TY - GEN

T1 - Multiple Models for Recommending Temporal Aspects of Entities

AU - Nguyen, Tu Ngoc

AU - Kanhabua, Nattiya

AU - Nejdl, Wolfgang

N1 - Funding information: This work was partially funded by the German Federal Ministry of Education and Research (BMBF) under project GlycoRec (16SV7172). Acknowledgements. This work was partially funded by the German Federal Ministry of Education and Research (BMBF) under project GlycoRec (16SV7172).

PY - 2018/6/3

Y1 - 2018/6/3

N2 - Entity aspect recommendation is an emerging task in semantic search that helps users discover serendipitous and prominent information with respect to an entity, of which salience (e.g., popularity) is the most important factor in previous work. However, entity aspects are temporally dynamic and often driven by events happening over time. For such cases, aspect suggestion based solely on salience features can give unsatisfactory results, for two reasons. First, salience is often accumulated over a long time period and does not account for recency. Second, many aspects related to an event entity are strongly time-dependent. In this paper, we study the task of temporal aspect recommendation for a given entity, which aims at recommending the most relevant aspects and takes into account time in order to improve search experience. We propose a novel event-centric ensemble ranking method that learns from multiple time and type-dependent models and dynamically trades off salience and recency characteristics. Through extensive experiments on real-world query logs, we demonstrate that our method is robust and achieves better effectiveness than competitive baselines.

AB - Entity aspect recommendation is an emerging task in semantic search that helps users discover serendipitous and prominent information with respect to an entity, of which salience (e.g., popularity) is the most important factor in previous work. However, entity aspects are temporally dynamic and often driven by events happening over time. For such cases, aspect suggestion based solely on salience features can give unsatisfactory results, for two reasons. First, salience is often accumulated over a long time period and does not account for recency. Second, many aspects related to an event entity are strongly time-dependent. In this paper, we study the task of temporal aspect recommendation for a given entity, which aims at recommending the most relevant aspects and takes into account time in order to improve search experience. We propose a novel event-centric ensemble ranking method that learns from multiple time and type-dependent models and dynamically trades off salience and recency characteristics. Through extensive experiments on real-world query logs, we demonstrate that our method is robust and achieves better effectiveness than competitive baselines.

UR - http://www.scopus.com/inward/record.url?scp=85048516806&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-93417-4_30

DO - 10.1007/978-3-319-93417-4_30

M3 - Conference contribution

AN - SCOPUS:85048516806

SN - 9783319934167

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 462

EP - 480

BT - The Semantic Web - 15th International Conference, ESWC 2018, Proceedings

A2 - Gangemi, Aldo

A2 - Troncy, Raphaël

A2 - Navigli, Roberto

A2 - Hollink, Laura

A2 - Vidal, Maria-Esther

A2 - Hitzler, Pascal

A2 - Tordai, Anna

A2 - Alam, Mehwish

PB - Springer Verlag

T2 - 15th International Conference on Extended Semantic Web Conference, ESWC 2018

Y2 - 3 June 2018 through 7 June 2018

ER -