Pathfinder associative networks: studies in knowledge organization
Pathfinder associative networks: studies in knowledge organization
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Modern Information Retrieval
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
An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources
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)
CrimeNet explorer: a framework for criminal network knowledge discovery
ACM Transactions on Information Systems (TOIS)
Automatic Fuzzy Ontology Generation for Semantic Web
IEEE Transactions on Knowledge and Data Engineering
Automatically labeling hierarchical clusters
dg.o '06 Proceedings of the 2006 international conference on Digital government research
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
Automated ontology construction for unstructured text documents
Data & Knowledge Engineering
Expert Systems with Applications: An International Journal
A new approach for constructing the concept map
Computers & Education
Fast factorization by similarity in formal concept analysis of data with fuzzy attributes
Journal of Computer and System Sciences
Journal of Management Information Systems
Mining e-Learning domain concept map from academic articles
Computers & Education
Interpreting TF-IDF term weights as making relevance decisions
ACM Transactions on Information Systems (TOIS)
A hybrid approach to semantic web services matchmaking
International Journal of Approximate Reasoning
Ontological approach to development of computing with words based systems
International Journal of Approximate Reasoning
Cluster Analysis
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
Analyzing the structure of expert knowledge
Information and Management
A fuzzy ontology and its application to news summarization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Semantic similarity estimation from multiple ontologies
Applied Intelligence
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A knowledge structure identifies how people think and displays a macro view of human perception. By discovering the hidden structural relations of knowledge, significant reasoning patterns are retrieved to enhance further knowledge sharing and distribution. However, the utilization of such approaches is apt to be limited due to the lack of hierarchical features and the problem of information overload, which make it difficult to enhance comprehension and provide effective navigation. To address these critical issues, we propose a new approach to construct a tree-based knowledge structure from corpus which can reveal the significant relations among knowledge objects and enhance user comprehension. The effectiveness of the proposed method is demonstrated with two representative public data sets. The evaluation results show that the method presented in this work achieves remarkable consistency with the domain-specific knowledge structure, and is capable of reflecting appropriate similarities among knowledge objects along with hierarchical implications in the document classification task.