Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Inferring hierarchical descriptions
Proceedings of the eleventh international conference on Information and knowledge management
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
Off to new shores: conceptual knowledge discovery and processing
International Journal of Human-Computer Studies
Building and maintaining ontologies: a set of algorithms
Data & Knowledge Engineering - NLDB2002
Inheritance processing and conflicts in structural generalization hierarchies
ACM Computing Surveys (CSUR)
Automatic Fuzzy Ontology Generation for Semantic Web
IEEE Transactions on Knowledge and Data Engineering
Text retrieval with more realistic concept matching and reinforcement learning
Information Processing and Management: an International Journal
Using Bayesian decision for ontology mapping
Web Semantics: Science, Services and Agents on the World Wide Web
Fast factorization by similarity in formal concept analysis of data with fuzzy attributes
Journal of Computer and System Sciences
Journal of Management Information Systems
Interpreting TF-IDF term weights as making relevance decisions
ACM Transactions on Information Systems (TOIS)
Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research
Formal concept analysis in information science
Annual Review of Information Science and Technology
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Knowledge structure identify the way how people think and provides a macro view of human perception. However, the usability of knowledge is limited due to its structural inconsistency and complexity which makes it difficult to communicate and share. Without knowledge transferring, individuals and organizations are not capable to achieve better performance by learning and communication from others. Previous researches exhibit several disadvantages, such as multiple inheritance and lacking hierarchical features, in state-of-the- art techniques. To tackle this critical matter, we propose a new tree-based knowledge structure for achieving knowledge interoperability and enhancing structural comprehensiveness. According to our evaluation results; the proposed method successfully formulates the hierarchical relations from human cognition and increases the complexity in knowledge navigation and visualization.