Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the Semantic b context, OWL ontologies represent the conceptualization of domains of interest while the corresponding assertional knowledge is given by the heterogeneous b resources referring to them. Being strongly decoupled, ontologies and assertion can be out-of-sync. An ontology can be incomplete, noisy and sometimes inconsistent with regard to the actual usage of its conceptual vocabulary in the assertions. Data mining can support the discovery of hidden knowledge patterns in the data, to enrich the ontologies.  present a method for discovering multi-relational association rules, coded in SWRL, from ontological knowledge bases. Unlike state-of-the-art approaches, the method is able to take the intensional knowledge into account. Furthermore, since discovered rules are represented in SWRL, they can be straight-forwardly integrated within the ontology, thus (i) enriching its expressive por and (ii) augmenting the assertional knowledge that can be derived. Discovered rules may also suggest new axioms to be added to the ontology.  performed experiments on publicly available ontologies validating the performances of our approach.
["Claudia d'Amato", 'Steffen Staab', 'Andrea G. B. Tettamanzi', 'Tran Duc Minh', 'Fabien Gandon']
I am writing a research paper on a idea Ontology enrichment by discovering multi-relational association rules from ontological knowledge bases
[{"from": "human", "value": "I am writing a research paper on a idea Ontology enrichment by discovering multi-relational association rules from ontological knowledge bases"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the Semantic b context, OWL ontologies represent the conceptualization of domains of interest while the corresponding assertional knowledge is given by the heterogeneous b resources referring to them. Being strongly decoupled, ontologies and assertion can be out-of-sync. An ontology can be incomplete, noisy and sometimes inconsistent with regard to the actual usage of its conceptual vocabulary in the assertions. Data mining can support the discovery of hidden knowledge patterns in the data, to enrich the ontologies.  present a method for discovering multi-relational association rules, coded in SWRL, from ontological knowledge bases. Unlike state-of-the-art approaches, the method is able to take the intensional knowledge into account. Furthermore, since discovered rules are represented in SWRL, they can be straight-forwardly integrated within the ontology, thus (i) enriching its expressive por and (ii) augmenting the assertional knowledge that can be derived. Discovered rules may also suggest new axioms to be added to the ontology.  performed experiments on publicly available ontologies validating the performances of our approach."}]