Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | The Semantic Web |
| Untertitel | Semantics and Big Data - 10th International Conference, ESWC 2013, Proceedings |
| Seiten | 548-562 |
| Seitenumfang | 15 |
| Publikationsstatus | Veröffentlicht - 9 Okt. 2013 |
| Veranstaltung | 10th International Conference on The Semantic Web: Semantics and Big Data, ESWC 2013 - Montpellier, Frankreich Dauer: 26 Mai 2013 → 30 Mai 2013 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 7882 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
One key feature of the Semantic Web lies in the ability to link related Web resources. However, while relations within particular datasets are often well-defined, links between disparate datasets and corpora of Web resources are rare. The increasingly widespread use of cross-domain reference datasets, such as Freebase and DBpedia for annotating and enriching datasets as well as documents, opens up opportunities to exploit their inherent semantic relationships to align disparate Web resources. In this paper, we present a combined approach to uncover relationships between disparate entities which exploits (a) graph analysis of reference datasets together with (b) entity co-occurrence on the Web with the help of search engines. In (a), we introduce a novel approach adopted and applied from social network theory to measure the connectivity between given entities in reference datasets. The connectivity measures are used to identify connected Web resources. Finally, we present a thorough evaluation of our approach using a publicly available dataset and introduce a comparison with established measures in the field.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
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The Semantic Web: Semantics and Big Data - 10th International Conference, ESWC 2013, Proceedings. 2013. S. 548-562 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 7882 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Combining a Co-occurrence-Based and a Semantic Measure for Entity Linking
AU - Pereira Nunes, Bernardo
AU - Dietze, Stefan
AU - Casanova, Marco Antonio
AU - Kawase, Ricardo
AU - Fetahu, Besnik
AU - Nejdl, Wolfgang
PY - 2013/10/9
Y1 - 2013/10/9
N2 - One key feature of the Semantic Web lies in the ability to link related Web resources. However, while relations within particular datasets are often well-defined, links between disparate datasets and corpora of Web resources are rare. The increasingly widespread use of cross-domain reference datasets, such as Freebase and DBpedia for annotating and enriching datasets as well as documents, opens up opportunities to exploit their inherent semantic relationships to align disparate Web resources. In this paper, we present a combined approach to uncover relationships between disparate entities which exploits (a) graph analysis of reference datasets together with (b) entity co-occurrence on the Web with the help of search engines. In (a), we introduce a novel approach adopted and applied from social network theory to measure the connectivity between given entities in reference datasets. The connectivity measures are used to identify connected Web resources. Finally, we present a thorough evaluation of our approach using a publicly available dataset and introduce a comparison with established measures in the field.
AB - One key feature of the Semantic Web lies in the ability to link related Web resources. However, while relations within particular datasets are often well-defined, links between disparate datasets and corpora of Web resources are rare. The increasingly widespread use of cross-domain reference datasets, such as Freebase and DBpedia for annotating and enriching datasets as well as documents, opens up opportunities to exploit their inherent semantic relationships to align disparate Web resources. In this paper, we present a combined approach to uncover relationships between disparate entities which exploits (a) graph analysis of reference datasets together with (b) entity co-occurrence on the Web with the help of search engines. In (a), we introduce a novel approach adopted and applied from social network theory to measure the connectivity between given entities in reference datasets. The connectivity measures are used to identify connected Web resources. Finally, we present a thorough evaluation of our approach using a publicly available dataset and introduce a comparison with established measures in the field.
KW - co-occurrence-based measure
KW - data integration
KW - link detection
KW - linked data
KW - semantic associations
KW - Semantic connectivity
UR - http://www.scopus.com/inward/record.url?scp=84884994039&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-38288-8-37
DO - 10.1007/978-3-642-38288-8-37
M3 - Conference contribution
AN - SCOPUS:84884994039
SN - 9783642382871
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 548
EP - 562
BT - The Semantic Web
T2 - 10th International Conference on The Semantic Web: Semantics and Big Data, ESWC 2013
Y2 - 26 May 2013 through 30 May 2013
ER -