Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Ontology matching is an essential aspect of the Semantic b with a goal of finding alignments among the entities of given ontologies. Ontology matching is a necessary step for establishing interoperation and knowledge sharing among Semantic b applications. In this study  present an algorithm and a tool developed based on this algorithm to find correspondences among entities of input ontologies. The proposed algorithm uses a new lexical similarity measure and also utilizes structural information of ontologies to determine their corresponding entities. The lexical similarity measure generates a bag of words for each entity based on its label and description information. The structural approach creates a grid for each node in the ontologies. The combination of lexical and structural approaches creates the similarity matrix beten the source and target ontologies. The proposed algorithm was tested on a ll known benchmark and also compared to other algorithms presented in the literature. Our experimental results show the proposed algorithm is effective and outperforms other algorithms.
['Ismail Akbari', 'Mohammad Fathian']
I am writing a research paper on a idea A novel algorithm for ontology matching
[{"from": "human", "value": "I am writing a research paper on a idea A novel algorithm for ontology matching"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Ontology matching is an essential aspect of the Semantic b with a goal of finding alignments among the entities of given ontologies. Ontology matching is a necessary step for establishing interoperation and knowledge sharing among Semantic b applications. In this study  present an algorithm and a tool developed based on this algorithm to find correspondences among entities of input ontologies. The proposed algorithm uses a new lexical similarity measure and also utilizes structural information of ontologies to determine their corresponding entities. The lexical similarity measure generates a bag of words for each entity based on its label and description information. The structural approach creates a grid for each node in the ontologies. The combination of lexical and structural approaches creates the similarity matrix beten the source and target ontologies. The proposed algorithm was tested on a ll known benchmark and also compared to other algorithms presented in the literature. Our experimental results show the proposed algorithm is effective and outperforms other algorithms."}]