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<Paper uid="W06-3505">
  <Title>Scaling Natural Language Understanding via User-driven Ontology Learning Berenike Loos</Title>
  <Section position="2" start_page="0" end_page="33" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> To let a computer system understand natural language one needs knowledge about objects and their relations in the real world. As the manual modeling and maintenance of such knowledge structures, i.e.</Paragraph>
    <Paragraph position="1"> ontologies, are not only time and cost consuming, but also lead to not-scalable systems, there exists a demand to build and populate them automatically or at least semi automatically. This is possible by analyzing unstructured, semi-structured or fully structured data by various linguistic as well as statistical means and by converting the results into an ontological form.</Paragraph>
    <Paragraph position="2"> In an open-domain scalable natural language understanding (NLU) system the automatic learning of ontological concepts and corresponding relations between them is essential, as a complete modeling of the world is neither practicable nor feasible, as the real world and its objects, models and processes are constantly changing along with their denotations.</Paragraph>
    <Paragraph position="3"> This paper assumes that a viable approach to this challenging problem is to learn ontological concepts and relations relevant to a certain user in a given context by the dialog system at the time of the user's inquiry. My central hypothesis is that the information about terms that lack any mapping to the employed knowledge representation of the language understanding component can only be found in topical corpora such as the Web. With the help of this information one can find the right node in the ontology to append the concept corresponding to the unknown term in case it is a noun or to insert it as an instance in case it is a proper noun or another named entity.</Paragraph>
    <Paragraph position="4"> The goal of the ontology learning component is to extend the knowledge base of the NLU system and therefore it will gradually adapt to the user's needs.</Paragraph>
    <Paragraph position="5"> An example from the area of spoken dialog systems would be that of a user walking through the city of Heidelberg and asking: &amp;quot;How do I get to  the Auerstein&amp;quot;. This would lead to the detection of Auerstein as being neither recognizable by the speech recognizer nor mappable to the knowledge representation of the system. Therefore, the corresponding hypernym of Auerstein has to be found on the internet by recourse to additional information about the context of the user. In this case, the additional information consists of the location of the user, namely Heidelberg. Once found, the hypernym is mapped to a corresponding concept, which already exists in the ontology. If there is no such corresponding concept, the concept for the hypernym thereof has to be determined. The formerly unknown term is mapped to a concept and is integrated into the system's ontology as a child of the concept for the found hypernym. In case the unknown term is a proper noun, it is integrated as an instance of the concept for the hypernym. So far, the research undertaken is related to nouns and proper nouns, also more generally referred to as terms in this paper.</Paragraph>
    <Paragraph position="6"> In the following section, I will describe related work undertaken to solve the task of ontology learning, followed by some remarks of the distinction between ontology learning and natural language in Section 3. Thereafter, I will sketch out the minimal stages involved in the type of ontology learning proposed herein in Section 4.</Paragraph>
  </Section>
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