Ranking Entities in the Age of Two Webs, an Application to Semantic Snippets
This work addresses the challenge of integrating structured and unstructured web data for improved entity ranking, with incremental contributions to semantic web applications.
The authors tackled the problem of ranking entities from the Web of data for applications like semantic snippets, proposing the LDRANK algorithm which outperforms state-of-the-art methods on a new 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 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.