Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Attribute synonyms are important ingredients for keyword-based search systems. For instance, b search engines recognize queries that seek the value of an entity on a specific attribute (referred to as e+a queries) and provide direct ansrs for them using a combination of knowledge bases, b tables and documents. Hover, users often refer to an attribute in their e+a query differently from how it is referred in the b table or text passage. In such cases, search engines may fail to return relevant ansrs. To address that problem,  propose to automatically discover all the alternate ways of referring to the attributes of a given class of entities (referred to as attribute synonyms) in order to improve search quality. The state-of-the-art approach that relies on attribute name co-occurrence in b tables suffers from low precision. Our main insight is to combine positive evidence of attribute synonymity from query click logs, with negative evidence from b table attribute name co-occurrences.  formalize the problem as an optimization problem on a graph, with the attribute names being the vertices and the positive and negative evidences from query logs and b table schemas as ighted edges.  develop a linear programming based algorithm to solve the problem that has bi-criteria approximation guarantees. Our experiments on real-life datasets show that our approach has significantly higher precision and recall compared with the state-of-the-art.
['Yeye He', 'Kaushik Chakrabarti', 'Tao Cheng', 'Tomasz Tylenda']
I am writing a research paper on a idea Automatic Discovery of Attribute Synonyms Using Query Logs and Table Corpora
[{"from": "human", "value": "I am writing a research paper on a idea Automatic Discovery of Attribute Synonyms Using Query Logs and Table Corpora"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Attribute synonyms are important ingredients for keyword-based search systems. For instance, b search engines recognize queries that seek the value of an entity on a specific attribute (referred to as e+a queries) and provide direct ansrs for them using a combination of knowledge bases, b tables and documents. Hover, users often refer to an attribute in their e+a query differently from how it is referred in the b table or text passage. In such cases, search engines may fail to return relevant ansrs. To address that problem,  propose to automatically discover all the alternate ways of referring to the attributes of a given class of entities (referred to as attribute synonyms) in order to improve search quality. The state-of-the-art approach that relies on attribute name co-occurrence in b tables suffers from low precision. Our main insight is to combine positive evidence of attribute synonymity from query click logs, with negative evidence from b table attribute name co-occurrences.  formalize the problem as an optimization problem on a graph, with the attribute names being the vertices and the positive and negative evidences from query logs and b table schemas as ighted edges.  develop a linear programming based algorithm to solve the problem that has bi-criteria approximation guarantees. Our experiments on real-life datasets show that our approach has significantly higher precision and recall compared with the state-of-the-art."}]