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<Paper uid="I05-2036">
  <Title>Svetlana.Hensman@comp.dit.ie</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> This paper describes a system for semi-automatic conceptual graph acquisition using a combination of linguistic resources, such as VerbNet and WordNet, together with semi-automatically compiled domain-specific knowledge. Such semantic information has a number of possible applications, for example in the area of information retrieval/extraction for enhancing the search methods or in question-answering systems, allowing users to communicate with the system in natural language (English).</Paragraph>
    <Paragraph position="1"> We use conceptual graphs (CGs) (Sowa, 1984), a knowledge-representation formalism based on semantic networks and the existential graphs of C. S. Pierce, to represent the semantics of documents. There are number of systems for generating conceptual graphs representation of sentences: Sowa and Way (Sowa and Way, 1986) use a lexicon of canonical graphs which are combined to build a conceptual graph representation of a sentence, while Veraldi at al. (Velardi et al., 1988) describe a prototype of a semantic processor for Italian sentences, which uses a manually acquired lexicon of about 850 word-sense definitions, each including 10 - 20 surface semantic patterns (SSPs) representing both usage information and semantic constraints.</Paragraph>
    <Paragraph position="2"> There are also systems aimed at extracting partial knowledge from texts, by either filling semantic templates (Hobbs et al., 1996) or by generating a set of linguistic patterns for information extraction (Harabagiu and Maiorano, 2000), to name but a few.</Paragraph>
    <Paragraph position="3"> The following sections describe in more detail the various aspects of our system, the experiments that we carried out to test the proposed algorithms and finally draw some conclusions.</Paragraph>
  </Section>
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