Capturing meaning: Toward an abstract Wikipedia
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ISWC, 2018, page 4. (2018)

Semantic Web languages allow to express ontologies and knowledge bases in a way meant to be particularly amenable to the Web. Ontologies formalize the shared understanding of a domain. But the most expressive and widespread languages that we know of are human natural languages, and the largest knowledge base we have is the wealth of text written in human languages. This paper looks for a path to bridge the gap between knowledge representation languages such as OWL and human natural languages such as English. We propose a project to simultaneously expose that gap, allow to collaborate on closing it, make progress widely visible, and is highly attractive and valuable in its own right: a Wikipedia written in an abstract language to be rendered into any natural language on request. This would make current Wikipedia editors about 100x more productive, and increase the content of Wikipedia by 10x. For billions of users this will unlock knowledge they currently do not have access to.
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