Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
Understanding semantic relationships
The VLDB Journal — The International Journal on Very Large Data Bases
What Are Ontologies, and Why Do We Need Them?
IEEE Intelligent Systems
Classifying the Semantics of Relationships in Conceptual Modeling by Categorization of Roles
NLDB'01 Proceedings of the 6th International Workshop on Applications of Natural Language to Information Systems
Query Expansion by Mining User Logs
IEEE Transactions on Knowledge and Data Engineering
Complex semantic web ontology mapping
Web Intelligence and Agent Systems
Comparing Relationships in Conceptual Modeling: Mapping to Semantic Classifications
IEEE Transactions on Knowledge and Data Engineering
An information retrieval approach to ontology mapping
Data & Knowledge Engineering - Special issue: Application of natural language to information systems (NLDB04)
A fuzzy clustering approach for finding similar documents using a novel similarity measure
Expert Systems with Applications: An International Journal
Corpus-based semantic role approach in information retrieval
Data & Knowledge Engineering
Integration of association rules and ontologies for semantic query expansion
Data & Knowledge Engineering
Integration of association rules and ontologies for semantic query expansion
Data & Knowledge Engineering
TextOntoEx: Automatic ontology construction from natural English text
Expert Systems with Applications: An International Journal
Using ontology network analysis for research document recommendation
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Most information retrieval systems use keywords entered by the user as the search criteria to find documents. However, the language used in documents is often complicated and ambiguous, and thus the results obtained by using keywords are often inaccurate. To address this problem, this study developed a semantic-based content mapping mechanism for an information retrieval system. This approach employs the semantic features and ontological structure of the content as the basis for constructing a content map, thus simplifying the search process and improving the accuracy of the returned results.