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<Paper uid="W06-1808">
  <Title>Numerical Data Integration for Cooperative Question-Answering</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1.2.1 Inclusion
</SectionTitle>
    <Paragraph position="0"> A candidate answer is in an inclusion relation if it entails another answer (for example, concepts of candidate answers linked in an ontology by the is-a or part-of relations). For example, in Brittany and in France are correct answers to the question Where is Brest?, linked by an inclusion relation since Brittany is a part of France.</Paragraph>
    <Paragraph position="1">  Candidate answers which are linked by an equivalence relation are consistent and entail mutually. The corpus analysis allows us to identify two main types of equivalence: (1) Lexical equivalence: use of acronyms or foreign language, synonymies, metonymies, paraphrases, proportional series. For example, to the question Who killed John Lennon?, Mark Chapman, the murderer of John Lennon and John Lennon's killer Mark Chapman are equivalent answers. null (2) Equivalence with inference: in a number of cases, some common knowledge, inferences or computations are necessary to detect equivalence relations. For example, The A320 is 22 and The A320 was built in 1984 are equivalent answers to the question How old is the Airbus A320?.</Paragraph>
    <Section position="1" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
1.2.3 Aggregation
</SectionTitle>
      <Paragraph position="0"> The aggregation relation defines a set of consistent answers when the question accepts several different ones. In this case, all candidate answers are potentially correct and can be integrated in the form of a conjunction of all these answers. For example, an answer to the question Where is Disneyland? can be in Tokyo, Paris, Hong-Kong and Los Angeles.</Paragraph>
    </Section>
  </Section>
class="xml-element"></Paper>