Editing Mika/Temp/WikiFCD/Grants (section)

=Impact=
This project will provide a new FCD knowledge graph that will support queries across multiple FCDs with a single search. This will reduce the time that epidemiologists, nutritionists and other researchers spend searching for food composition data. This knowledge graph will support federated queries with Wikidata and other public SPARQL endpoints that will allow researchers to ask questions of this data in combination with other linked open datasets. The data in the knowledge graph is structured data. Due to the fact that many of these tables were published as PDFs, getting the data into a more readily accessible structured format increases ease of reuse. 

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. These aliases serve several disambiguation functions. They allow the use of common names as well as scientific names and they allow multilingual indexing. They also allow us to store historic names, whether scientific or common, that are no longer used, but may be found in the literature or in historical sources. For example, this is the record for Jugulans regia in our knowledge graph [https://wikifcd.wiki.opencura.com/wiki/Item:Q82650]. In addition to the species name we also support the aliases 'common walnut', 'Old World Walnut','Walnut, 'Persian Walnut' and 'Juglans fallax' for this item. A more extensive example is Vaccinium vitis-idaea [https://wikifcd.wiki.opencura.com/wiki/Item:Q117098], for which we provide 13 aliases beyond the species name. 

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".   

We will connect scientific publications about the nutritional components of foods with the food items. This is possible because of the existence of roughly 50,000,000 scientific publications in Wikidata. Many of the publications in PubMed are already represented in Wikidata, thus our domain is adequately represented. We will create new Wikidata items for publications we would like to reference if they do not yet exist. Connecting publications with food items in our knowledge graph will allow us to provide additional evidence for researchers to reuse, investigate, and extend.

The knowledge graph approach allows us to combine food composition data and recipes in the same database, which will enable us to create novel user interfaces for people interested in the nutritional components of home-cooked dishes.

The knowledge graph approach also facilitates expansion of this project into related domains. We could look at food chemistry and metabolic processes by combining this with subsets of Wikidata. We could combine this data with research literature about health benefits of plant-derived medicines and extend our data models to include plant components that have been tested for medicinal efficacy. 

The ability to federate SPARQL queries between our Wikibase and Wikidata allows us to combine our data with resources from the media repository of the Wikimedia Foundation, Wikimedia Commons. The ability to quickly locate images, videos and sound files related to the resources in our Wikibase allows us to provide interactive multi-media interactions in applications we build on top of our Wikibase. Wikimedia Commons has images of many of the taxa of which our food items are products. 

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
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.