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<Paper uid="P87-1005">
  <Title>AN ENVIRONMENT FOR ACQUIRING SEMANTIC INFORMATION</Title>
  <Section position="11" start_page="37" end_page="38" type="concl">
    <SectionTitle>
6 Concluding Remarks
</SectionTitle>
    <Paragraph position="0"> Our approach to semantic knowledge acquisition: 1) is in the context of a general purpose NL interface rather than one that accesses only databases, 2) employs a knowledge representation formalism with limited inferencing capabilities, 3) assumes a trained person but not an AI expert, and 4) provides a cornplete environment for not only acquiring semantic knowledge, but also maintaining and editing it in a consistent knowledge base. This section comments on what we have learned thus far about the point of view espoused above.</Paragraph>
    <Paragraph position="1"> First, we have transferred the IRUS natural language interface, which includes IRACQ, to the staff of the Naval Ocean Systems Center. The person in charge of the effort at NOSC has a master's degree in linguistics and had some familiarity with natural language processing before the effort started. She received three weeks of hands-on experience with IRUS at BBN in 1985, before returning to NOSC where she trained a few part-time employees who are computer science undergraduates. Development of the dictionary and IRules for the Fleet Command Center Battle Management Program (FCCBMP), a large Navy application \[23\], has been performed exclusively by NOSC since August, 1986. Currently, about 5000 words and 150 IRules have been defined.</Paragraph>
    <Paragraph position="2"> There are two strong positive facts regarding IRACQ's generality. First, IRUS accesses both a large relational data base and an applications package in the FCCBMP. Only one set of IRules is used, with no cleavage in that set between IRules for the two applications. Second, the same software has been useful for two different versions of IRUS. One employs MRL \[29\], a procedural first order logic, as the semantic representation of inputs; the second employs IL, a higher-order intensional logic. Since the IRules define selectional restrictions, and since the Davidson-like representation (see section 3) is used in both cases, IRACQ did not have to be changed; only the general procedures for generating quantifiers, scoping decisions, treatment of tense, etc. had to be revised in IRUS. Therefore, a noteworthy degree of generality has been achieved.</Paragraph>
    <Paragraph position="3"> Our key knowledge representation decisions were the treatment of events and states of affairs, and the use of NIKL to store and reason about axioms concerning the predicates of our logic. This strongly influenced the style and questions of our semantic acquisition process. For example, IRACQ is able to propose a set of predicates that is consistent with the domain model to use for the interpretation of an input phrase. We believe representation decisions must dictate much of an acquisition scenario no matter what the decisions are. In addition, the limited knowledge representation and inference techniques of NIKL deeply affected other parts of our NLI, particulariy in the translation from conceptually-oriented domain predicates to predicates of the underlying systems. null The system does provide an initial version of a complete environment for creating and maintaining semantic knowledge. The result has been very desirable compared to earlier versions of IRACQ and IRUS that did not have such debugging aids nor integration with tools for acquiring and maintaining the  domain model. We intend to integrate the various acquisition, consistency, editing, and maintenance aids for the various knowledge bases even further.</Paragraph>
  </Section>
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