Patterns and operators: the foundations of data representation
Patterns and operators: the foundations of data representation
Formal ontology, conceptual analysis and knowledge representation
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
Making ontologies work for resolving redundancies across documents
Communications of the ACM - Ontology: different ways of representing the same concept
Determining Semantic Similarity among Entity Classes from Different Ontologies
IEEE Transactions on Knowledge and Data Engineering
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Interaction of Purposeful Agents that Use Different Ontologies
MICAI '00 Proceedings of the Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Document Comparison with a Weighted Topic Hierarchy
DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
Knowledge accumulation through automatic merging of ontologies
Expert Systems with Applications: An International Journal
iRank: Ranking Geographical Information by Conceptual, Geographic and Topologic Similarity
GeoS '09 Proceedings of the 3rd International Conference on GeoSpatial Semantics
Geographic information retrieval by topological, geographical, and conceptual matching
GeoS'07 Proceedings of the 2nd international conference on GeoSpatial semantics
Semantic similarity applied to generalization of geospatial data
GeoS'07 Proceedings of the 2nd international conference on GeoSpatial semantics
The centroid or consensus of a set of objects with qualitative attributes
Expert Systems with Applications: An International Journal
Multi-criteria geographic information retrieval model based on geospatial semantic integration
GeoS'11 Proceedings of the 4th international conference on GeoSpatial semantics
Hi-index | 12.05 |
The concept of hierarchy has being explored by the computer science communities during last few decades. Relatively simple hierarchical structures found extensive use in such diverse areas as data modeling, information retrieval, knowledge representation and processing, natural language, pattern recognition, and so on. Recent investigations in information retrieval and data integration have emphasized the use of ontologies and semantic similarity functions as a mechanism for comparing objects that can be retrieved or integrated across heterogeneous repositories. Hierarchies being a simpler, albeit very useful, version of ontologies, can perfectly contribute to model solutions of these problems. Present paper aims to illustrate above thesis by discussing a simple method of information retrieval that uses a hierarchical qualitative data organization. Its main goal is to retrieve objects from any database that are just close to a desired item and control the retrieval process up to a given error, called herein confusion. For doing this, we define a semantic dissimilarity (confusion) between objects to be retrieved as well as introduce a calculus of predicates based on the confusion function.