Name
Abstract | ||
|---|---|---|
In this paper, we investigate whether semantic relationships between entities can be learnt from analyzing microblog posts published on Twitter. We identify semantic links between persons, products, events and other entities. We develop a relation discovery framework that allows for the detection of typed relations that moreover may have temporal dynamics. Based on a large Twitter dataset, we evaluate different strategies and show that co-occurrence based strategies allow for high precision and perform particularly well for relations between persons and events achieving precisions of more than 80%. We further analyze the performance in learning relationships that are valid only for a certain time period and reveal that for those types of relationships Twitter is a suitable source as it allows for discovering trending topics with higher accuracy and with lower delay in time than traditional news media. |
Year | Venue | Keywords |
|---|---|---|
2011 | ICWE | semantic link,certain time period,different strategy,relationships twitter,large twitter dataset,microblog post,lower delay,high precision,semantic relationship,higher accuracy,social web |
Field | DocType | Volume |
Data science,Data mining,World Wide Web,Social media,Social web,Relation discovery,Computer science,Microblogging,News media | Conference | 6757 |
ISSN | Citations | PageRank |
0302-9743 | 10 | 0.64 |
References | Authors | |
14 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ilknur Celik | 1 | 75 | 5.90 |
| Fabian Abel | 2 | 1187 | 62.22 |
| Geert-jan Houben | 3 | 2547 | 209.67 |