| Full text | |
| Author(s): Show less - |
do Carmo, Paulo
;
Marcacini, Ricardo
;
Chen, Y
;
Ludwig, H
;
Tu, Y
;
Fayyad, U
;
Zhu, X
;
Hu, X
;
Byna, S
;
Liu, X
;
Zhang, J
;
Pan, S
;
Papalexakis, V
;
Wang, J
;
Cuzzocrea, A
;
Ordonez, C
Total Authors: 16
|
| Document type: | Journal article |
| Source: | 2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA); v. N/A, p. 10-pg., 2021-01-01. |
| Abstract | |
Events can be defined as phenomena that occur at a specific time and place. Social networks and news portals publish thousands of events daily, and this knowledge is beneficial for many social, political, and economic studies. Recently, heterogeneous networks have been used successfully for modeling large event datasets since they model different event components as nodes (e.g., events, actors, locations, people, and organizations), and network links express different relationships between these nodes. However, event analysis from heterogeneous networks is a research challenge due to several factors: (1) event nodes are usually associated with textual (unstructured) and high dimensional data; and (2) inapplicability of several machine learning methods that assume input data represented by independent vectors in a vector space. In this paper, we present a language model-based embedding propagation method for heterogeneous event networks. While most of the existing network embedding methods mainly explore the network's topology, our method maps both (i) textual information about events and (ii) the complex relationships between events and their components to a low dimensional vector space in order to use several machine learning algorithms, such as clustering and classification. We carried out an extensive experimental evaluation involving link prediction tasks in heterogeneous event networks, such as event forecasting, prediction of event locations, and event actors. Our approach proved competitive compared to the state-of-the-art network embedding methods for link prediction tasks in different real-world event datasets, in addition to allowing dynamic and incremental updating of the embeddings as new events arise. (AU) | |
| FAPESP's process: | 19/25010-5 - Semantically enriched representations for Portuguese textmining: models and applications |
| Grantee: | Solange Oliveira Rezende |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 19/07665-4 - Center for Artificial Intelligence |
| Grantee: | Fabio Gagliardi Cozman |
| Support Opportunities: | Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program |