Time-aware and corpus-specific entity relatedness

verfasst von: Nilamadhaba Mohapatra, Vasileios Iosifidis, Asif Ekbal, Stefan Dietze, Pavlos Fafalios
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.
Organisationseinheit(en): Forschungszentrum L3S
Externe Organisation(en): Indian Institute of Technology Patna (IITP)
Typ: Konferenzaufsatz in Fachzeitschrift
Journal: CEUR Workshop Proceedings
Band: 2106
Seiten: 33-39
Anzahl der Seiten: 7
ISSN: 1613-0073
Publikationsdatum: 2018
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Informatik (insg.)
Elektronische Version(en): https://ceur-ws.org/Vol-2106/paper4.pdf (Zugang: Offen)

BibTeX

@article{36922dc979f046ddb0c837ea30f08d8c,
title = "Time-aware and corpus-specific entity relatedness",
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.",
keywords = "Entity Embeddings, Entity Relatedness, Word2Vec",
author = "Nilamadhaba Mohapatra and Vasileios Iosifidis and Asif Ekbal and Stefan Dietze and Pavlos Fafalios",
note = "Funding information: The work was partially funded by the European Commission for the ERC Advanced Grant ALEXANDRIA under grant No. 339233.; 1st Workshop on Deep Learning for Knowledge Graphs and Semantic Technologies, DL4KGS 2018 ; Conference date: 04-06-2018 Through 04-06-2018",
year = "2018",
language = "English",
volume = "2106",
pages = "33--39",
journal = "CEUR Workshop Proceedings",
issn = "1613-0073",
publisher = "CEUR Workshop Proceedings",
}