MANUFACTURING community of practice: Difference between revisions
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[http://ichs.ucsf.edu/open-knowledge-network/ Open Knowledge Networks] (OKN) | [http://ichs.ucsf.edu/open-knowledge-network/ Open Knowledge Networks] (OKN) | ||
:What is the state-of-the-art for open knowledge networks in | :What is the state-of-the-art for open knowledge networks in manufacturing? | ||
:What are some driving research questions that will benefit from | :What are some driving research questions that will benefit from manufacturing OKN? | ||
:What are some driving commercial or consumer questions that will benefit from | :What are some driving commercial or consumer questions that will benefit from manufacturing OKN | ||
:What are the gaps, why do they exist, and how do we address them? | :What are the gaps, why do they exist, and how do we address them? | ||
:How is | :How is manufacturing different from other practices (biomedical, health, GEO, finance, self-driving vehicles, etc)? | ||
::The manufacturing industry uses sophisticated CAD software, but the model-based design paradigm has not been extended to other areas such as materials, supply chain management, product life cycle, and so foth. | ::The manufacturing industry uses sophisticated CAD software, but the model-based design paradigm has not been extended to other areas such as materials, supply chain management, product life cycle, and so foth. | ||
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:What do we share with other domains? How can we benefit from this synergy? | :What do we share with other domains? How can we benefit from this synergy? | ||
''' | '''Manufacturing Community of Practice''' | ||
:[http://ontology.buffalo.edu/smith Barry Smith], [http://ncor.us National Center for Ontological Research] | :[http://ontology.buffalo.edu/smith Barry Smith], [http://ncor.us National Center for Ontological Research] | ||
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:[https://www.energy.gov/eere/amo/articles/welcome-dr-sudarsan-rachuri Ruchari Sudarsan], DOE | :[https://www.energy.gov/eere/amo/articles/welcome-dr-sudarsan-rachuri Ruchari Sudarsan], DOE | ||
''' | '''Related initiatives in the manufacturing domain''' | ||
The [http://ncorwiki.buffalo.edu/index.php/Industry_Ontology_Foundry:_ASME_Workshop_2017 Industry Ontologies Foundry] | The [http://ncorwiki.buffalo.edu/index.php/Industry_Ontology_Foundry:_ASME_Workshop_2017 Industry Ontologies Foundry] | ||
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'''Use cases''' | '''Use cases''' | ||
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'''Examples of questions the OKN methods might be able to answer''' | '''Examples of questions the OKN methods might be able to answer''' | ||
:One goal is to develop automatically short reports / wikipedia article specific to the question being queried | :One goal is to develop automatically short reports / wikipedia article specific to the question being queried | ||
'''What open data already exist?''' | '''What open data already exist?''' | ||
:What are the questions that are being asked to the data? How is the answer currently discovered? Which datasets are consulted to find the answer? | :What are the questions that are being asked to the data? How is the answer currently discovered? Which datasets are consulted to find the answer? | ||
Revision as of 14:21, 1 October 2017
Open Knowledge Networks (OKN)
- What is the state-of-the-art for open knowledge networks in manufacturing?
- What are some driving research questions that will benefit from manufacturing OKN?
- What are some driving commercial or consumer questions that will benefit from manufacturing OKN
- What are the gaps, why do they exist, and how do we address them?
- How is manufacturing different from other practices (biomedical, health, GEO, finance, self-driving vehicles, etc)?
- The manufacturing industry uses sophisticated CAD software, but the model-based design paradigm has not been extended to other areas such as materials, supply chain management, product life cycle, and so foth.
- What do we share with other domains? How can we benefit from this synergy?
Manufacturing Community of Practice
- John Milinovich, Pinterest
- William Regli, DARPA
- Ram Sriram, NIST
- Ruchari Sudarsan, DOE
Related initiatives in the manufacturing domain
The Industry Ontologies Foundry
Use cases
Use cases span the following broad areas:
- Manufacturing Capabilities (of companies, of manufacturing equipment, of sensors, ...)
- Use case: classification of suppliers, screening to select suitable suppliers (risk mitigation in supply-chain management -- for example when accepted bidder might drop out)
- In progress: scraping information on the webpages of manufacturing companies and mapping terms identified to ontologies to enable reasoning (Farhad Ameri, Collaborative agreement between NIST and Texas State)
- Can we create wikipedia-like pages for each company from this activity?
- Manufacturing Readiness Levels (MRL) of interest also to DOD
- Manufacturing Processes
- Manufactured Products
- So far what exists are primarily NLP-based attempts to identify emerging trends in customer needs or markets for example from the study of Amazon reviews of products
- Standard for the Exchange of Product Model Data (STEP)
- Can we convert this activity into an ontology-based OKN?
- Materials
- On-going AFRL work (Clare Paul, Wright-Patt) to create a MatOnto, a large materials science ontology growing out of the Materials Genome Initiative
- Workforce development
- (from OKN Finance CoP) Creating many economic datasets based on social media, e.g., “i lost my job” to approximate something about the labor market.
- Patents
- Use case: to enable enhanced patent search resolving terminological inconsistencies
- Focus on the patent system
- Retrieval of patent information
- Comparison of International Patent Classification (IPC) with MeSH
- Robots
- Probably not enough data in the public domain to enable a useful OKN for robot use in manufacturing at this stage
Examples of questions the OKN methods might be able to answer
- One goal is to develop automatically short reports / wikipedia article specific to the question being queried
What open data already exist?
- What are the questions that are being asked to the data? How is the answer currently discovered? Which datasets are consulted to find the answer?
