Ordonnancement d'entités pour la rencontre du web des documents et du web des données
This work addresses the need for better entity ranking to enable new applications bridging structured and unstructured web data, though it is incremental as it builds on existing entity detection services.
The paper tackles the problem of ranking entities detected in web pages to combine the web of documents and web of data, proposing a query-biased algorithm that combines link analysis with dimensionality reduction and achieves competitive results compared to state-of-the-art methods on a crowdsourced dataset.
The advances of the Linked Open Data (LOD) initiative are giving rise to a more structured web of data. Indeed, a few datasets act as hubs (e.g., DBpedia) connecting many other datasets. They also made possible new web services for entity detection inside plain text (e.g., DBpedia Spotlight), thus allowing for new applications that will benefit from a combination of the web of documents and the web of data. To ease the emergence of these new use-cases, we propose a query-biased algorithm for the ranking of entities detected inside a web page. Our algorithm combine link analysis with dimensionality reduction. We use crowdsourcing for building a publicly available and reusable dataset on which we compare our algorithm to the state of the art. Finally, we use this algorithm for the construction of semantic snippets for which we evaluate the usability and the usefulness with a crowdsourcing-based approach.