Recommendation of Text Tags in Social Applications Using Linked Data

Calì, Andrea; Capuzzi, Stefano; Dimartino, Mirko Michele; Frosini, Riccardo

doi:10.1007/978-3-319-04244-2_17

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…• Semantic reasoning. These techniques are based on rules to infer logical consequences from a set of asserted facts or axioms (Calì, Capuzzi, Dimartino, & Frosini, 2013;Cantador, Castells, & Bellogín, 2011;Ozdikis, Orhan, & Danismaz, 2011). Some approaches combine reasoning with context-awareness (Karpus, Vagliano, & Goczyła, 2017;Karpus, Vagliano, Goczyła, & Morisio, 2016).…”

Section: Othersmentioning

confidence: 99%

Semantic Web Science and Real-World Applications

Figueroa¹,

Vagliano

Rocha

et al. 2019

Advances in Web Technologies and Engineering

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Data published on the Web following the Linked Data principles has resulted in a global data space called the Web of Data. These principles led to semantically interlink and connect different resources at data level regardless their structure, authoring, location, etc. The tremendous and continuous growth of the Web of Data also implies that now it is more likely to find resources that describe real-life concepts. However, discovering and recommending relevant related resources is still an open research area. This chapter studies recommender systems that use Linked Data as a source containing a significant amount of available resources and their relationships useful to produce recommendations. Furthermore, it also presents a framework to deploy and execute state-of-the-art algorithms for Linked Data that have been re-implemented to measure and benchmark them in different application domains and without being bound to a unique dataset.

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Section: Othersmentioning

confidence: 99%

Semantic Web Science and Real-World Applications

Figueroa¹,

Vagliano

Rocha

et al. 2019

Advances in Web Technologies and Engineering

View full text Add to dashboard Cite

show abstract

Executing, Comparing, and Reusing Linked-Data-Based Recommendation Algorithms With the Allied Framework

Figueroa

Vagliano

Rocha

et al. 2019

Semantic Web Science and Real-World Applications

View full text Add to dashboard Cite

Data published on the web following the principles of linked data has resulted in a global data space called the Web of Data. These principles led to semantically interlink and connect different resources at data level regardless their structure, authoring, location, etc. The tremendous and continuous growth of the Web of Data also implies that now it is more likely to find resources that describe real-life concepts. However, discovering and recommending relevant related resources is still an open research area. This chapter studies recommender systems that use linked data as a source containing a significant amount of available resources and their relationships useful to produce recommendations. Furthermore, it also presents a framework to deploy and execute state-of-the-art algorithms for linked data that have been re-implemented to measure and benchmark them in different application domains and without being bound to a unique dataset.

show abstract

Recommendation of Text Tags in Social Applications Using Linked Data

Cited by 2 publications

References 8 publications

Semantic Web Science and Real-World Applications

Semantic Web Science and Real-World Applications

Executing, Comparing, and Reusing Linked-Data-Based Recommendation Algorithms With the Allied Framework

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