Name
Abstract | ||
|---|---|---|
When searching in a document collection by keywords, good auto-completion suggestions can be derived from query logs and corpus statistics. On the other hand, when querying documents which have automatically been linked to entities and semantic categories, auto-completion has not been investigated much. We have developed a semantic auto-completion system, where suggestions for entities and categories are computed in real-time from the context of already entered entities or categories and from entity-level co-occurrence statistics for the underlying corpus. Given the huge size of the knowledge bases that underlie this setting, a challenge is to compute the best suggestions fast enough for interactive user experience. Our demonstration shows the effectiveness of our method, and its interactive usability. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2911451.2911461 | SIGIR |
Field | DocType | Citations |
Data mining,World Wide Web,User experience design,Information retrieval,Computer science,Usability | Conference | 5 |
PageRank | References | Authors |
0.55 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Schmidt | 1 | 5 | 0.89 |
| Johannes Hoffart | 2 | 1362 | 52.62 |
| Dragan Milchevski | 3 | 62 | 4.26 |
| Gerhard Weikum | 4 | 12710 | 2146.01 |