http://www.cnr.it/ontology/cnr/individuo/prodotto/ID294982
A scalable approach for efficiently generating structured dataset topic profiles (Contributo in atti di convegno)
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- A scalable approach for efficiently generating structured dataset topic profiles (Contributo in atti di convegno) (literal)
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- 2014-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1007/978-3-319-07443-6_35 (literal)
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Fetahu, Besnik; Dietze, Stefan; Pereira Nunes, Bernardo; Casanova, Marco Antônio; Taibi, Davide; Nejdl, Wolfgang (2014)
A scalable approach for efficiently generating structured dataset topic profiles
in 11th European Semantic Web Conference ESWC 2014, Anissaras, Crete, Greece, May 25th - 29th, 2014
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- Fetahu, Besnik; Dietze, Stefan; Pereira Nunes, Bernardo; Casanova, Marco Antônio; Taibi, Davide; Nejdl, Wolfgang (literal)
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- Universitat Hannover; Pontificia Universidade Catolica do Rio de Janeiro; Consiglio Nazionale delle Ricerche (literal)
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- A scalable approach for efficiently generating structured dataset topic profiles (literal)
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- Abstract
- The increasing adoption of Linked Data principles has led to an abundance of datasets on the Web. However, take-up and reuse is hindered by the lack of descriptive information about the nature of the data, such as their topic coverage, dynamics or evolution. To address this issue, we propose an approach for creating linked dataset profiles. A profile consists of structured dataset metadata describing topics and their relevance. Profiles are generated through the configuration of techniques for resource sampling from datasets, topic extraction from reference datasets and their ranking based on graphical models. To enable a good trade-off between scalability and accuracy of generated profiles, appropriate parameters are determined experimentally. Our evaluation considers topic profiles for all accessible datasets from the Linked Open Data cloud. The results show that our approach generates accurate profiles even with comparably small sample sizes (10%) and outperforms established topic modelling approaches. (literal)
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