Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Efficient Subgraph Isomorphism Detection: A Decomposition Approach
IEEE Transactions on Knowledge and Data Engineering
Polynomial Algorithms for Projection and Matching
Proceedings of the 7th Annual Workshop on Conceptual Structures: Theory and Implementation
Projection and Unification for Conceptual Graphs
ICCS '95 Proceedings of the Third International Conference on Conceptual Structures: Applications, Implementation and Theory
Notio - A Java API for Developing CG Tools
ICCS '99 Proceedings of the 7th International Conference on Conceptual Structures: Standards and Practices
Reasoning and Unification over Conceptual Graphs
Reasoning and Unification over Conceptual Graphs
On querying simple conceptual graphs with negation
Data & Knowledge Engineering
The effect of data structures modifications on algorithms for reasoning operations using a conceptual graphs knowledge base
Extensions of simple conceptual graphs: the complexity of rules and constraints
Journal of Artificial Intelligence Research
Enhancing the initial requirements capture of multi-agent systems through conceptual graphs
ICCS'05 Proceedings of the 13th international conference on Conceptual Structures: common Semantics for Sharing Knowledge
Arc Consistency Projection: A New Generalization Relation for Graphs
ICCS '07 Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications
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Knowledge representation (KR) is used to store and retrieve meaningful data. This data is saved using dynamic data structures that are suitable for the style of KR being implemented. The KR allows the system to manipulate the knowledge in the data by using reasoning operations. The data structure, together with the contents of the transformed knowledge, is known as the knowledge base (KB). An algorithm and the associated data structures make up the reasoning operation, and the performance of this operation is dependent on the KB.In this paper, the basic reasoning operation for a query-answer system, projection, is explored using different theoretical algorithms. Within this discussion, the associated algorithms will be using different KBs for their Conceptual Graph (CG) knowledge representation. The basic projection algorithm defined using the CG representation is looking for a graph morphism of a query graph onto a graph from the KB.The overall running time for the projection operation is known to be a NP class problem; however, by modifying the algorithm, taking into account the associated KB, the actual time needed for discovering and creating the projection/s can be improved. In fact, a new projection algorithm will be defined that, given a typical query onto a carefully defined KB, presents a running time for the actual projection that only grows with the number of projections present.