Semantic Contextual Reasoning to Provide Human Behavior
Sarika Jain, Archana Patel

TL;DR
This paper introduces a semantic contextual reasoning model and a diagnostic belief algorithm to improve human behavior understanding and response accuracy in intelligent systems, considering resource constraints and user context.
Contribution
It presents a novel model for quantifying user context and a diagnostic belief algorithm for event identification and confidence estimation based on available resources.
Findings
Query answers and confidence vary with user context
The model effectively handles incomplete and tentative premises
Experimental results validate the approach in routine queries
Abstract
In recent years, the world has witnessed various primitives pertaining to the complexity of human behavior. Identifying an event in the presence of insufficient, incomplete, or tentative premises along with the constraints on resources such as time, data and memory is a vital aspect of an intelligent system. Data explosion presents one of the most challenging research issues for intelligent systems; to optimally represent and store this heterogeneous and voluminous data semantically to provide human behavior. There is a requirement of intelligent but personalized human behavior subject to constraints on resources and priority of the user. Knowledge, when represented in the form of an ontology, procures an intelligent response to a query posed by users; but it does not offer content in accordance with the user context. To this aim, we propose a model to quantify the user context and…
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Taxonomy
TopicsData Management and Algorithms · Semantic Web and Ontologies · Context-Aware Activity Recognition Systems
