%0 Conference Paper %1 Tran2017 %A Tran, Nam Khanh %A Tran, Tuan %A Niederée, Claudia %B The Semantic Web: 14th International Conference, ESWC 2017, Portoroz, Slovenia, May 28 -- June 1, 2017, Proceedings, Part I %C Cham %D 2017 %E Blomqvist, Eva %E Maynard, Diana %E Gangemi, Aldo %E Hoekstra, Rinke %E Hitzler, Pascal %E Hartig, Olaf %I Springer International Publishing %K myown %P 353--368 %R 10.1007/978-3-319-58068-5_22 %T Beyond Time: Dynamic Context-Aware Entity Recommendation %U https://doi.org/10.1007/978-3-319-58068-5_22 %X 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. %@ 978-3-319-58068-5

@inproceedings{Tran2017, 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.}, added-at = {2017-12-11T17:33:42.000+0100}, address = {Cham}, author = {Tran, Nam Khanh and Tran, Tuan and Nieder{\'e}e, Claudia}, biburl = {https://www.bibsonomy.org/bibtex/2446b010b59cd07fe0ee896dbd8e3e5ec/ntran}, booktitle = {The Semantic Web: 14th International Conference, ESWC 2017, Portoro{\v{z}}, Slovenia, May 28 -- June 1, 2017, Proceedings, Part I}, description = {Beyond Time: Dynamic Context-Aware Entity Recommendation | SpringerLink}, doi = {10.1007/978-3-319-58068-5_22}, editor = {Blomqvist, Eva and Maynard, Diana and Gangemi, Aldo and Hoekstra, Rinke and Hitzler, Pascal and Hartig, Olaf}, interhash = {3f481c6cd9a44dad20c56a3ccb7b32f6}, intrahash = {446b010b59cd07fe0ee896dbd8e3e5ec}, isbn = {978-3-319-58068-5}, keywords = {myown}, pages = {353--368}, publisher = {Springer International Publishing}, timestamp = {2017-12-11T17:33:42.000+0100}, title = {Beyond Time: Dynamic Context-Aware Entity Recommendation}, url = {https://doi.org/10.1007/978-3-319-58068-5_22}, year = 2017 }