Knowledge base systems are brittle when the users of the knowledge
base are unfamiliar with its content and structure. Querying a
knowledge base requires users to state their questions in precise and
complete formal representations that relate the facts in the question
with relevant terms and relations in the underlying knowledge base.
This requirement places a heavy burden on the users to become deeply
familiar with the contents of the knowledge base and prevents novice
users to effectively using the knowledge base for problem solving. As
a result, the utility of knowledge base systems is often restricted to
the developers themselves.
The goal of this work is to help users, who may possess little domain
expertise, to use unfamiliar knowledge bases for problem solving. Our
thesis is that the difficulty in using unfamiliar knowledge bases can
be addressed by an approach that funnels natural questions, expressed
in English, into formal representations appropriate for automated
reasoning. The approach uses a simplified English controlled language,
a domain-neutral ontology, a set of mechanisms to handle a handful of
well known question types, and a software component, called the
Question Mediator, to identify relevant information in the knowledge
base for problem solving. With our approach, a knowledge base user
can use a variety of unfamiliar knowledge bases by posing their
questions with simplified English to retrieve relevant information in
the knowledge base for problem solving.
We studied the thesis in the context of a system called ASKME. We
evaluated ASKME on the task of answering exam questions for college
level biology, chemistry, and physics. The evaluation consists of
successive experiments to test if ASKME can help novice users employ
unfamiliar knowledge bases for problem solving. The initial
experiment measures ASKME's level of performance under ideal
conditions, where the knowledge base is built and used by the same
knowledge engineers. Subsequent experiments measure ASKME's level of
performance under increasingly realistic conditions. In the final
experiment, we measure ASKME's level of performance under conditions
where the knowledge base is independently built by subject matter
experts and the users of the knowledge base are a group of novices who
are unfamiliar with the knowledge base.
Results from the evaluation show that ASKME works well on different
knowledge bases and answers a broad range of questions that were posed
by novice users in a variety of domains. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2009-12-580 |
| Date | 02 April 2012 |
| Creators | Chaw, Shaw Yi |
| Contributors | Porter, Bruce, 1956- |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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