineering, software engineering, information systems modelling, and data mining. {nid 39R0}

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The principal goal of the summit was to bring together and foster collaboration between the ontology community, systems community, and stakeholders of Big Systems. Together, summit participants exchanged ideas on how ontological analysis and ontology engineering might make a difference, when applied in these "big systems." We produced recommendations describing how ontologies fit into Big Systems, as well as providing examples where ontological techniques have been, or could be applied, in domains such as: bioinformatics, electronic health records, intelligence, the smart electrical grid, manufacturing and supply chains, earth and environmental, e-science, cyber-physical systems and e-government. As is traditional with the Ontology Summit series, the results will be captured in the form of a communique, with expanded supporting material provided on the web. {nid 39R1}
The principal goal of the summit was to bring together and foster collaboration between the ontology community, systems community, and stakeholders of Big Systems. Together, summit participants exchanged ideas on how ontological analysis and ontology engineering might make a difference, when applied in these "big systems." We produced recommendations describing how ontologies fit into Big Systems, as well as providing examples where ontological techniques have been, or could be applied, in domains such as: bioinformatics, electronic health records, intelligence, the smart electrical grid, manufacturing and supply chains, earth and environmental, e-science, cyber-physical systems and e-government. As is traditional with the Ontology Summit series, the results will be captured in the form of a communique, with expanded supporting material provided on the web. {nid 39R1}

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Here’s a surprising fact: we use ontologies all the time. In fact, we’re all unwitting ontologists. The mental models we use to interact with our world, are a type of highly internalized, implicit ontology. Our mental models serve to organize and exploit the assumptions we hold about the world - the things that exist in it and how they’re related to one another. {nid 39R2}
Here’s a surprising fact: we use ontologies all the time. In fact, we’re all unwitting ontologists. The mental models we use to interact with our world, are a type of highly internalized, implicit ontology. Our mental models serve to organize and exploit the assumptions we hold about the world - the things that exist in it and how they’re related to one another. {nid 39R2}

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Pondering the nature of being, while interesting, is not a priority for most people. Sure, we all answer “What exists” everyday, but we do so pragmatically, often intuitively and almost always implicitly. Our personal ontologies are so internalized that we are rarely aware that we use them. {nid 39R5}
Pondering the nature of being, while interesting, is not a priority for most people. Sure, we all answer “What exists” everyday, but we do so pragmatically, often intuitively and almost always implicitly. Our personal ontologies are so internalized that we are rarely aware that we use them. {nid 39R5}

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Ontologies are also embedded in many of our everyday objects and systems. The forks we use are designed based on assumptions about human mouths, hands and the types of foods we eat. A transit system is designed according to assumptions about population density, growth, usage and rates. The objects and systems that pervade our lives carry with them an imprint of the beliefs of their designers. {nid 39R6}
Ontologies are also embedded in many of our everyday objects and systems. The forks we use are designed based on assumptions about human mouths, hands and the types of foods we eat. A transit system is designed according to assumptions about population density, growth, usage and rates. The objects and systems that pervade our lives carry with them an imprint of the beliefs of their designers. {nid 39R6}

Changed: 44c44

Ontology engineering arose as an answer to a “problem” in computing. When humans interact with one another, we rely on a large body of assumed, shared belief about the context, including what kinds of things are in it and how they interact. The fact that we are humans, already means that we share a vast body of broadly similar experiences and knowledge. When we interact with computers that lack this knowledge, they do or conclude things we find bizarre. But we can't, in every interaction, spend time identifying and formulating the contextual background knowledge the computer needs in order understand what we say, do, or represent as data. {nid 39R7}
Ontology engineering arose as an answer to a “problem” in computing. When humans interact with one another, we rely on a large body of assumed, shared belief about the context, including what kinds of things are in it and how they interact. The fact that we are humans, already means that we share a vast body of broadly similar experiences and knowledge. When we interact with computers that lack this knowledge, they do or conclude things we find bizarre. But we can't, in every interaction, spend time identifying and formulating the contextual background knowledge the computer needs in order understand what we say, do, or represent as data. {nid 39R7}

Changed: 56,57c56,57

* An ontology defines the terms used to describe and represent an area of knowledge (subject matter). {nid 39RD}
* An ontology also is the model (set of concepts) for the meaning of those terms. {nid 39RE}
* An ontology defines the terms used to describe and represent an area of knowledge (subject matter). {nid 39RD}
* An ontology also is the model (set of concepts) for the meaning of those terms. {nid 39RE}

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=== Designing Big System s=== {nid 39CQ}
=== Designing Big Systems {nid 39RQ} ===

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=== Integrating System s=== {nid 39CU}
=== Integrating Systems {nid 39RR} ===