Difference between revisions of "Data-centric knowledge"
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=Data-Centric Knowledge under the context of [[MU]]= | =Data-Centric Knowledge under the context of [[MU]]= | ||
Knowledge is | Knowledge is represented as a special kind of data based on raw data and computed from priorly established information content under a unifying context of [[MU]] data operations. Every piece of knowledge needs to go through the following stages to be given a representable handle for ongoing integration of knowledge content: | ||
# Grounding Raw Data: This data set is collected from widely deployed user terminals or certified data sensors that should always be annotated with timestamps and spatial tags that explicitly specify who, when and where the data are being collected. These raw data content, especially the timestamps and location/account that provided the data will be used as a reference to determine the authenticity of data. | # Grounding Raw Data: This data set is collected from widely deployed user terminals or certified data sensors that should always be annotated with timestamps and spatial tags that explicitly specify who, when and where the data are being collected. These raw data content, especially the timestamps and location/account that provided the data will be used as a reference to determine the authenticity of data. | ||
# Inferred information: The ordering and prioritization of information content is filtered by previously mentioned raw data. This information filtering procedure is conducted by a set of computational inference tools, whose source code are version-controlled based on [[MU]]-compliant rules. Computational procedures specified using Neural network, Bayesian Belief Networks, System Dynamic models, and other data-intensive inference mechanisms will have their training data set as part of the version-controlled data content. | # Inferred information: The ordering and prioritization of information content is filtered by previously mentioned raw data. This information filtering procedure is conducted by a set of computational inference tools, whose source code are version-controlled based on [[MU]]-compliant rules. Computational procedures specified using Neural network, Bayesian Belief Networks, System Dynamic models, and other data-intensive inference mechanisms will have their training data set as part of the version-controlled data content. | ||
# Action of Acknowledgement: | # Action of Acknowledgement: Knowledge is represented as a set of causal relations that are written into actionable or executable programs/contracts. An action of acknowledgment can be automatically triggered by verified raw data and programmatically computed information content, including semi-automatically acknowledged by human-in-the-loop authorization of action. The event of acknowledgment can be represented as a piece of authenticated data that possesses pragmatic value, such as a token of appreciation, honor badges, or cash payment. It is the data on the event-of-acknowledgement that we register and represent knowledge content in [[MU]]. | ||
=Related Pages= | =Related Pages= | ||
Revision as of 10:33, 18 February 2022
Data-centric knowledge is a formalized mapping of concepts to data points. Its universal applicability is based on the representability assumption of Kan Extension. Kan extension states that all concepts and idealized knowledge are representable through functors from a domain of complex data types to uniquely identifiable data entries in set-theoretic format. This means that knowledge of any kinds can all be stored or represented using concrete data points stored in databases.
Data-Centric Knowledge under the context of MU
Knowledge is represented as a special kind of data based on raw data and computed from priorly established information content under a unifying context of MU data operations. Every piece of knowledge needs to go through the following stages to be given a representable handle for ongoing integration of knowledge content:
- Grounding Raw Data: This data set is collected from widely deployed user terminals or certified data sensors that should always be annotated with timestamps and spatial tags that explicitly specify who, when and where the data are being collected. These raw data content, especially the timestamps and location/account that provided the data will be used as a reference to determine the authenticity of data.
- Inferred information: The ordering and prioritization of information content is filtered by previously mentioned raw data. This information filtering procedure is conducted by a set of computational inference tools, whose source code are version-controlled based on MU-compliant rules. Computational procedures specified using Neural network, Bayesian Belief Networks, System Dynamic models, and other data-intensive inference mechanisms will have their training data set as part of the version-controlled data content.
- Action of Acknowledgement: Knowledge is represented as a set of causal relations that are written into actionable or executable programs/contracts. An action of acknowledgment can be automatically triggered by verified raw data and programmatically computed information content, including semi-automatically acknowledged by human-in-the-loop authorization of action. The event of acknowledgment can be represented as a piece of authenticated data that possesses pragmatic value, such as a token of appreciation, honor badges, or cash payment. It is the data on the event-of-acknowledgement that we register and represent knowledge content in MU.