Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
Sound and Complete Forward and backward Chainingd of Graph Rules
ICCS '96 Proceedings of the 4th International Conference on Conceptual Structures: Knowledge Representation as Interlingua
Piece Resolution: Towards Larger Perspectives
ICCS '98 Proceedings of the 6th International Conference on Conceptual Structures: Theory, Tools and Applications
Knowledge Representation and Reasonings Based on Graph Homomorphism
ICCS '00 Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic, and Computational Issues
Extensions of simple conceptual graphs: the complexity of rules and constraints
Journal of Artificial Intelligence Research
A Datatype Extension for Simple Conceptual Graphs and Conceptual Graphs Rules
ICCS '07 Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications
Conceptual Graph Rules and Equivalent Rules: A Synthesis
ICCS '09 Proceedings of the 17th International Conference on Conceptual Structures: Conceptual Structures: Leveraging Semantic Technologies
On rules with existential variables: Walking the decidability line
Artificial Intelligence
Towards farsighted dependencies for existential rules
RR'11 Proceedings of the 5th international conference on Web reasoning and rule systems
| Hi-index | 0.00 |
Conceptual Graphs Rules were proposed as an extension of Simple Conceptual Graphs (CGs) to represent knowledge of form “ifAthenB”, where A and B are simple CGs. Optimizations of the deduction calculus in this KR formalism include a Backward Chaining that unifies at the same time whole subgraphs of a rule, and a Forward Chaining that relies on compiling dependencies between rules. In this paper, we show that the unification used in the first algorithm is exactly the operation required to compute dependencies in the second one. We also combine the benefits of the two approaches, by using the graph of rules dependencies in a Backward Chaining framework.