Name
Abstract | ||
|---|---|---|
Recent technological advances have led to unprecedented amounts of generated data that originate from the Web, sensor networks, and social media. Analytics in terms of defeasible reasoning - for example, for decision making - could provide richer knowledge of the underlying domain. Traditionally, defeasible reasoning has focused on complex knowledge structures over small to medium amounts of data, but recent research efforts have attempted to parallelize the reasoning process over theories with large numbers of facts. Such work has shown that traditional defeasible logics come with overheads that limit scalability. In this work, we design a new logic for defeasible reasoning, thus ensuring scalability by design. We establish several properties of the logic, including its relation to existing defeasible logics. Our experimental results indicate that our approach is indeed scalable and defeasible reasoning can be applied to billions of facts. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1017/S1471068420000010 | THEORY AND PRACTICE OF LOGIC PROGRAMMING |
Keywords | DocType | Volume |
defeasible reasoning,parallel peasoning,scalability | Journal | 20 |
Issue | ISSN | Citations |
4 | 1471-0684 | 0 |
PageRank | References | Authors |
0.34 | 31 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael J. Maher | 1 | 3073 | 373.09 |
| Ilias Tachmazidis | 2 | 48 | 11.44 |
| Grigoris Antoniou | 3 | 2401 | 190.28 |
| Stephen Wade | 4 | 0 | 0.34 |
| Long Cheng | 5 | 91 | 16.99 |