<?xml version="1.0" standalone="yes"?>
<Paper uid="J88-3006">
  <Title>TAILORING OBJECT DESCRIPTIONS TO A USER'S LEVEL OF EXPERTISE</Title>
  <Section position="17" start_page="0" end_page="0" type="concl">
    <SectionTitle>
7 CONCLUSIONS
</SectionTitle>
    <Paragraph position="0"> In this paper we have demonstrated that the user's domain knowledge can be used as a factor in tailoring an answer. In particular, we have shown how the description of a complex physical object might be tailored to a user's level of expertise. We presented different kinds of knowledge users can have, explaining how a system can take them into consideration in order to generate a description. From our studies of texts, we have found two distinct discourse strategies that are used in describing complex devices. We postulated that the level of expertise of the user affects the kind of information given as opposed to just the amount of detail provided.</Paragraph>
    <Paragraph position="1"> Even though we conducted this study in the domain of complex physical objects, we believe this result can extend to other domains. We thus propose that a user model containing information about the user's domain knowledge can be used in a question answering system to guide the decision process. We presented the two distinct descriptive strategies that can be used in a question answering program and showed how they can be mixed to include the appropriate information from the knowledge base, based on the information contained in user model.</Paragraph>
    <Paragraph position="2"> Finally, we presented TAILOR, a program that generates descriptions tailored to users with various levels of expertise. TAILOR employs one of the two discourse strategies described to generate a text for a novice or an expert. TAILOR is also able to automatically mix the strategies to provide device descriptions tailored to users whose domain knowledge fall anywhere along the knowledge spectrum. By representing explicitly the user's domain knowledge in terms of parameters, TAILOR does not require an a priori set of stereotypes but can provide wide variety of descriptions for a whole range of users.</Paragraph>
  </Section>
class="xml-element"></Paper>