A survey of approaches for ranking on the web of data

Roa-Valverde, Antonio J.; Sicilia, Miguel–Ángel

doi:10.1007/s10791-014-9240-0

Cited by 29 publications

(15 citation statements)

References 47 publications

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“…For further references that focus on ranking strategies for degree-dependent datasets, such as DBpedia or DBLP, we refer the reader (Bast et al 2016;Roa-Valverde et al 2014;Yumusak et al 2014;Marx et al 2016b). We continue our discussion with some alternative strategies that do not highly depend on node degree.…”

Section: Importance Measures For Structured Databasesmentioning

confidence: 99%

Novel Node Importance Measures to Improve Keyword Search over RDF Graphs

Menendez

Casanova

Leme

2019

Lecture Notes in Computer Science

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Section: Importance Measures For Structured Databasesmentioning

confidence: 99%

Novel Node Importance Measures to Improve Keyword Search over RDF Graphs

Menendez

Casanova

Leme

2019

Lecture Notes in Computer Science

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“…We touch on two fields in this work, namely ranking for RDF knowledge bases and entity summarization. For a good survey on ranking for RDF knowledge bases we refer the reader to Roa-Valverde and Sicilia [12]. Recent work on this topic includes Ngomo et al [10] which gives an alternative to traditional PageRank computation.…”

Section: Related Workmentioning

confidence: 99%

PageRank and Generic Entity Summarization for RDF Knowledge Bases

Diefenbach

Thalhammer

2018

The Semantic Web

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Ranking and entity summarization are operations that are tightly connected and recurrent in many different domains. Possible application fields include information retrieval, question answering, named entity disambiguation, co-reference resolution, and natural language generation. Still, the use of these techniques is limited because there are few accessible resources. PageRank computations are resource-intensive and entity summarization is a complex research field in itself. We present two generic and highly re-usable resources for RDF knowledge bases: a component for PageRank-based ranking and a component for entity summarization. The two components, namely PageRankRDF and summaServer, are provided in form of open source code along with example datasets and deployments. In addition, this work outlines the application of the components for PageRank-based RDF ranking and entity summarization in the question answering project WDAqua.

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“…In addition, relevance feedback (RF) is one of the classical ways of refining search engine rankings: search engine first generates an initial set of rankings; then users select the relevant documents within this ranking, and based on the information in these documents, a more appropriate ranking is presented (e.g., the query may be expanded using the terms contained in the first set of relevant documents). In [77], the problem of ranking linked data is discussed, giving a comprehensive overview of existing ranking methods for the Web of data.…”

Section: Ir Models and Weighting Schemesmentioning

confidence: 99%