Transforming Wikipedia into an Ontology-based Information Retrieval Search Engine for Local Experts using a Third-Party Taxonomy
Gregory Grefenstette (TAO), Karima Rafes (TAO, LRI)

TL;DR
This paper presents a method to connect Wikipedia content with local expert information using a third-party taxonomy, enabling users to find local experts related to Wikipedia topics without modifying Wikipedia itself.
Contribution
It introduces a novel approach to link Wikipedia with local data sources via a third-party taxonomy and a Wikipedia plugin, enhancing local information retrieval.
Findings
Implemented a relational database linking Wikipedia pages and local expert reports.
Developed a Wikipedia gadget to access local expert data through a SPARQL endpoint.
Demonstrated the method with a case study on finding local scientific experts.
Abstract
Wikipedia is widely used for finding general information about a wide variety of topics. Its vocation is not to provide local information. For example, it provides plot, cast, and production information about a given movie, but not showing times in your local movie theatre. Here we describe how we can connect local information to Wikipedia, without altering its content. The case study we present involves finding local scientific experts. Using a third-party taxonomy, independent from Wikipedia's category hierarchy, we index information connected to our local experts, present in their activity reports, and we re-index Wikipedia content using the same taxonomy. The connections between Wikipedia pages and local expert reports are stored in a relational database, accessible through as public SPARQL endpoint. A Wikipedia gadget (or plugin) activated by the interested user, accesses the…
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Taxonomy
TopicsWikis in Education and Collaboration · Expert finding and Q&A systems · Information Retrieval and Search Behavior
