Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Vast amounts of information is encoded in structured tables found in documents, on the b, and in spreadsheets or databases. Integrating or searching over this information benets from understanding its intended meaning. Evidence for a table’s meaning can be found in its column headers, cell values, implicit relations beten columns, caption and surrounding text but also requires general and domain-specic background knowledge.  represent a table’s meaning by mapping columns to classes in an appropriate ontology, linking cell values to literal constants, implied measurements, or entities in the linked data cloud (existing or new) and discovering or and identifying relations beten columns.  describe techniques grounded in graphical models and probabilistic reasoning to infer meaning (semantics) associated with a table. Using background knowledge from the Linked Open Data cloud,  jointly infer the semantics of column headers, table cell values (e.g.,strings and numbers) and relations beten columns and represent the inferred meaning as graph of RDF triples.  motivate the value of this approach using tables from the medical domain, discussing some of the challenges presented by these tables and describing techniques to tackle them.
['Varish Mulwad', 'Tim Finin', 'Anupam Joshi']
I am writing a research paper on a idea Generating Linked Data by Inferring the Semantics of Tables
[{"from": "human", "value": "I am writing a research paper on a idea Generating Linked Data by Inferring the Semantics of Tables"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Vast amounts of information is encoded in structured tables found in documents, on the b, and in spreadsheets or databases. Integrating or searching over this information benets from understanding its intended meaning. Evidence for a table\u2019s meaning can be found in its column headers, cell values, implicit relations beten columns, caption and surrounding text but also requires general and domain-specic background knowledge.  represent a table\u2019s meaning by mapping columns to classes in an appropriate ontology, linking cell values to literal constants, implied measurements, or entities in the linked data cloud (existing or new) and discovering or and identifying relations beten columns.  describe techniques grounded in graphical models and probabilistic reasoning to infer meaning (semantics) associated with a table. Using background knowledge from the Linked Open Data cloud,  jointly infer the semantics of column headers, table cell values (e.g.,strings and numbers) and relations beten columns and represent the inferred meaning as graph of RDF triples.  motivate the value of this approach using tables from the medical domain, discussing some of the challenges presented by these tables and describing techniques to tackle them."}]