Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: There is a large amount of textual data on the b and in Wikipedia, where mentions of entities (such as Gandhi) are annotated with a link to the disambiguated entity (such as M. K. Gandhi). Such annotation may have been done manually (as in Wikipedia) or can be done using named entity recognition/disambiguation techniques. Such an annotated corpus allows queries to return entities, instead of documents. Entity ranking queries retrieve entities that are related to keywords in the query and belong to a given type/category specified in the query; entity ranking has been an active area of research in the past few years. More recently, there have been extensions to allow entity-relationship queries, which allow specification of multiple sets of entities as ll as relationships beten them.#R##N##R##N#  address the problem of entity ranking ("near") queries and entity-relationship queries on theWikipedia corpus.  first present an extended graph model which combines the por of graph models used earlier for structured/semi-structured data, with information from textual data. Based on this model,  show how to specify entity and entity-relationship queries, and defined scoring methods for ranking ansrs. Finally,  provide efficient algorithms for ansring such queries, exploiting a space efficient in-memory graph structure. A performance comparison with the ERQ system proposed earlier shows significant improvement in ansr quality for most queries, while also handling a much larger set of entity types.
['Ankur Agrawal', 'S. Sudarshan', 'A. Sahoo', 'Adil Anis Sandalwala', 'Prashant Jaiswal']
I am writing a research paper on a idea Entity ranking and relationship queries using an extended graph model
[{"from": "human", "value": "I am writing a research paper on a idea Entity ranking and relationship queries using an extended graph model"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: There is a large amount of textual data on the b and in Wikipedia, where mentions of entities (such as Gandhi) are annotated with a link to the disambiguated entity (such as M. K. Gandhi). Such annotation may have been done manually (as in Wikipedia) or can be done using named entity recognition/disambiguation techniques. Such an annotated corpus allows queries to return entities, instead of documents. Entity ranking queries retrieve entities that are related to keywords in the query and belong to a given type/category specified in the query; entity ranking has been an active area of research in the past few years. More recently, there have been extensions to allow entity-relationship queries, which allow specification of multiple sets of entities as ll as relationships beten them.#R##N##R##N#  address the problem of entity ranking (\"near\") queries and entity-relationship queries on theWikipedia corpus.  first present an extended graph model which combines the por of graph models used earlier for structured/semi-structured data, with information from textual data. Based on this model,  show how to specify entity and entity-relationship queries, and defined scoring methods for ranking ansrs. Finally,  provide efficient algorithms for ansring such queries, exploiting a space efficient in-memory graph structure. A performance comparison with the ERQ system proposed earlier shows significant improvement in ansr quality for most queries, while also handling a much larger set of entity types."}]