Jerome Thomere

Knowledge entry as the graphical assembly of components

Despite some successes, the lack of tools to allow subject matter experts to directly enter, query, and debug formal domain knowledge in a knowledge-base still remains a major obstacle to their deployment. Our goal is to create such tools, so that a trained knowledge engineer is no longer required to mediate the interaction. This paper presents our work on the knowledge entry part of this overall knowledge capture task, which is based on several claims: that users can construct representations by connecting pre-fabricated, representational components, rather than writing low-level axioms; that these components can be presented to users as graphs; and the user can then perform composition through graph manipulation operations. To operationalize this, we have developed a novel technique of graphical dialog using examples of the component concepts, followed by an automated process for generalizing the users graphically-entered assertions into axioms. We present these claims, our approach, the system (called SHAKEN) that we are developing, and an evaluation of our progress based on having users encode knowledge using the system.

Enabling domain experts to convey questions to a machine: a modified, template-based approach

In order for a knowledge capture system to be effective, it needs to not only acquire general domain knowledge from experts, but also capture the specific problem-solving scenarios and questions which those experts are interested in solving using that knowledge. For some tasks, this latter aspect of knowledge capture is straightforward. In other cases, in particular for systems aimed at a wide variety of tasks, the question-posing aspect of knowledge capture can be a challenge in its own right. In this paper, we present the approach we have developed to address this challenge, based on the creation of a catalog of domain-independent question types and the extension of question template methods with graphical tools. Our goal was that domain experts could directly convey complex questions to a machine, in a form which it could then reason with. We evaluated the resulting system over several weeks, and in this paper we report some important lessons learned from this evaluation, revealing several interesting strengths and weaknesses of the approach.

Template-Based Structured Argumentation

A semiautomated approach to evidential reasoning uses template-based structured argumentation. A template captures best analytic practice as a hierarchically structured set of coordinated questions; an argument answers the questions posed by a template, including references to the source material used as evidence to support those answers. Graphical depictions of arguments readily convey lines of reasoning, from evidence through to conclusions, making it easy to compare and contrast alternative lines of reasoning. Collaborative analysis is supported via simultaneous access to arguments through web browser clients connected to a common argument server. This approach to analysis has been applied to a wide range of analytic problems and has been experimentally shown to speed the development and improve the quality of analytic assessments.

A Process Catalog for Workflow Generation

As AI developers increasingly look to workflow technologies to perform complex integrations of individual software components, there is a growing need for the workflow systems to have expressive descriptions of those components. They must know more than just the types of a components inputs and outputs; instead, they need detailed characterizations that allow them to make fine-grained distinctions between candidate components and between candidate workflows. This paper describes ProCat , an implemented ontology-based catalog for components, conceptualized as processes , that captures and communicates this detailed information. ProCat is built on a layered representation that allows reasoning about processes at varying levels of abstraction, from qualitative constraints reflecting preconditions and effects, to quantitative predictions about output data and performance. ProCat employs Semantic Web technologies RDF, OWL, and SPARQL, and builds on Semantic Web services research. We describe ProCats approach to representing and answering queries about processes, discuss some early experiments evaluating the quantitative predictions, and report on our experience using ProCat in a system producing workflows for intelligence analysis.