Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describe an approach for performing entity type recognition in heterogeneous semantic graphs in order to reduce the computational cost of performing coreferenceresolution. Our research specifically addresses the problem of working with semi-structured text that uses ontologies that are not informative or not known. This problem is similar to co reference resolution in unstructured text, where entities and their types are identified using contextual information and linguistic-based analysis. Semantic graphs are semi-structured with very little contextual information and trivial grammars that do not convey additional information. In the absence of known ontologies, performing co reference resolution can be challenging. Our work uses a supervised machine learning algorithm and entity type dictionaries to map attributes to a common attribute space.  evaluated the approach in experiments using data from Wikipedia, Freebase and Arnetminer.
['Jennifer Sleeman', 'Tim Finin']
I am writing a research paper on a idea Type Prediction for Efficient Coreference Resolution in Heterogeneous Semantic Graphs
[{"from": "human", "value": "I am writing a research paper on a idea Type Prediction for Efficient Coreference Resolution in Heterogeneous Semantic Graphs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describe an approach for performing entity type recognition in heterogeneous semantic graphs in order to reduce the computational cost of performing coreferenceresolution. Our research specifically addresses the problem of working with semi-structured text that uses ontologies that are not informative or not known. This problem is similar to co reference resolution in unstructured text, where entities and their types are identified using contextual information and linguistic-based analysis. Semantic graphs are semi-structured with very little contextual information and trivial grammars that do not convey additional information. In the absence of known ontologies, performing co reference resolution can be challenging. Our work uses a supervised machine learning algorithm and entity type dictionaries to map attributes to a common attribute space.  evaluated the approach in experiments using data from Wikipedia, Freebase and Arnetminer."}]