Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Implementing a semantic interpreter using conceptual graphs
IBM Journal of Research and Development
Conceptual graphs for the analysis and generation of sentences
IBM Journal of Research and Development
Natural language understanding (2nd ed.)
Natural language understanding (2nd ed.)
Guest Editors‘ Introduction: Machine Learning and Natural Language
Machine Learning - Special issue on natural language learning
Assembly of Conceptual Graphs from Natural Language by Means of Multiple Knowledge Specialists
Proceedings of the 7th Annual Workshop on Conceptual Structures: Theory and Implementation
A CG-Based Behavior Extraction System
ICCS '99 Proceedings of the 7th International Conference on Conceptual Structures: Standards and Practices
From a children's first dictionary to a lexical knowledge base of conceptual graphs
From a children's first dictionary to a lexical knowledge base of conceptual graphs
Using decision trees for conference resolution
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Conceptual Graph Matching for Semantic Search
ICCS '02 Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces
PANTO: A Portable Natural Language Interface to Ontologies
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
A Robust Ontology-Based Method for Translating Natural Language Queries to Conceptual Graphs
ASWC '08 Proceedings of the 3rd Asian Semantic Web Conference on The Semantic Web
Ontology-Based Natural Query Retrieval Using Conceptual Graphs
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Conceptual Graph Interchange Format for Mining Financial Statements
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Assessing Semantic Quality of Web Directory Structure
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Ontology-based administration of web directories
Transactions on computational collective intelligence I
Ontology-based understanding of natural language queries using nested conceptual graphs
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
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
On the need to bootstrap ontology learning with extraction grammar learning
ICCS'05 Proceedings of the 13th international conference on Conceptual Structures: common Semantics for Sharing Knowledge
Detection of unknown malicious script code using a conceptual graph and SVM
Proceedings of the 2012 ACM Research in Applied Computation Symposium
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Automatically generating Conceptual Graphs (CGs) [1] from natural language sentences is a difficult task in using CG as a semantic (knowledge) representation language for natural language information source. However, up to now only few approaches have been proposed for this task and most of them either are highly dependent on one domain or use manual rules. In this paper, we propose a machine-learning based approach that can be trained for different domains and requires almost no manual rules. We adopt a dependency grammar -- Link Grammar [2] -- for this purpose. The link structures of the grammar are very similar to conceptual graphs. Based on the link structure, through the word-conceptualization, concept-folding, link-folding and relationalization operations, we can train the system to generate conceptual graphs from domain specific sentences. An implementation system of the method is currently under development with IBM China Research Lab.