Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Although, the syntactical and structural heterogeneities among inter-language linked open data (LOD) data sources bring many challenges, entity co-reference resolution in a multilingual linked open data (MLOD) setting is not ll studied.   In this research, a three phase approach is proposed. First, statistical relational learning (SRL) with factorization of three way tensor is used to compute structural similarity beten entities. Second, textual data from the b of documents is associated in order to increase our knowledge of entities. Through a latent Dirichlet allocation (LDA), entities' textual data is projected into a cross-lingual topic space. This cross-lingual topic space is used to find textual similarities beten entities. Third, a belief aggregation strategy is used to combine the structural and textual similarity results into a global similarity score.    have shown by experiments that our algorithm out-performs state of the art approaches based on tensor decomposition for the task of entity co-reference resolution in a MLOD setting.
['Melkamu Beyene', 'Pierre-Edouard Portier', 'Solomon Atnafu', 'Sylvie Calabretto']
I am writing a research paper on a idea Dataset linking in a multilingual linked open data context
[{"from": "human", "value": "I am writing a research paper on a idea Dataset linking in a multilingual linked open data context"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Although, the syntactical and structural heterogeneities among inter-language linked open data (LOD) data sources bring many challenges, entity co-reference resolution in a multilingual linked open data (MLOD) setting is not ll studied.   In this research, a three phase approach is proposed. First, statistical relational learning (SRL) with factorization of three way tensor is used to compute structural similarity beten entities. Second, textual data from the b of documents is associated in order to increase our knowledge of entities. Through a latent Dirichlet allocation (LDA), entities' textual data is projected into a cross-lingual topic space. This cross-lingual topic space is used to find textual similarities beten entities. Third, a belief aggregation strategy is used to combine the structural and textual similarity results into a global similarity score.    have shown by experiments that our algorithm out-performs state of the art approaches based on tensor decomposition for the task of entity co-reference resolution in a MLOD setting."}]