Mapping part-whole hierarchies into connectionist networks
Artificial Intelligence - On connectionist symbol processing
Recursive distributed representations
Artificial Intelligence - On connectionist symbol processing
Integrating Multiple Paradigms within the Blackboard Framework
IEEE Transactions on Software Engineering
Transition network grammars for natural language analysis
Communications of the ACM
Symbolic Logic and Mechanical Theorem Proving
Symbolic Logic and Mechanical Theorem Proving
Prolog/Rex-A Way to Extend Prolog for Better Knowledge Representation
IEEE Transactions on Knowledge and Data Engineering
Incremental Syntactic Parsing of Natural Language Corpora with Simple Synchrony Networks
IEEE Transactions on Knowledge and Data Engineering
Representation and Processing of Structures with Binary Sparse Distributed Codes
IEEE Transactions on Knowledge and Data Engineering
DAML+OIL: An Ontology Language for the Semantic Web
IEEE Intelligent Systems
Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential
Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential
Semantic Information Processing
Semantic Information Processing
Extracting finite structure from infinite language
Knowledge-Based Systems
Semantic Knowledge Processing using localist approach
TELE-INFO'08 Proceedings of the 7th WSEAS International Conference on Telecommunications and Informatics
Semantic approach to knowledge processing
WSEAS Transactions on Information Science and Applications
Applying neural networks to knowledge representation and determination of its meaning
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
NIMFA - Natural language Implicit Meaning Formalization and Abstraction
Expert Systems with Applications: An International Journal
Adapting domain ontology for personalized knowledge search and recommendation
Information and Management
| Hi-index | 12.05 |
Good knowledge organization is crucial in two cases-in the representation of semantically related information and in the representation of huge quantities of data. The answer to the problem of semantic knowledge representation is given by various knowledge representation techniques used in Artificial Intelligence, while for the representation of large quantities of data, relational databases represent the optimal choice. If we analyze the characteristics important from the knowledge organization point of view, we could say that structure representation provides means for knowledge representation, unique object representation ensures representational efficiency, while representation of bottom-up hierarchies (e.g. indexing scheme in relational databases) brings search efficiency. Furthermore, unique object representation and bottom-up (contextual) hierarchies provide support for semantically organized knowledge. In this paper we describe a knowledge representation technique, Hierarchical Semantic Form (HSF), which, together with the Space Of Universal Links (SOUL) algorithm, aims at supporting both of the above mentioned characteristics of well organized knowledge. HSF represents a hybrid solution that enables both structure and context representations, combining the characteristics of classicist approach (symbolic and semantic processing) with the characteristics of connectionist approach (parallelism, learning).