Ontologies and reasoning techniques for (legal) intelligent information retrieval systems

Zarri, Gian Piero

doi:10.1007/s10506-007-9037-1

Cited by 8 publications

(2 citation statements)

References 27 publications

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“…In addition, they gave a capability for enormous scalability in a brief, which is able to balance calculations efficiently and effectively in the age of Big Data. Some aspects of this work were depicted by Gian Piero Zarri [22] (2007). Indeed, we believe that the information extraction methods and intellectual data gathering methods used in this research may be useful in an 'Ontological Analysis of Legal Occurrences and Legal Thought' environment.…”

Section: Literature Reviewmentioning

confidence: 96%

“…In terms of efficiency, AI databases have a numerical framework. Microsoft Batch AI proposes a cloud-based platform for deep energy-efficient learning and machine energy-efficient learning techniques running on Microsoft Azure GPUs [22]. A further illustration is Google's AutoML system, which is re-engineering the process through which machine energy-efficient learning models are trained.…”

Section: Microsoftazure and Googleautomlmentioning

confidence: 99%

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AI Based Learning Model Management Framework for Private Cloud Computing

Dhaya¹,

Dhaya²,

Kanthavel³

2022

Journal of Internet Technology

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<p>Artificial Intelligence (AI) systems are computational simulations that are “educated” using knowledge and individual expert participation to replicate a decision that a professional would make provided the same data. A model tries to simulate a specific decision loop that several scientists would take if they had access to all kinds of knowledge. To convey a model, you make a model asset in AI Platform Prediction, make a variant of that model and, at that point, interface the model form to the model record put away in Cloud Storage. AI and DB information sharing are essential for cutting-edge processing for DBMS innovation. The inspirations promoting their incorporation advances incorporate the requirement for admittance to a lot of data that is shared information handling, effective administration of data as information, and astute preparation of information. Notwithstanding these inspirations, the plan for a smart information base interface (IDI) was likewise spurred by the craving to save the considerable speculation spoke to by most existing data sets. A few general ways to deal with the connectivity of AI and databases and different improvements in the area of clever information bases were already examined and announced in this paper.</p> <p> </p>

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Section: Literature Reviewmentioning

confidence: 96%

Section: Microsoftazure and Googleautomlmentioning

confidence: 99%