Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ldquoCurse of dimensionalityrdquo is a common problem in the area of information retrieval. It was verified that points in a vector space are projected to a random subspace of suitably high dimension, and then the distances beten the points are approximately preserved. Although such a random projection can be used to reduce the dimension of the document space, it does not bring together semantically related documents. Latent Semantic Indexing (LSI) projects documents to lor dimensional LSI space from higher dimensional term space with singular-value decomposition (SVD) for the purpose of reducing the dimensions of the document space and bringing together semantically related documents. But the computation time of SVD is a bottleneck because of the higher dimensions of documents. , a novel method of dimension reduction for improving LSI is provided. A term-to-concept projection matrix based on domain ontology was created in this method. This way documents re projected to lor dimensional concept space by the projection matrix. LSI pre-computation was performed not on the original term by document matrix, but on the lor dimensional concept by document matrix at great computational savings. Experiments indicate that this method improves the efficiency of LSI. And the similarity judgment beten documents is not disturbed.
['Jingmin Hao', 'Lejian Liao', 'Xiujie Dong']
I am writing a research paper on a idea Improving Latent Semantic Indexing with concepts mapping based on domain ontology
[{"from": "human", "value": "I am writing a research paper on a idea Improving Latent Semantic Indexing with concepts mapping based on domain ontology"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ldquoCurse of dimensionalityrdquo is a common problem in the area of information retrieval. It was verified that points in a vector space are projected to a random subspace of suitably high dimension, and then the distances beten the points are approximately preserved. Although such a random projection can be used to reduce the dimension of the document space, it does not bring together semantically related documents. Latent Semantic Indexing (LSI) projects documents to lor dimensional LSI space from higher dimensional term space with singular-value decomposition (SVD) for the purpose of reducing the dimensions of the document space and bringing together semantically related documents. But the computation time of SVD is a bottleneck because of the higher dimensions of documents. , a novel method of dimension reduction for improving LSI is provided. A term-to-concept projection matrix based on domain ontology was created in this method. This way documents re projected to lor dimensional concept space by the projection matrix. LSI pre-computation was performed not on the original term by document matrix, but on the lor dimensional concept by document matrix at great computational savings. Experiments indicate that this method improves the efficiency of LSI. And the similarity judgment beten documents is not disturbed."}]