Beyond time

Dynamic context-aware entity recommendation

authored by: Nam Khanh Tran, Tuan Tran, Claudia Niederée
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.
Organisation(s): L3S Research Centre
Type: Conference contribution
Pages: 353-368
No. of pages: 16
Publication date: 16.05.2017
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Theoretical Computer Science, General Computer Science
Electronic version(s): https://doi.org/10.1007/978-3-319-58068-5_22 (Access: Closed)

BibTeX

@inproceedings{fa98540dd83b45a0b792521b1ad359d7,
title = "Beyond time: Dynamic context-aware entity recommendation",
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.",
keywords = "Contextual entity relatedness, Entity recommendation",
author = "Tran, {Nam Khanh} and Tuan Tran and Claudia Nieder{\'e}e",
note = "Funding information: This work was partially funded by the German Federal Ministry of Education and Research (BMBF) for the project eLabour (01UG1512C).; 14th Extended Semantic Web Conference, ESWC 2017 ; Conference date: 28-05-2017 Through 01-06-2017",
year = "2017",
month = may,
day = "16",
doi = "10.1007/978-3-319-58068-5_22",
language = "English",
isbn = "9783319580678",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "353--368",
editor = "Diana Maynard and Aldo Gangemi and Rinke Hoekstra and Eva Blomqvist and Olaf Hartig and Pascal Hitzler",
booktitle = "The Semantic Web",
address = "Germany",
}