Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | The Semantic Web |
| Untertitel | 14th International Conference, ESWC 2017, Proceedings |
| Herausgeber/-innen | Diana Maynard, Aldo Gangemi, Rinke Hoekstra, Eva Blomqvist, Olaf Hartig, Pascal Hitzler |
| Erscheinungsort | Cham |
| Herausgeber (Verlag) | Springer Verlag |
| Seiten | 353-368 |
| Seitenumfang | 16 |
| ISBN (elektronisch) | 978-3-319-58068-5 |
| ISBN (Print) | 9783319580678 |
| Publikationsstatus | Veröffentlicht - 16 Mai 2017 |
| Veranstaltung | 14th Extended Semantic Web Conference, ESWC 2017 - Portoroz, Slowenien Dauer: 28 Mai 2017 → 1 Juni 2017 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 10249 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
Entities and their relatedness are useful information in various tasks such as entity disambiguation, entity recommendation or search. In many cases, entity relatedness is highly affected by dynamic contexts, which can be reflected in the outcome of different applications. However, the role of context is largely unexplored in existing entity relatedness measures. In this paper, we introduce the notion of contextual entity relatedness, and show its usefulness in the new yet important problem of context-aware entity recommendation. We propose a novel method of computing the contextual relatedness with integrated time and topic models. By exploiting an entity graph and enriching it with an entity embedding method, we show that our proposed relatedness can effectively recommend entities, taking contexts into account. We conduct large-scale experiments on a real-world data set, and the results show considerable improvements of our solution over the states of the art.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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The Semantic Web: 14th International Conference, ESWC 2017, Proceedings. Hrsg. / Diana Maynard; Aldo Gangemi; Rinke Hoekstra; Eva Blomqvist; Olaf Hartig; Pascal Hitzler. Cham: Springer Verlag, 2017. S. 353-368 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10249 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Beyond time
T2 - 14th Extended Semantic Web Conference, ESWC 2017
AU - Tran, Nam Khanh
AU - Tran, Tuan
AU - Niederée, Claudia
N1 - Funding information: This work was partially funded by the German Federal Ministry of Education and Research (BMBF) for the project eLabour (01UG1512C).
PY - 2017/5/16
Y1 - 2017/5/16
N2 - Entities and their relatedness are useful information in various tasks such as entity disambiguation, entity recommendation or search. In many cases, entity relatedness is highly affected by dynamic contexts, which can be reflected in the outcome of different applications. However, the role of context is largely unexplored in existing entity relatedness measures. In this paper, we introduce the notion of contextual entity relatedness, and show its usefulness in the new yet important problem of context-aware entity recommendation. We propose a novel method of computing the contextual relatedness with integrated time and topic models. By exploiting an entity graph and enriching it with an entity embedding method, we show that our proposed relatedness can effectively recommend entities, taking contexts into account. We conduct large-scale experiments on a real-world data set, and the results show considerable improvements of our solution over the states of the art.
AB - Entities and their relatedness are useful information in various tasks such as entity disambiguation, entity recommendation or search. In many cases, entity relatedness is highly affected by dynamic contexts, which can be reflected in the outcome of different applications. However, the role of context is largely unexplored in existing entity relatedness measures. In this paper, we introduce the notion of contextual entity relatedness, and show its usefulness in the new yet important problem of context-aware entity recommendation. We propose a novel method of computing the contextual relatedness with integrated time and topic models. By exploiting an entity graph and enriching it with an entity embedding method, we show that our proposed relatedness can effectively recommend entities, taking contexts into account. We conduct large-scale experiments on a real-world data set, and the results show considerable improvements of our solution over the states of the art.
KW - Contextual entity relatedness
KW - Entity recommendation
UR - http://www.scopus.com/inward/record.url?scp=85020047153&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-58068-5_22
DO - 10.1007/978-3-319-58068-5_22
M3 - Conference contribution
AN - SCOPUS:85020047153
SN - 9783319580678
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 353
EP - 368
BT - The Semantic Web
A2 - Maynard, Diana
A2 - Gangemi, Aldo
A2 - Hoekstra, Rinke
A2 - Blomqvist, Eva
A2 - Hartig, Olaf
A2 - Hitzler, Pascal
PB - Springer Verlag
CY - Cham
Y2 - 28 May 2017 through 1 June 2017
ER -