Enhancing Information Awareness Through Directed Qualification of Semantic Relevancy Scoring Operations
This work addresses a domain-specific problem in semantic technologies for researchers and practitioners dealing with analytics-based relevancy scoring.
The paper tackles the problem of modeling analytics-based relationships in semantic technologies by implementing Directed Qualification through pairing Prov-O Ontology with a relevancy ontology, resulting in the capability to associate semantically referenced documents with analytics results as modeled relational nodes.
Successfully managing analytics-based semantic relationships and their provenance enables determinations of document importance and priority, furthering capabilities for machine-based relevancy scoring operations. Semantic technologies are well suited for modeling explicit and fully qualified relationships but struggle with modeling relationships that are qualified in nature, or resultant from applied analytics. Our work seeks to implement the autonomous Directed Qualification of analytic-based relationships by pairing the Prov-O Ontology (W3C Recommendation) with a relevancy ontology supporting analytics terminology. This work results in the capability for any semantically referenced document, concept, or named graph to be associated with the results of applied analytics as Direct Qualification (DQ) modeled relational nodes. This new capability will enable role, identity, or any other content-based measures of relevancy and analytics-based metrics for semantically described documents.