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<Paper uid="N03-1012">
  <Title>Semantic Coherence Scoring Using an Ontology</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
3 The Knowledge Base
</SectionTitle>
    <Paragraph position="0"> In this section, we provide a description of the pre-existing knowledge source employed by ONTOSCORE, as far as it is necessary to understand the empirical data generated by the system. It is important to note that the ontology employed in this evaluation existed already and was crafted as a general knowledge representation for various processing modules within the system.3 Ontologies have traditionally been used to represent general and domain specific knowledge and are employed for various natural language understanding tasks, e.g. semantic interpretation (Allen, 1987). We propose an additional way of employing ontologies, i.e. to use the knowledge modeled therein as the basis for evaluating the semantic coherence of sets of concepts.</Paragraph>
    <Paragraph position="1"> The system described herein can be employed independently of the specific ontology language used, as the underlying algorithm operates only on the nodes and named edges of the directed graph represented by the ontology.</Paragraph>
    <Paragraph position="2"> The specific knowledge base, e.g. written in DAML+OIL or OWL,4 is converted into a graph, consisting of: 3Alternative knowledge representations, such as WORD-NET, could have been employed in theory as well, however most of the modern domains of the system, e.g. electronic media or program guides, are not covered by WORDNET.</Paragraph>
    <Paragraph position="3"> 4DAML+OIL and OWL are frequently used knowledge modeling languages originating in W3C and Semantic Web a0 the class hierarchy, with each class corresponding to a concept representing either an entity or a process; a0 the slots, i.e. the named edges of the graph corresponding to the class properties, constraints and restrictions. null The ontology employed herein has about 730 concepts and 200 relations. It includes a generic top-level ontology whose purpose is to provide a basic structure of the world, i.e. abstract classes to divide the universe in distinct parts as resulting from the ontological analysis. The top-level was developed following the procedure outlined in Russell and Norvig (1995).</Paragraph>
    <Paragraph position="4"> In the view of the ontology employed herein, Role is the most general class in the ontology and represents a role that any entity or process can perform. It is divided into Event and Abstract Event. Event is used to describe a kind of role any entity or process may have in a &amp;quot;real&amp;quot; situation or process, e.g. a building or an information search. It is contrasted with Abstract Event, which is abstracted from a set of situations and processes. It reflects no reality and is used for the general categorization and description, e.g. Number, Set, Spatial Relation. There are two kinds of events: Physical Object and Process.</Paragraph>
    <Paragraph position="5"> The class Physical Object describes any kind of objects we come in contact with - living as well as non-living - having a location in space and time in contrast to abstract objects. These objects refer to different domains, such as Sight and Route in the tourism domain, Av Medium and Actor in the TV and cinema domain, etc., and can be associated with certain relations in the processes via slot constraint definitions.</Paragraph>
    <Paragraph position="6"> The modeling of Process as a kind of event that is continuous and homogeneous in nature, follows the frame semantic analysis used for generating the FRAMENET data (Baker et al., 1998). Currently, there are four groups of processes (see Figure 1): a0 General Process, a set of the most general processes such as duplication, imitation or repetition processes; a0 Mental Process, a set of processes such as cognitive, emotional or perceptual processes; a0 Physical Process, a set of processes such as motion, transaction or controlling processes; a0 Social Process, a set of processes such as communication or instruction processes.</Paragraph>
    <Paragraph position="7"> Let us consider the definition of the Information Search Process in the ontology. It is modeled as a projects. For more detail, see www.w3c.org.</Paragraph>
    <Paragraph position="8"> subclass of the Cognitive Process, which is a sub-class of the Mental Process and inherits the following slot constraints: a0 begin time, a time expression indicating the starting time point; a0 end time, a time expression indicating the time point when the process is complete; a0 state, one of the abstract process states, e.g. start, continue, interrupt, etc.; a0 cognizer, filled with a class Person including its subclasses.</Paragraph>
    <Paragraph position="9"> Information Search Process features one additional slot constraint, piece-of-information. The possible slot-fillers are a range of domain objects, e.g. Sight, Performance, or whole sets of those, e.g. Tv Program, but also processes, e.g. Controlling Tv Device Process. This way, an utterance such as:  can be mapped onto Information Search Process, which has an agent of type User and has a piece of information of type Controlling Tv Device Process.</Paragraph>
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