Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Big Data Analytics and Knowledge Discovery |
| Untertitel | 26th International Conference, DaWaK 2024, Proceedings |
| Herausgeber/-innen | Robert Wrembel, Silvia Chiusano, Gabriele Kotsis, Ismail Khalil, A Min Tjoa |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 388-394 |
| Seitenumfang | 7 |
| ISBN (Print) | 9783031683220 |
| Publikationsstatus | Veröffentlicht - 18 Aug. 2024 |
| Veranstaltung | 26th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2024 - Naples, Italien Dauer: 26 Aug. 2024 → 28 Aug. 2024 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 14912 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
Wikidata, a community-maintained knowledge graph (KG), may integrate different entities and properties with the same meaning. Contributors can add new properties similar in meaning to other properties in the KG. Detecting relationships among these properties plays a crucial role in interoperability and downstream tasks. We tackle the problem of discovering relationships among properties regarding their instances in Wikidata. Although our approach is knowledge graph-agnostic, it can be applied to any KGs. Our approach resorts to Class-based Relationship Discovery (CRD) to capture the most important characteristics of the properties. We evaluate our approach over Wikidata and show the benefits of exploiting statements annotated with qualifiers, references, and ranks. We empirically study the distribution and frequency of relationships among predicates in six domains to provide evidence that KGs enclose relationships that define the same real-world properties.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Big Data Analytics and Knowledge Discovery : 26th International Conference, DaWaK 2024, Proceedings. Hrsg. / Robert Wrembel; Silvia Chiusano; Gabriele Kotsis; Ismail Khalil; A Min Tjoa. Springer Science and Business Media Deutschland GmbH, 2024. S. 388-394 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 14912 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Discovering Relationships Among Properties in Wikidata Knowledge Graph
AU - Niazmand, Emetis
AU - Vidal, Maria Esther
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/8/18
Y1 - 2024/8/18
N2 - Wikidata, a community-maintained knowledge graph (KG), may integrate different entities and properties with the same meaning. Contributors can add new properties similar in meaning to other properties in the KG. Detecting relationships among these properties plays a crucial role in interoperability and downstream tasks. We tackle the problem of discovering relationships among properties regarding their instances in Wikidata. Although our approach is knowledge graph-agnostic, it can be applied to any KGs. Our approach resorts to Class-based Relationship Discovery (CRD) to capture the most important characteristics of the properties. We evaluate our approach over Wikidata and show the benefits of exploiting statements annotated with qualifiers, references, and ranks. We empirically study the distribution and frequency of relationships among predicates in six domains to provide evidence that KGs enclose relationships that define the same real-world properties.
AB - Wikidata, a community-maintained knowledge graph (KG), may integrate different entities and properties with the same meaning. Contributors can add new properties similar in meaning to other properties in the KG. Detecting relationships among these properties plays a crucial role in interoperability and downstream tasks. We tackle the problem of discovering relationships among properties regarding their instances in Wikidata. Although our approach is knowledge graph-agnostic, it can be applied to any KGs. Our approach resorts to Class-based Relationship Discovery (CRD) to capture the most important characteristics of the properties. We evaluate our approach over Wikidata and show the benefits of exploiting statements annotated with qualifiers, references, and ranks. We empirically study the distribution and frequency of relationships among predicates in six domains to provide evidence that KGs enclose relationships that define the same real-world properties.
KW - Knowledge Discovery
KW - Knowledge Graphs
KW - Wikidata
UR - http://www.scopus.com/inward/record.url?scp=85202161713&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-68323-7_35
DO - 10.1007/978-3-031-68323-7_35
M3 - Conference contribution
AN - SCOPUS:85202161713
SN - 9783031683220
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 388
EP - 394
BT - Big Data Analytics and Knowledge Discovery
A2 - Wrembel, Robert
A2 - Chiusano, Silvia
A2 - Kotsis, Gabriele
A2 - Khalil, Ismail
A2 - Tjoa, A Min
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2024
Y2 - 26 August 2024 through 28 August 2024
ER -