Attributive concept descriptions with complements
Artificial Intelligence
Lp, a logic for representing and reasoning with statistical knowledge
Computational Intelligence
Automated resolution of semantic heterogeneity in multidatabases
ACM Transactions on Database Systems (TODS)
Machine Learning
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Commonality-Based ABox Retrieval
Commonality-Based ABox Retrieval
A dissimilarity measure for ALC concept descriptions
Proceedings of the 2006 ACM symposium on Applied computing
Data Mining
The Description Logic Handbook
The Description Logic Handbook
Computing least common subsumers in description logics with existential restrictions
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Inductive concept retrieval and query answering with semantic knowledge bases through kernel methods
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Decentralized case-based reasoning for the semantic web
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
A framework for handling inconsistency in changing ontologies
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Resolution-Based approximate reasoning for OWL DL
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
A declarative kernel for concept descriptions
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Fuzzy Clustering for Categorical Spaces
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Prediction of class and property assertions on OWL ontologies through evidence combination
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
This work presents a method founded in instance-based learning for inductive (memory-based) reasoning on ABoxes. The method, which exploits a semantic dissimilarity measure between concepts and instances, can be employed both to answer class membership queries and to predict new assertions that may be not logically entailed by the knowledge base. These tasks may be the baseline for other inductive methods for ontology construction and evolution. In a preliminary experimentation, we show that the method is sound and it is actually able to induce new assertions that might be acquired in the knowledge base.