5 research outputs found
A formalism and method for representing and reasoning with process models authored by subject matter experts
Enabling Subject Matter Experts (SMEs) to formulate knowledge without the intervention of Knowledge Engineers (KEs) requires providing SMEs with methods and tools that abstract the underlying knowledge representation and allow them to focus on modeling activities. Bridging the gap between SME-authored models and their representation is challenging, especially in the case of complex knowledge types like processes, where aspects like frame management, data, and control flow need to be addressed. In this paper, we describe how SME-authored process models can be provided with an operational semantics and grounded in a knowledge representation language like F-logic in order to support process-related reasoning. The main results of this work include a formalism for process representation and a mechanism for automatically translating process diagrams into executable code following such formalism. From all the process models authored by SMEs during evaluation 82% were well-formed, all of which executed correctly. Additionally, the two optimizations applied to the code generation mechanism produced a performance improvement at reasoning time of 25% and 30% with respect to the base case, respectively
Using Natural Language as Knowledge Representation in an Intelligent Tutoring System
Knowledge used in an intelligent tutoring system to teach students is usually acquired from authors who are experts in the domain. A problem is that they cannot directly add and update knowledge if they donât learn formal language used in the system. Using natural language to represent knowledge can allow authors to update knowledge easily. This thesis presents a new approach to use unconstrained natural language as knowledge representation for a physics tutoring system so that non-programmers can add knowledge without learning a new knowledge representation. This approach allows domain experts to add not only problem statements, but also background knowledge such as commonsense and domain knowledge including principles in natural language. Rather than translating into a formal language, natural language representation is directly used in inference so that domain experts can understand the internal process, detect knowledge bugs, and revise the knowledgebase easily. In authoring task studies with the new system based on this approach, it was shown that the size of added knowledge was small enough for a domain expert to add, and converged to near zero as more problems were added in one mental model test. After entering the no-new-knowledge state in the test, 5 out of 13 problems (38 percent) were automatically solved by the system without adding new knowledge
Ontology evolution in physics
With the advent of reasoning problems in dynamic environments, there is an increasing
need for automated reasoning systems to automatically adapt to unexpected changes
in representations. In particular, the automation of the evolution of their ontologies
needs to be enhanced without substantially sacrificing expressivity in the underlying
representation. Revision of beliefs is not enough, as adding to or removing from beliefs
does not change the underlying formal language. General reasoning systems employed
in such environments should also address situations in which the language for representing
knowledge is not shared among the involved entities, e.g., the ontologies in
a multi-ontology environment or the agents in a multi-agent environment. Our techniques
involve diagnosis of faults in existing, possibly heterogeneous, ontologies and
then resolution of these faults by manipulating the signature and/or the axioms.
This thesis describes the design, development and evaluation of GALILEO (Guided
Analysis of Logical Inconsistencies Lead to Evolution of Ontologies), a system designed
to detect conflicts in highly expressive ontologies and resolve the detected conflicts
by performing appropriate repair operations. The integrated mechanism that
handles ontology evolution is able to distinguish between various types of conflicts,
each corresponding to a unique kind of ontological fault. We apply and develop our
techniques in the domain of Physics. This an excellent domain because many of its
seminal advances can be seen as examples of ontology evolution, i.e. changing the
way that physicists perceive the world, and case studies are well documented â unlike
many other domains. Our research covers analysing a wide ranging development set
of case studies and evaluating the performance of the system on a test set. Because
the formal representations of most of the case studies are non-trivial and the underlying
logic has a high degree of expressivity, we face some tricky technical challenges,
including dealing with the potentially large number of choices in diagnosis and repair.
In order to enhance the practicality and the manageability of the ontology evolution
process, GALILEO incorporates the functionality of generating physically meaningful
diagnoses and repairs and, as a result, narrowing the search space to a manageable size