Name
Title | ||
|---|---|---|
A Statistical Approach to Incremental Induction of First-Order Hierarchical Knowledge Bases |
Abstract | ||
|---|---|---|
Knowledge bases play an important role in many forms of artificial intelligence research. A simple approach to producing such knowledge is as a database of ground literals. However, this method is neither compact nor computationally tractable for learning or performance systems to use. In this paper, we present a statistical method for incremental learning of a hierarchically structured, first-order knowledge base. Our approach uses both rules and ground facts to construct succinct rules that generalize the ground literals. We demonstrate that our approach is computationally efficient and scales well to domains with many relations. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-85928-4_22 | ILP |
Keywords | Field | DocType |
statistical approach,first-order knowledge base,simple approach,computationally tractable,artificial intelligence research,important role,knowledge base,ground fact,incremental learning,first-order hierarchical knowledge bases,incremental induction,ground literal,statistical method,first order,artificial intelligent | Inductive logic programming,Horn clause,First order,Computer science,Incremental learning,Knowledge-based systems,Theoretical computer science,Artificial intelligence,Knowledge base,Machine learning | Conference |
Volume | ISSN | Citations |
5194 | 0302-9743 | 1 |
PageRank | References | Authors |
0.38 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David J. Stracuzzi | 1 | 91 | 25.68 |
| Tolga Könik | 2 | 86 | 8.21 |