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<Paper uid="H86-1003">
  <Title>Overview of the TACITUS Project</Title>
  <Section position="4" start_page="19" end_page="20" type="metho">
    <SectionTitle>
3 Commonsense Knowledge
</SectionTitle>
    <Paragraph position="0"> Our aim in this phase of the project is to encode large amounts of commonsense knowledge in first-order predicate calculus in a way that can be used for knowledge-based processing of natural language discourse. Our approach is to define rich core theories of various domains, explicating their basic ontologies and structure, and then to define, or at least to characterize, various English words in terms of predicates provided by these core theories. So far, we have alternated between working from the inside out, from explications of the core theories to characterizations of the words, and from the outside in, from the words to the core theories. Thus, we first proceeded from the outside in by examining the concept of ~wear', as in &amp;quot;worn bearings&amp;quot;, seeking to define ~wear&amp;quot;, and then to define the concepts we defined Uwear&amp;quot; in terms of, pushing the process back to basic concepts in the domains of space, materials, and force, among others. We then proceeded from the inside out, trying to flesh out the core theories of these domains, as well as the domains of scalar notions, time, measure, orientation, shape, and functionality. Then to test the adequacy of these theories, we began working from the outside in again, spending some time defining, or characterizing, the words related to these domains that occurred in our target set of casreps. We are now working from the inside out again, going over the core theories and the definitions with a fine-tooth comb, checking manually for consistency and adequacy and proving simple consequences of the axioms on the KADS theorem-prover. This work is described in an enclosed publication \[1\].</Paragraph>
  </Section>
  <Section position="5" start_page="20" end_page="20" type="metho">
    <SectionTitle>
4 Domain Knowledge
</SectionTitle>
    <Paragraph position="0"> In all of our work we are seeking general solutions that can be used in a wide variety of applications. This may seem impossible for domain knowledge. In our particular case, we must express facts about the starting air compressor of a ship. It would appear difficult to employ this knowledge in any other application. However, our approach makes most of our work even in this area relevant to many other domains. We are specifying a number of &amp;quot;abstract machines&amp;quot; or &amp;quot;abstract systems&amp;quot;, in levels, of which the particular device we must model is an instantiation. We define, for example, a &amp;quot;closed producer-consumer system&amp;quot;. We then define a &amp;quot;closed clean fluid producer-consumer system&amp;quot; as a closed producer-consumer system with certain additional properties, and at one more level of specificity, we define a &amp;quot;pressurized lube-oil system&amp;quot;. The specific lube-oil system of the starting air compressor, with all its idiosyncratic features, is then an instantiation of the last of these. In this way, when we have to model other devices, we can do so by defining them to be the most specific applicable abstract machine that has been defined previously, thereby obviating much of the work of specification. An electrical circuit, for example, is also a closed producer-consumer system.</Paragraph>
  </Section>
  <Section position="6" start_page="20" end_page="21" type="metho">
    <SectionTitle>
5 Deduction
</SectionTitle>
    <Paragraph position="0"> The deduction component of the TACITUS system is the KLAUS Automated Deduction System (KADS), developed as part of the KLAUS project for research on the interactive acquisition and use of knowledge through natural language. Its principal inference operation is nonclausal resolution, with possible resolution operations encoded in a connection graph.</Paragraph>
    <Paragraph position="1"> The nonclausal representation eliminates redundancy introduced by translating formulas to clause form, and improves readability as well. Special control connectives can be used to restrict use of the formulas to either forward chaining or backward chaining. Evaluation functions determine the sequence of inference operations in KADS. At each step, KADS resolves on the highest-rated link. The resolvent is then evaluated for retention and links to the new formula are evaluated for retention and priority. KADS supports the incorporation of theories for more efficient deduction, including deduction by demodulation, associative and commutative unification, many-sorted unification, and theory resolution. The last of these has been used for efficient deduction using a sort hierarchy. Its efficient methods for performing some reasoning about sorts and equality and the facility for or- null dering searches by means of an evaluation function make it particularly well suited for the kinds of deductive processing required in a knowledge-based natural language system.</Paragraph>
  </Section>
  <Section position="7" start_page="21" end_page="23" type="metho">
    <SectionTitle>
6 Local Pragmatics
</SectionTitle>
    <Paragraph position="0"> We have begun to formulate a general approach to several problems that lie at the boundary between semantics and pragmatics. These are problems that arise in single sentences, even though one may have to look beyond the single sentence to solve them. The problems are metonymy, reference, the interpretation of compound nominals, and lexical and syntactic ambiguity.</Paragraph>
    <Paragraph position="1"> All of these may be called problems in &amp;quot;local pragmatics&amp;quot;. Solving them constitutes at least part of what the interpretation of a text is. We take it that interpretation is a matter of reasoning about what is possible, and therefore rests fundamentally on deductive operations. We have formulated very abstract characterizations of the solutions to the local pragmatics problems in terms of what can be deduced from a knowledge base of commonsense and domain knowledge. In particular, we have devised a general algorithm for building an expression from the logical form of a sentence, such that a constructive proof of the expression from the knowledge base will constitute an interpretation of the sentence. This can be illustrated with the sentence from the casreps Disengaged compressor after lube oil alarm.</Paragraph>
    <Paragraph position="2"> To resolve the reference of &amp;quot;alarm&amp;quot; one must prove constructively the ex-</Paragraph>
    <Paragraph position="4"> To resolve the implicit relation between the two nouns in the compound nominal &amp;quot;lube oil alarm&amp;quot; (where &amp;quot;lube oil&amp;quot; is taken as a multiword), one must prove constructively from the knowledge base the existence of some possible relation, which we may call nn, between the entities referred to by the nouns:</Paragraph>
    <Paragraph position="6"> A metonymy occurs in the sentence in that &amp;quot;after&amp;quot; requires its object to be an event, whereas the explicit object is a device. To resolve a metonymy that occurs when a predicate is applied to an explicit argument that fails to  satisfy the constraints imposed by the predicate on its argument, one must prove constructively the possible existence of an entity that is related to the explicit argument and satisfies the constraints imposed by the predicate. Thus, the logical form of the sentence is modified to ... A after(d,e) A q(e,x) A alarm(x) A ...</Paragraph>
    <Paragraph position="7"> and the expression to be proved constructively is</Paragraph>
    <Paragraph position="9"> In the most general approach, nn and q are predicate variables. In less ambitious approaches, they can be predicate constants, as illustrated below.</Paragraph>
    <Paragraph position="10"> These are very abstract and insufficiently constrained formulations of solutions to the local pragmatics problems. Our further research in this area has probed in four directions.</Paragraph>
    <Paragraph position="11"> (1) We have been examining various previous approaches to these problems in linguistics and computational linguistics, in order to reinterpret them into our framework. For example, an approach that says the implicit relation in a compound nominal must be one of a specified set of relations, such as &amp;quot;part-of', can be captured by treating &amp;quot;nn&amp;quot; as a predicate constant and by including in the knowledge base axioms like (v x, y)part-of(y, x) ..(x, y) In this fashion, we have been able to characterize succinctly the most common methods used for solving these problems in previous natural language systems, such as the methods used in the TEAM system.</Paragraph>
    <Paragraph position="12"> (2) We have been investigating constraints on the most general formulations of the problems. There are general constraints, such as the Minimality Principle, which states that one should favor the minimal solution in the sense that the fewest new entities and relations must be hypothesized. For example, the argument-relation pattern in compound nominals, as in &amp;quot;lube oil pressure&amp;quot;, can be seen as satisfying the Minimality Principle, since the implicit relation is simply the one already given by the head noun. In addition, we are looking for constraints that are specific to given problems. For example, whereas whole-part compound nominals, like &amp;quot;regulator valve&amp;quot;, are quite common, part-whole compound nominals seem to be quite rare.</Paragraph>
    <Paragraph position="13"> This is probably because of a principle that says that noun modifiers should further restrict the possible reference of the noun phrase, and parts are common to too many wholes to perform that function.</Paragraph>
    <Paragraph position="14">  (3) A knowledge base contains two kinds of knowledge, &amp;quot;type&amp;quot; knowledge  about what kinds of situations are possible, and &amp;quot;token&amp;quot; knowledge about what the actual situation is. We are trying to determine which of these kinds of knowledge are required for each of the pragmatics problems. For example, reference requires both type and token knowledge, whereas most if not all instances of metonymy seem to require only type knowledge.</Paragraph>
    <Paragraph position="15"> (4) At the most abstract level, interpretation requires the constructive proof of a single logical expression consisting of many conjuncts. The deduction component can attempt to prove these conjuncts in a variety of orders. We have been investigating some of these possible orders. For example, one plausible candidate is that one should work from the inside out, trying first to solve the reference problems of arguments of predications before attempting to solve the compound nominal and metonymy problems presented by those predications. In our framework, this is an issue of where subgoals for the deduction component should be placed on an agenda.</Paragraph>
  </Section>
  <Section position="8" start_page="23" end_page="23" type="metho">
    <SectionTitle>
7 Implementation
</SectionTitle>
    <Paragraph position="0"> In our implementation of the TACITUS system, we are beginning with the minimal approach and building up slowly. As we implement the local pragmatics operations, we are using a knowledge base containing only the axioms that are needed for the test examples. Thus, it grows slowly as we try out more and more texts. As we gain greater confidence in the pragmatics operations, we will move more and more of the axioms from our commonsense and domain knowledge bases into the system's knowledge base. Our initial versions of the pragmatics operations are, for the most part, fairly standard techniques recast into our abstract framework. When the knowledge base has reached a significant size, we will begin experimenting with more general solutions and with various constraints on those general solutions.</Paragraph>
  </Section>
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