# Transforming Wikipedia into an Ontology-based Information Retrieval Search Engine for Local Experts using a Third-Party Taxonomy

arXiv: https://arxiv.org/abs/1511.01259 · HTML: https://mlsanity.com/paper/1511.01259/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2015.

## Built on by (most cited first, 1 of 1)

- [Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems](https://mlsanity.com/paper/2112.04368/) (2021)

## Abstract (arXiv)

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 endpoint as each Wikipedia page is accessed. An additional tab on the Wikipedia page allows the user to open up a list of teams of local experts associated with the subject matter in the Wikipedia page. The technique, though presented here as a way to identify local experts, is generic, in that any third party taxonomy, can be used in this to connect Wikipedia to any non-Wikipedia data source.