Ranking Entities in the Age of Two Webs, an Application to Semantic Snippets

TL;DR

This paper introduces LDRANK, a query-biased ranking algorithm for Web of Data resources that combines link analysis and dimensionality reduction, improving entity ranking and enabling more useful semantic snippets.

Contribution

The paper presents LDRANK, a novel algorithm that outperforms existing methods in ranking Web of Data resources using a combined link analysis and dimensionality reduction approach.

Findings

LDRANK outperforms state-of-the-art ranking algorithms.

A crowdsourced dataset was created for evaluation.

Semantic snippets generated using LDRANK are more useful according to user assessments.

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 can benefit from a combination of the Web of documents and the Web of data. To ease the emergence of these new applications, we propose a query-biased algorithm (LDRANK) for the ranking of web of data resources with associated textual data. Our algorithm combines link analysis with dimensionality reduction. We use crowdsourcing for building a publicly available and reusable dataset for the evaluation of query-biased ranking of Web of data resources detected in Web pages. We show that, on this dataset, LDRANK outperforms the state of the art. Finally, we use this algorithm for the construction of semantic snippets of which we evaluate the usefulness with a crowdsourcing-based approach.