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On Establishing Behaviorally Adoptive Semantic Narratives

Authors:
Jason Bryant
Matthew Paulini
Gregory Hasseler
Norman Ahmed

Keywords: semantic modeling; consumer behavioral modeling; direct qualification; semantic collaborative filtering; information personalization;

Abstract:
Providing personalized consumer contents can be both empowered and simplified through adapting analytics modeling and results with semantic representations. Analytics representations tend to be unique to their proprietary technological solutions, growing silos of non-interoperable, non-shareable results. Our approach to overcoming these obstacles is to pair our analytical modeling solution, Direct Qualification, with middleware integrated algorithms for graph, content, identity, and behavioral-based analytics. This abstraction layer of semantically represented analytics enabled multiple best-fit analytics engines to be deployed in parallel while providing a common query front-end for analytics observations, provenance, and trends. We introduce the establishment and the adaptation of a Behavior Ontology (BO) and Behavior Analytics (BA) modeling. We describe the integration of such behavior modeling with the semantic modeling of analytics and state management for an effective consumer content personalization system. We illustrate our prototype with publish and subscribe middleware and show the preliminary results. These components will be integrated into a holistic semantic analytics solution with autonomous functions for behavior optimizations, pluggable algorithm components, and end-to-end, machine learned personalization for information consumers and producers.

Pages: 126 to 131

Copyright: Copyright (c) The Government of USA, 2015. Used by permission to IARIA.

Publication date: July 19, 2015

Published in: conference

ISSN: 2308-4510

ISBN: 978-1-61208-420-6

Location: Nice, France

Dates: from July 19, 2015 to July 24, 2015