Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Information retrieval
Conceptual graph matching: a flexible algorithm and experiments
Journal of Experimental & Theoretical Artificial Intelligence - Special issue: conceptual graphs workshop
Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
CGMA: A Novel Conceptual Graph Matching Algorithm
Proceedings of the 7th Annual Workshop on Conceptual Structures: Theory and Implementation
Exploiting the Induced Order on Type-Labeled Graphs for Fast Knowledge Retrieval
ICCS '94 Proceedings of the Second International Conference on Conceptual Structures: Current Practices
An Experiment in Document Retrieval Using Conceptual Graphs
ICCS '97 Proceedings of the Fifth International Conference on Conceptual Structures: Fulfilling Peirce's Dream
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Comparison of Conceptual Graphs
MICAI '00 Proceedings of the Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Information Retrieval with Conceptual Graph Matching
DEXA '00 Proceedings of the 11th International Conference on Database and Expert Systems Applications
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Text Mining at Detail Level Using Conceptual Graphs
ICCS '02 Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces
Address extraction: extraction of location-based information from the web
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Textual entailment beyond semantic similarity information
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
A generalised similarity measure for question answering
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
A method for efficient malicious code detection based on conceptual similarity
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part IV
Interactive knowledge validation in CBR for decision support in medicine
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Integrating relation and keyword matching in information retrieval
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
A context-based framework and method for learning object description and search
ICWL'07 Proceedings of the 6th international conference on Advances in web based learning
A novel event network matching algorithm
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
International Journal of Information System Modeling and Design
Cross-Language plagiarism detection using a multilingual semantic network
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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Conceptual graphs allow for powerful and computationally affordable representation of the semantic contents of natural language texts. We propose a method of comparison (approximate matching) of conceptual graphs. The method takes into account synonymy and subtype/supertype relationships between the concepts and relations used in the conceptual graphs, thus allowing for greater flexibility of approximate matching. The method also allows the user to choose the desirable aspect of similarity in the cases when the two graphs can be generalized in different ways. The algorithm and examples of its application are presented. The results are potentially useful in a range of tasks requiring approximate semantic or another structural matching - among them, information retrieval and text mining.