Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Warren's abstract machine: a tutorial reconstruction
Warren's abstract machine: a tutorial reconstruction
Characterization and Algorithmic Recognition of Canonical Conceptual Graphs
ICCS '93 Proceedings on Conceptual Graphs for Knowledge Representation
UDS: A Universal Data Structure
ICCS '94 Proceedings of the Second International Conference on Conceptual Structures: Current Practices
A self-organizing retrieval system for graphs (organic, machine, chemistry, learning, partial-ordering)
Extracting ontological concepts for tendering conceptual structures
Data & Knowledge Engineering
Declarative Programs with Implicit Implications
IEEE Transactions on Knowledge and Data Engineering
Using BWW model to evaluate building ontologies in CGs formalism
Information Systems
Ontologies and reasoning techniques for (legal) intelligent information retrieval systems
Artificial Intelligence and Law
Conceptual Modelling (and Problem Solving Techniques) for Complex Narrative Events
Proceedings of the 2006 conference on Information Modelling and Knowledge Bases XVII
Integrating the two main inference modes of NKRL, transformations and hypotheses
Journal on Data Semantics IV
Advanced computational reasoning based on the NKRL conceptual model
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
This paper addresses problems in conceptual graph implementation: subsumption and classification in a taxonomy. Conceptual graphs are typically stored using a directed acyclic graph data structure based on the partial order over conceptual graphs.We give an improved algorithm for classifying conceptual graphs into this hierarchy. It prunes the search space in the database using the information gathered while searching.We show how conceptual graphs in this hierarchy can be compiled into instructions which represent specialized cases of the canonical formation rules. This compiles subsumption of conceptual graphs and compresses knowledge in a knowledge base. Conceptual graphs are compiled as differences between adjacent graphs in the hierarchy. The differences represent the rules used in deriving the graph from the adjacent graphs. We illustrate how the method compresses knowledge bases in some experiments.Compilation is effected in three ways: removal of redundant data, use of simple instructions which ignore redundant checks when performing matching, and by sharing common processing between graphs.