Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | The Semantic Web |
| Untertitel | Trends and Challenges - 11th International Conference, ESWC 2014, Proceedings |
| Herausgeber (Verlag) | Springer Verlag |
| Seiten | 519-534 |
| Seitenumfang | 16 |
| ISBN (Print) | 9783319074429 |
| Publikationsstatus | Veröffentlicht - 2014 |
| Veranstaltung | 11th International Conference on Semantic Web: Trends and Challenges, ESWC 2014 - Anissaras, Crete, Griechenland Dauer: 25 Mai 2014 → 29 Mai 2014 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 8465 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
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.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
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The Semantic Web: Trends and Challenges - 11th International Conference, ESWC 2014, Proceedings. Springer Verlag, 2014. S. 519-534 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 8465 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - A Scalable Approach for Efficiently Generating Structured Dataset Topic Profiles
AU - Fetahu, Besnik
AU - Dietze, Stefan
AU - Pereira Nunes, Bernardo
AU - Antonio Casanova, Marco
AU - Taibi, Davide
AU - Nejdl, Wolfgang
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Linked Data
KW - Metadata
KW - Profiling
KW - Vocabulary of Links
UR - http://www.scopus.com/inward/record.url?scp=84902602805&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-07443-6_35
DO - 10.1007/978-3-319-07443-6_35
M3 - Conference contribution
AN - SCOPUS:84902602805
SN - 9783319074429
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 519
EP - 534
BT - The Semantic Web
PB - Springer Verlag
T2 - 11th International Conference on Semantic Web: Trends and Challenges, ESWC 2014
Y2 - 25 May 2014 through 29 May 2014
ER -