Name
Title | ||
|---|---|---|
Spatio-Temporal Contextualization of Queries for Microtexts in Social Media: Mathematical Modeling. |
Abstract | ||
|---|---|---|
In this paper, we present our ongoing project on query contextualization by integrating all possible IoT-based data sources. Most importantly, mobile users are regarded as the IoT sensors which can be the textual data sources with spatio-temporal contexts. Given a large amount of text streams, it has been difficult for the traditional information retrieval systems to conduct the searching tasks. The goal of this work is i) to understand and process microtexts in social media (e.g., Twitter and Facebook), and ii) to reformulate the queries for searching for relevant microtexts in these social media. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.procs.2017.08.317 | Procedia Computer Science |
Keywords | Field | DocType |
Query contextualization,Spatio-temporal contexts,Information fusion | Data mining,World Wide Web,Social media,Computer science,Internet of Things,Information fusion,Contextualization | Conference |
Volume | ISSN | Citations |
113 | 1877-0509 | 0 |
PageRank | References | Authors |
0.34 | 7 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jae Hong Park | 1 | 19 | 2.98 |
| O.-Joun Lee | 2 | 36 | 7.98 |
| Joo-Man Han | 3 | 0 | 0.34 |
| Eon-Ji Lee | 4 | 0 | 0.34 |
| Jason J. Jung | 5 | 1451 | 135.51 |
| Luca Carratore | 6 | 0 | 0.34 |
| Francesco Piccialli | 7 | 400 | 44.41 |