Name
Abstract | ||
|---|---|---|
Current context-aware systems often model rigid inference rules for limited user contexts, which constrain their effectiveness in real world usage. In order to achieve flexible context-aware inference, a commonsense knowledge base is essential. But such knowledge base is hard to construct manually. This paper proposes some automatic algorithms to extract commonsense directly from text corpuses. We evaluate the extraction algorithms with comparison with human annotators. We also evaluate the effectiveness of the knowledge base in practical context-awareness inference. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICCA.2013.6565173 | ICCA |
Keywords | Field | DocType |
context-awareness,knowledge based systems,inference mechanisms,context-awareness inference,inference rules,commonsense knowledge,knowledge extraction,ubiquitous computing,knowledge modeling,commonsense knowledge base,context-aware systems,computational modeling,semantics,context modeling | Commonsense knowledge,Computer science,Inference,Commonsense reasoning,Model-based reasoning,Knowledge-based systems,Artificial intelligence,Inference engine,Knowledge base,Machine learning,Open Knowledge Base Connectivity | Conference |
Volume | Issue | ISSN |
null | null | 1948-3449 |
ISBN | Citations | PageRank |
978-1-4673-4707-5 | 0 | 0.34 |
References | Authors | |
17 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Zhang | 1 | 141 | 20.37 |
| Shijian Li | 2 | 1155 | 69.34 |
| Gang Pan | 3 | 1501 | 123.57 |