A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Construction of the L-fuzzy concept lattice
Fuzzy Sets and Systems
Learning by discovering concept hierarchies
Artificial Intelligence
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Automatic Ontology-Based Knowledge Extraction from Web Documents
IEEE Intelligent Systems
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
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Knowledge-Based Systems
Expert Systems with Applications: An International Journal
A flexible framework to experiment with ontology learning techniques
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International Journal of Approximate Reasoning
Relations of attribute reduction between object and property oriented concept lattices
Knowledge-Based Systems
Formal concept analysis via multi-adjoint concept lattices
Fuzzy Sets and Systems
A new model of evaluating concept similarity
Knowledge-Based Systems
FCA-MERGE: bottom-up merging of ontologies
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Attribute reduction in fuzzy concept lattices based on the T implication
Knowledge-Based Systems
Approaches to attribute reduction in concept lattices induced by axialities
Knowledge-Based Systems
Relation between concept lattice reduction and rough set reduction
Knowledge-Based Systems
Ontology-based concept similarity in Formal Concept Analysis
Information Sciences: an International Journal
Conceptual representing of documents and query expansion based on ontology
WISM'12 Proceedings of the 2012 international conference on Web Information Systems and Mining
A bottom-up algorithm of vertical assembling concept lattices
International Journal of Data Mining and Bioinformatics
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This paper introduces concept lattice and granular computing into ontology learning, and presents a unified research model for ontology building, ontology merging and ontology connection based on the domain ontology base in different granulations. In this model, as the knowledge in the lowest and most basic level, the domain ontology base is presented firstly, which provides a uniform technology for ontology learning on the whole; secondly, in order to better understand problems rather than be overwhelmed unnecessary details, granular computing is introduced to abstract and simplify domain ontology bases in complex domains. Moreover, the similarly of concepts in different granulations is introduced to help domain experts judging relations except for inheritance relation, and the similarity of ontologies in multi-granulations is introduced to measure the degree of connection of ontologies; finally, based on similarity models mentioned above, the ontology building, ontology merging and ontology connection can be obtained in different granulations with the help of domain experts. It is shown by instances that the application of the model presented in this paper is valid and practicable. Although there are still some problems in applications of this model (for example, ontology learning cannot dispense with the intervention of domain experts yet), this paper offers a new way for combining ontology learning and concept lattice.