Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Workshop on Deep Learning for Knowledge Graphs and Semantic Technologies 2018 |
| Untertitel | Proceedings of the First Workshop on Deep Learning for Knowledge Graphs and Semantic Technologies (DL4KGS) co-located with the 15th Extended Semantic Web Conerence (ESWC 2018) |
| Seiten | 33-39 |
| Seitenumfang | 7 |
| Publikationsstatus | Veröffentlicht - 2018 |
| Veranstaltung | 1st Workshop on Deep Learning for Knowledge Graphs and Semantic Technologies, DL4KGS 2018 - Heraklion, Crete, Griechenland Dauer: 4 Juni 2018 → 4 Juni 2018 |
Publikationsreihe
| Name | CEUR Workshop Proceedings |
|---|---|
| Herausgeber (Verlag) | CEUR Workshop Proceedings |
| Band | 2106 |
| ISSN (Print) | 1613-0073 |
Abstract
Entity relatedness has emerged as an important feature in a plethora of applications such as information retrieval, entity recommendation and entity linking. Given an entity, for instance a person or an organization, entity relatedness measures can be exploited for generating a list of highly-related entities. However, the relation of an entity to some other entity depends on several factors, with time and context being two of the most important ones (where, in our case, context is determined by a particular corpus). For example, the entities related to the International Monetary Fund are different now compared to some years ago, while these entities also may highly differ in the context of a USA news portal compared to a Greek news portal. In this paper, we propose a simple but exible model for entity relatedness which considers time and entity aware word embeddings by exploiting the underlying corpus. The proposed model does not require external knowledge and is language independent, which makes it widely useful in a variety of applications.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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- RIS
Workshop on Deep Learning for Knowledge Graphs and Semantic Technologies 2018: Proceedings of the First Workshop on Deep Learning for Knowledge Graphs and Semantic Technologies (DL4KGS) co-located with the 15th Extended Semantic Web Conerence (ESWC 2018). 2018. S. 33-39 (CEUR Workshop Proceedings; Band 2106).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Time-aware and corpus-specific entity relatedness
AU - Mohapatra, Nilamadhaba
AU - Iosifidis, Vasileios
AU - Ekbal, Asif
AU - Dietze, Stefan
AU - Fafalios, Pavlos
N1 - Funding information: The work was partially funded by the European Commission for the ERC Advanced Grant ALEXANDRIA under grant No. 339233.
PY - 2018
Y1 - 2018
N2 - Entity relatedness has emerged as an important feature in a plethora of applications such as information retrieval, entity recommendation and entity linking. Given an entity, for instance a person or an organization, entity relatedness measures can be exploited for generating a list of highly-related entities. However, the relation of an entity to some other entity depends on several factors, with time and context being two of the most important ones (where, in our case, context is determined by a particular corpus). For example, the entities related to the International Monetary Fund are different now compared to some years ago, while these entities also may highly differ in the context of a USA news portal compared to a Greek news portal. In this paper, we propose a simple but exible model for entity relatedness which considers time and entity aware word embeddings by exploiting the underlying corpus. The proposed model does not require external knowledge and is language independent, which makes it widely useful in a variety of applications.
AB - Entity relatedness has emerged as an important feature in a plethora of applications such as information retrieval, entity recommendation and entity linking. Given an entity, for instance a person or an organization, entity relatedness measures can be exploited for generating a list of highly-related entities. However, the relation of an entity to some other entity depends on several factors, with time and context being two of the most important ones (where, in our case, context is determined by a particular corpus). For example, the entities related to the International Monetary Fund are different now compared to some years ago, while these entities also may highly differ in the context of a USA news portal compared to a Greek news portal. In this paper, we propose a simple but exible model for entity relatedness which considers time and entity aware word embeddings by exploiting the underlying corpus. The proposed model does not require external knowledge and is language independent, which makes it widely useful in a variety of applications.
KW - Entity Embeddings
KW - Entity Relatedness
KW - Word2Vec
UR - http://www.scopus.com/inward/record.url?scp=85048329919&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85048329919
T3 - CEUR Workshop Proceedings
SP - 33
EP - 39
BT - Workshop on Deep Learning for Knowledge Graphs and Semantic Technologies 2018
T2 - 1st Workshop on Deep Learning for Knowledge Graphs and Semantic Technologies, DL4KGS 2018
Y2 - 4 June 2018 through 4 June 2018
ER -