Enhancing Information Awareness Through Directed Qualification of Semantic Relevancy Scoring Operations

Abstract

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.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2014
Accession Number
ADA606763

Entities

People

  • Greg Hasseler
  • Jason Bryant
  • Matthew Paulini
  • Timothy Lebo

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Battle Damage Assessment
  • Command And Control
  • Computer Access Control
  • Data Sets
  • Graphs
  • Identities
  • Language
  • Military Research
  • Models
  • Natural Language Processing
  • Natural Languages
  • Ontologies
  • Qualifications
  • Semantic Models
  • Semantics

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Distributed Systems and Data Platform Development
  • Psychometric Testing or Psychological Assessment.