Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The Linked Data principles provide a decentral approach for publishing structured data in the RDF format on the b. In contrast to structured data published in relational databases where a key is often provided explicitly, finding a set of properties that allows identifying a resource uniquely is a non-trivial task. Still, finding keys is of central importance for manifold applications such as resource deduplication, link discovery, logical data compression and data integration. ,  address this research gap by specifying a refinement operator, dubbed ROCKER, which  prove to be finite, proper and non-redundant.  combine the theoretical characteristics of this operator with two monotonicities of keys to obtain a time-efficient approach for detecting keys, i.e., sets of properties that describe resources uniquely.  then utilize a hash index to compute the discriminability score efficiently. Therewith,  ensure that our approach can scale to very large knowledge bases. Results show that ROCKER yields more accurate results, has a comparable runtime, and consumes less memory w.r.t. existing state-of-the-art techniques.
['Tommaso Soru', 'Edgard Marx', 'Axel-Cyrille Ngonga Ngomo']
I am writing a research paper on a idea ROCKER: A Refinement Operator for Key Discovery
[{"from": "human", "value": "I am writing a research paper on a idea ROCKER: A Refinement Operator for Key Discovery"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The Linked Data principles provide a decentral approach for publishing structured data in the RDF format on the b. In contrast to structured data published in relational databases where a key is often provided explicitly, finding a set of properties that allows identifying a resource uniquely is a non-trivial task. Still, finding keys is of central importance for manifold applications such as resource deduplication, link discovery, logical data compression and data integration. ,  address this research gap by specifying a refinement operator, dubbed ROCKER, which  prove to be finite, proper and non-redundant.  combine the theoretical characteristics of this operator with two monotonicities of keys to obtain a time-efficient approach for detecting keys, i.e., sets of properties that describe resources uniquely.  then utilize a hash index to compute the discriminability score efficiently. Therewith,  ensure that our approach can scale to very large knowledge bases. Results show that ROCKER yields more accurate results, has a comparable runtime, and consumes less memory w.r.t. existing state-of-the-art techniques."}]