Name
Abstract | ||
|---|---|---|
This paper describes an advanced news analytics and exploration system that allows users to visualize trends of entities like politicians, countries, and organizations in continuously updated news articles. Our system improves state-of-the-art text analytics by linking ambiguous names in news articles to entities in knowledge bases like Freebase, DBpedia or YAGO. This step enables indexing entities and interpreting the contents in terms of entities. This way, the analysis of trends and co-occurrences of entities gains accuracy, and by leveraging the taxonomic type hierarchy of knowledge bases, also in expressiveness and usability. In particular, we can analyze not only individual entities, but also categories of entities and their combinations, including co-occurrences with informative text phrases. Our Web-based system demonstrates the power of this approach by insightful anecdotic analysis of recent events in the news. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2661829.2661835 | CIKM |
Keywords | Field | DocType |
category analytics,entity analytics,entity search,information search and retrieval | Data science,Data mining,World Wide Web,Information retrieval,Computer science,Usability,Search engine indexing,Semantic analytics,News analytics,Analytics,Hierarchy,Expressivity | Conference |
Citations | PageRank | References |
6 | 0.50 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Johannes Hoffart | 1 | 1362 | 52.62 |
| Dragan Milchevski | 2 | 62 | 4.26 |
| Gerhard Weikum | 3 | 12710 | 2146.01 |