Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Measuring the semantic relatedness beten two entities is the basis for numerous tasks in IR, NLP, and b-based knowledge extraction.  focuses on disambiguating names in a b or text document by jointly mapping all names onto semantically related entities registered in a knowledge base. To this end,  have developed a novel notion of semantic relatedness beten two entities represented as sets of ighted (multi-word) keyphrases, with consideration of partially overlapping phrases. This measure improves the quality of prior link-based models, and also eliminates the need for (usually Wikipedia-centric) explicit interlinkage beten entities. Thus, our method is more versatile and can cope with long-tail and newly emerging entities that have few or no links associated with them. For efficiency,  have developed approximation techniques based on min-hash sketches and locality-sensitive hashing. Our experiments on semantic relatedness and on named entity disambiguation demonstrate the superiority of our method compared to state-of-the-art baselines.
['Johannes Hoffart', 'Stephan Seufert', 'Dat Ba Nguyen', 'Martin Theobald', 'Gerhard Weikum']
I am writing a research paper on a idea KORE: keyphrase overlap relatedness for entity disambiguation
[{"from": "human", "value": "I am writing a research paper on a idea KORE: keyphrase overlap relatedness for entity disambiguation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Measuring the semantic relatedness beten two entities is the basis for numerous tasks in IR, NLP, and b-based knowledge extraction.  focuses on disambiguating names in a b or text document by jointly mapping all names onto semantically related entities registered in a knowledge base. To this end,  have developed a novel notion of semantic relatedness beten two entities represented as sets of ighted (multi-word) keyphrases, with consideration of partially overlapping phrases. This measure improves the quality of prior link-based models, and also eliminates the need for (usually Wikipedia-centric) explicit interlinkage beten entities. Thus, our method is more versatile and can cope with long-tail and newly emerging entities that have few or no links associated with them. For efficiency,  have developed approximation techniques based on min-hash sketches and locality-sensitive hashing. Our experiments on semantic relatedness and on named entity disambiguation demonstrate the superiority of our method compared to state-of-the-art baselines."}]