Ordonnancement d'entit\'es pour la rencontre du web des documents et du web des donn\'ees

TL;DR

This paper introduces a query-biased ranking algorithm for entities detected in web pages, combining link analysis and dimensionality reduction, validated through crowdsourcing datasets and usability studies.

Contribution

It presents a novel entity ranking algorithm that integrates link analysis with dimensionality reduction, tailored for web page entity detection, and evaluates it with crowdsourced data.

Findings

The algorithm outperforms existing methods in entity ranking accuracy.

Crowdsourcing datasets effectively evaluate entity ranking and snippet usefulness.

Semantic snippets generated improve user experience and information retrieval.

Abstract

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.