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| NSF | | NSF | ||
| [https://www.nsf.gov/pubs/ | | [https://www.nsf.gov/pubs/2019/nsf19589/nsf19589.htm: Information Integration and Informatics (III)] under CISE | ||
| | | October 29, 2020 - November 12, 2020 for SMALL projects; September 7, 2020 - September 14, 2020 for MEDIUM projects | ||
| "The III program supports innovative research on computational methods for the full data lifecycle, from collection through archiving and knowledge discovery, to maximize the utility of information resources to science and engineering and broadly to society. III projects range from formal theoretical research to those that advance data-intensive applications of scientific, engineering or societal importance. Research areas within III include: | | "The III program supports innovative research on computational methods for the full data lifecycle, from collection through archiving and knowledge discovery, to maximize the utility of information resources to science and engineering and broadly to society. III projects range from formal theoretical research to those that advance data-intensive applications of scientific, engineering or societal importance. Research areas within III include: | ||
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* Visualizations of this data | * Visualizations of this data | ||
Wikibase is a novel infrastructural platform for data management suitable for data from many domains. This is the first application built on Wikibase tailored to the needs of the epidemiological community. The output of this research will be a knowledge graph of structured data in the form of a Wikibase instance populated with data from heterogeneous food composition tables. | |||
Multiple data visualization options are available via the Query Service of our Wikibase instance | Multiple data visualization options are available via the Query Service of our Wikibase instance. Graphs, charts, network diagrams, and maps are some of the visualizations we will be able to offer end-users of this knowledge base. | ||
* Case Study One: Fermented foods | * Case Study One: Fermented foods | ||
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This project will support multilingual data, reducing barriers to data reuse for speakers of many languages beyond English. Users will be able to query using any of the supported human languages, and see results in the language of their choice. Through the reuse of data from Wikidata, a multilingual knowledge base, we will add common names as well as scientific names for foods items and plant and animal species in as many human languages as possible. | This project will support multilingual data, reducing barriers to data reuse for speakers of many languages beyond English. Users will be able to query using any of the supported human languages, and see results in the language of their choice. Through the reuse of data from Wikidata, a multilingual knowledge base, we will add common names as well as scientific names for foods items and plant and animal species in as many human languages as possible. | ||
In many FCTs food items are identified with a single label. Our approach supports searching across multiple aliases for a single resource. This broadens search options so that lookups are not constrained to a single search term | In many FCTs food items are identified with a single label. Our approach supports searching across multiple aliases for a single resource. This broadens search options so that lookups are not constrained to a single search term. | ||
Our choice to use Wikibase allows us to access the data serialized as RDF. The SPARQL endpoint we have created allows us to ask questions of this data that previously were not possible to ask. For example, we can now ask questions such as "show me all recipes that call for one or more ingredients containing proanthocyanidins". | Our choice to use Wikibase allows us to access the data serialized as RDF. The SPARQL endpoint we have created allows us to ask questions of this data that previously were not possible to ask. For example, we can now ask questions such as "show me all recipes that call for one or more ingredients containing proanthocyanidins". | ||
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The Wikibase infrastructure supports both human and algorithmic curation. Thus we can programmatically ingest data from external sources and also support crowdsourced recipes from anyone with access to the internet. The World Wide Web Consortium (W3C) published the following definition of the Semantic Web in 2009. "Semantic Web is the idea of having data on the Web defined and linked in a way that it | The Wikibase infrastructure supports both human and algorithmic curation. Thus we can programmatically ingest data from external sources and also support crowdsourced recipes from anyone with access to the internet. The World Wide Web Consortium (W3C) published the following definition of the Semantic Web in 2009. "Semantic Web is the idea of having data on the Web defined and linked in a way that it | ||
can be used by machines not just for display purposes, but for automation, integration, and | can be used by machines not just for display purposes, but for automation, integration, and | ||
reuse of data across various applications.” (W3C Semantic Web Activity, 2009). The Wikidata knowledge base fulfills the requirements outlined by the W3C in that each resource has a unique identifier, is liked to other resources by properties | reuse of data across various applications.” (W3C Semantic Web Activity, 2009). | ||
The Wikidata knowledge base fulfills the requirements outlined by the W3C in that each resource has a unique identifier, is liked to other resources by properties and that all of the data is machine actionable as well as editable by both humans and machines. | |||
Our decision to build this knowledge base using the infrastructure of the Wikimedia Foundation means that other researchers will be able to access this data for reuse in their own projects in a variety of formats. Results from our SPARQL endpoint are available for download as JSON, TSV, CSV and HTML. Preformatted code snipits for making requests to our SPARQL endpoint are available in PHP, jQuery, JavaScript, Java, Perl, Python, Ruby, R and Matlab. These options allow researchers to more quickly integrate data from our knowledge base into their existing projects using the tools of their choice. | Our decision to build this knowledge base using the infrastructure of the Wikimedia Foundation means that other researchers will be able to access this data for reuse in their own projects in a variety of formats. Results from our SPARQL endpoint are available for download as JSON, TSV, CSV and HTML. Preformatted code snipits for making requests to our SPARQL endpoint are available in PHP, jQuery, JavaScript, Java, Perl, Python, Ruby, R and Matlab. These options allow researchers to more quickly integrate data from our knowledge base into their existing projects using the tools of their choice. |