Abstract not found

Semantic similarity plays an important role in geographic information systems as it supports the identification of objects that are conceptually close, but not identical. Similarity assessments are particularly important for retrieval of geospatial data in such settings as digital libraries, heterogeneous databases, and the World Wide Web. Although some computational models for semantic similarity assessment exist, these models are typically limited by their inability to handle such important cognitive properties of similarity judgments as their inherent asymmetry and their dependence on context. This paper defines the Matching-Distance Similarity Measure (MDSM) for determining semantic similarity among spatial entity classes, taking into account the distinguishing features of these classes (parts, functions, and attributes) and their semantic interrelations (is-a and part-whole relations). A matching process is combined with a semantic-distance calculation to obtain asymmetric values of similarity that depend on the degree of generalization of entity classes. MDSM's matching process is also driven by contextual considerations, where the context determines the relative importance of distinguishing features. Based on a human-subject experiment, MDSM results correlate well with people's judgments of similarity. When contextual information is used for determining the importance of distinguishing features, this correlation increases; however, the major component of the correlation between MDSM results and people's judgments is due to a detailed definition of entity classes.

This paper begins with a brief review of SME and MAC/FAC, our simulations of matching and retrieval. Next I lay out several arguments for exploring analogy in the large, including why it is now very feasible and what we can learn by such explorations. A new constraint on cognitive simulations, the Integration Constraint, is proposed: A cognitive simulation of some aspect of analogical processing should be usable as a component in larger-scale cognitive simulations. I believe that the implications of this new constraint for cognitive simulation of analogy are far-reaching. After that, two explorations of larger-scale phenomena are described. First, I describe a theoretical framework in which we model common sense reasoning as an interplay of analogical and first-principles reasoning. Second, I describe how SME and MAC/FAC have been used in a case-based coach that is accessible to engineering thermodynamics students worldwide via electronic mail. These examples show that exploring analogy in the large can provide new insights and new challenges to our simulations. Finally, the broader implications of this approach are discussed.

Sketch maps are an important spatial representation used in many geospatial reasoning tasks.

This article describes the path-mapping theory of how humans integrate analogical mapping and general problem solving. The theory posits that humans represent analogs with declarative roles, map analogs by lower-level retrieval of analogous role paths, and coordinate mappings with higher-level organizational knowledge. Implemented in the ACT-R cognitive architecture, the path-mapping theory enables models of analogical mapping behavior to incorporate and interface with other problem-solving knowledge. Path-mapping models thus can include task-specific skills such as encoding analogs or generating responses, and can make behavioral predictions at the level of real-world metrics such as latency or correctness. We show that the path-mapping theory can successfully account for the major phenomena addressed by previous theories of analogy. We also describe a path-mapping model that can account for subjects’ incremental eye-movement and typing behavior in a story-mapping task. We discuss extensions and implications of this work to other areas of analogy and problem-solving research.

This paper describes an analogy ontology, a formal representation of some key ideas in analogical processing, that supports the integration of analogical processing with first-principles reasoners. The ontology is based on Gentner's structure-mapping theory, a psychological account of analogy and similarity. The semantics of the ontology are enforced via procedural attachment, using cognitive simulations of structure-mapping to provide analogical processing services. Queries that include analogical operations can be formulated in the same way as standard logical inference, and analogical processing systems in turn can call on the services of first-principles reasoners for creating cases and validating their conjectures.

The ways in which analogy was used in a nonexperimental environment—politics—was investigated. We used the framework developed in analogy research to analyze the selection of analogical sources in political discourse. We took all the analogies reported in newspapers during the final week of a referendum campaign in Canada and analyzed the features of the different analogies used. We identified 234 analogies and analyzed the range over which analogies were used, semantic categories of analogies, goals of the analogizer, and emotional connotation of the analogies. Our results reveal that analogy was frequently used, that over two-thirds of the analogical sources were nonpolitical, and that many of the sources had strong emotional connotations. Furthermore, the goal of the analogizer influenced the selection of sources. We conclude that characteristics of the audience and emotionality of the source analog are important features in the selection of source analogs.

Most CBR systems rely on a fixed library of cases, where each case consists of a set of facts specified in advance. This paper describes techniques for dynamically extracting cases for analogical reasoning from general-purpose knowledge bases, and dynamically expanding them during the course of analogical reasoning. These techniques have several advantages: (1) Knowledge authoring is simplified, since facts can be added without regard to which case(s) they will be used in. (2) Reasoning is more efficient, since task constraints can be used during case extraction to focus on facts likely to be relevant. (3) Larger problems can be tackled, since cases can be dynamically expanded with more details during the matching process itself, rather than starting with completely detailed cases. We describe algorithms for case extraction and case expansion, including how a version of the Structure-Mapping Engine (SME) has been modified to incorporate this new matching technique. The...

Introduction There are currently two theories about how children acquire a language. The first is generative grammar, according to which all human children innately possess a universal grammar, abstract enough to structure any language of the world. Acquisition then consists of two processes: (1) acquiring all the words, idioms, and quirky constructions of the particular language being learned (by `normal' processes of learning); and (2) linking the particular language being learned to the abstract universal grammar. Because it is innate, universal grammar does not develop ontogenetically but is the same throughout the lifespan -- this is the so-called continuity assumption (Pinker, 1984). This assumption allows generativists to use adult-like formal grammars to describe children's language and so to assume that the first time a child utters, for example, "I wanna play", she has an adult-like understanding of infinitival complement sentences and so can generate `similar' infinitival c

Many scientific fields continue to explore cognition related to Theory of Mind abilities, where people reason about the mental states of themselves and others. Experimental and theoretical approaches to this problem have largely avoided issues concerning the contents of representations employed in this class of reasoning. In this paper, we describe a new approach to the investigation of representations related to Theory of Mind abilities that is based on the analysis of commonsense strategies. We argue that because the mental representations of strategies must include concepts of mental states and processes, the large-scale analysis of strategies can be informative of the representational scope of Theory of Mind abilities. The results of an analysis of this sort are presented as a description of thirty representational areas that organize the breadth of Theory of Mind concepts. Implications for Theory Theories and Simulation Theories of Theory of Mind reasoning are discussed.