DIVINE | FIT-IT Semantic Systems Project
Dynamic Integration and Visualization of Information
from Multiple Evidence Sources
Content providers and analysts alike increasingly rely on combining multiple data sources to build comprehensive, up-to-date and properly interlinked information spaces. These organizations critically depend on technologies for integrating these sources and tracking their evolution.
DIVINE aims to provide such technologies, with a lightweight seed ontology acting as the focal point for integrating new evidence derived from multiple, evolving data sources. As such, the project advances ontology evolution research characterized by single-source solutions, which exploit mostly textual and rather static data. DIVINE integrates structured, unstructured and social sources. A modular and scalable portfolio of evidence acquisition services crawls public Web documents, queries Linked Open Data repositories, aggregates resource annotations from Web 2.0 applications, and triggers validation processes for missing or conflicting evidence. Since evidence from third-party sources is inherently uncertain, source-specific transformation rules and impact factors assign a confidence value to each new fact. A spreading activation network utilizes the collected evidence in conjunction with the confidence values for extending the seed ontology.
Knowledge Evolution Patterns
DIVINE will monitor domain changes over time to derive knowledge evolution patterns. This domain-centric view makes DIVINE novel among existing change detection approaches, which tend to be domain-agnostic. Each ontology element is assigned a confidence matrix, which records the changes in confidence values over time. Data services and dynamic visualizations reveal rising, declining or cyclic patterns in the confidence matrices. Such patterns are important indicators – the rate of change or the date of a concept’s first appearance, for example, shed light on the evolution of knowledge and on the underlying processes that drive this evolution.
Use Cases and Prototypes
Two use cases on news media monitoring and environmental knowledge management demonstrate and evaluate the system’s capabilities to structure large knowledge repositories in a scalable manner, uncover and manage flows of relevant information between stakeholders, and measure how well an organization’s external communication is received, understood, and remembered. The use cases will be pursued in close collaboration with the project’s industry and associate partners.
Project Partners
- Research Institute for Computational Methods,
Vienna University of Economics & Business - Department of New Media Technology,
MODUL University Vienna (Sub-Contractor) - Know-Center GmbH
- Gentics Software GmbH
- webLyzard Technology GmbH
