Daniel Hunter

CADRE: continuous analysis and discovery from relational evidence

CADRE (continuous analysis and discovery from relational evidence) is a link detection system that takes in a threat pattern and partial evidence about threat cases and outputs threat hypotheses with inferred actors and events. CADRE uses a Prolog-based frame system to represent threat patterns and enforce temporal and equality constraints among pattern slots. Based on rules involving uniquely identifying slots in the pattern, CADRE triggers an initial set of threat hypotheses, and then refines these hypotheses by generating queries for unknown slots from constraints involving known slots. To evaluate hypotheses, CADRE scores each local hypothesis using a probabilistic model in order to create a consistent, high-value global hypothesis by pruning conflicting lower scoring hypotheses. In a program-wide first year evaluation using simulated threats, CADRE performed best overall among five participating link detection systems.

Rapid knowledge base design via extension of mid-level knowledge components

The full potential of knowledge based systems will be realized only when users who are not knowledge engineers are able to develop and deploy the underlying knowledge bases. To this end we are developing a system for rapid knowledge base design that permits users to draw on an existing library of knowledge components and, through intuitive interfaces, shape them into an ontology for a domain and application of interest. The models created are supported by an expressive language that provides sophisticated constraint reasoning.

Towards explanation of scientific and technological emergence

Analysts who are interested in quickly identifying new and emerging scientific advancements have numerous challenges as the breadth, depth, and volume of scientific literature increases. Network analysis and mining is key to the success in this task. The ARBITER system seeks to identify indicators of emergence and provide a system that is capable of analyzing corpora of full text and metadata to identify emerging science topics and explain its reasoning and conclusions. In this paper, we describe a network-modeling framework that is used in the ARBITER system, and describe our novel hybrid approach using probabilistic foundations in combination with semantic technology and introduce our explanation infrastructure. We include a discussion of some challenges and opportunities related to explaining hybrid approaches to indicator-based analysis and emergence detection.

Explaining Scientific and Technical Emergence Forecasting

In decision support systems such as those designed to predict scientific and technical emergence based on analysis of collections of data the presentation of provenance lineage records in the form of a human-readable explanation has been shown to be an effective strategy for assisting users in the interpretation of results. This work focuses on the development of a novel infrastructure for enabling the explanation of hybrid intelligence systems including probabilistic models—in the form of Bayes nets—and the presentation of corresponding evidence. Our design leverages Semantic Web technologies—including a family of ontologies—for representing and explaining emergence forecasting for entity prominence. Our infrastructure design has been driven by two goals: first, to provide technology to support transparency into indicator-based forecasting systems; second, to provide analyst users context-aware mechanisms to drill down into evidence underlying presented indicators. The driving use case for our explanation infrastructure has been a specific analysis system designed to automate the forecasting of trends in science and technology based on collections of published patents and scientific journal articles.

Flexible creation of indicators of scientific and technological emergence: Emerging phenomena and big data

This paper describes ARBITER, a system for characterizing scientific and technological fields and detecting emergent fields. ARBITER processes large collections of technoscientific publications and patents to extract full-text and metadata features relevant to rich characterizations of emergent fields. The paper describes how ARBITER uses these indicators in a flexible manner to infer a wide variety of patterns of interest using customizable models that capture the users understanding of what is important in emergence.

Hypothesis management for information fusion

The efficient management of large collections of fusion hypotheses presents a critical challenge for scaling high-level information fusion systems to solve large problems. We motivate this challenge in the context of two Alphatech research projects, and discuss several partial solutions. A recurring theme is the exploitation of space-efficient, factored representations of multiple hypotheses to enable an efficient search for good hypotheses.