Heather D. Pfeiffer

Conceptual Structures at Work

In Peirce’s account of the normative sciences, logical validity and truth ultimately rely upon – though they are not reduced to – ethical and esthetic insight. An examination of the relations among critical esthetics, ethics and logic in Peirce’s system suggests a possible account of the logic of creative discovery, which Peirce identified as abductive inference.

Analyzing Clusters and Constellations from Untwisting shortened links on Twitter using Conceptual Graphs

The analysis of big data, although potentially a very rewarding task, can present difficulties due to the complexity inherent to such datasets. We suggest that conceptual graphs provide a mechanism for representing knowledge about a domain that can also be used as a useful scaffold for big data analysis. Conceptual graphs may be used as a means to collaboratively build up a robust model forming the skeleton of a data analysis project. This paper describes a case study in which conceptual graphs were used to underpin an exploration of a corpus of tweets relating to the Transportation Security Administration (TSA). Through this process we will demonstrate the emerging model built up of the data landscape involved and of the business structures that underlie the technical frameworks relied upon by microblogging software.

Temporal, spatial, and constraint handling in the Conceptual Programming environment, CP

The goal of logic synthesis is to obtain high-quality designs from specifications. Current approaches to logic synthesis often trade off design quality for technology independence. In this paper, we present a model of logic synthesis that uses technology-specific design rules and extends rule-based search to functional decomposition and technology mapping. While this model improves design quality by taking advantage of the target technology, it is not robust to technology changes. To improve robustness, we augment the model with two learning components: one for acquiring rules that make use of physical cells in a technology library, and another for acquiring rules that make use of appropriate design styles. These components are related to work in the learning of macro-operators and explanation-based learning.

Games of inquiry for collaborative concept structuring

Googles project to digitize five of the worlds greatest libraries will dramatically extend their search engine reach in the future. Current search-engine philosophy, which asserts that ”any search starts with a question to be answered,” will need to be advanced in terms of Peirces philosophy: ”Any inquiry begins by creating an hypothesis to be tested, or with abduction.” As conceptual structures researchers prepare to meet access challenges in the world of large Internet knowledge stores, they have a solid foundation in Peirces theorized stages of inquiry: abduction, deduction, and induction. To indicate how conceptual structures tools must augment collaborative, Internet-based inquiry, we imagine a future scenario in the context of a user-centered testbed, where Peirce scholars apply Peirces pragmatic theory in their complex manuscript reconstruction work. We suggest that games of inquiry can be developed to formalize user collaboration and technology needs, for improved specification of tool requirements in the testbed context.

Visual representation of procedural knowledge

Traditionally, knowledge representation (KR) languages have declarative semantics based on classical logic and have a concrete syntax that is textual. Conversely, programming languages (PL) have mainly procedural semantics and are represented in visual terms. Moreover, PLs can only represent procedures at a very low level. Our conceptual programming language (CPL) bridges this gap, while still retaining visual appeal. It has both the declarative semantics of a KR language and the procedural semantics of a PL. CPL is a visual language for expressing procedural knowledge explicitly as programs.

A Comparison of Different Conceptual Structures Projection Algorithms

Knowledge representation (KR) is used to store and retrieve meaningful data. This data is saved using dynamic data structures that are suitable for the style of KR being implemented. The KR allows the system to manipulate the knowledge in the data by using reasoning operations. The data structure, together with the contents of the transformed knowledge, is known as the knowledge base (KB). An algorithm and the associated data structures make up the reasoning operation, and the performance of this operation is dependent on the KB. In this paper, the basic reasoning operation for a query-answer system, projection, is explored using different theoretical algorithms. Within this discussion, the associated algorithms will be using different KBs for their Conceptual Graph (CG) knowledge representation. The basic projection algorithm defined using the CG representation is looking for a graph morphism of a query graph onto a graph from the KB. The overall running time for the projection operation is known to be a NP class problem; however, by modifying the algorithm, taking into account the associated KB, the actual time needed for discovering and creating the projection/s can be improved. In fact, a new projection algorithm will be defined that, given a typical query onto a carefully defined KB, presents a running time for the actual projection that only grows with the number of projections present.

An Exportable CGIF Module from the CP Environment: A Pragmatic Approach

We have upgraded the Conceptual Programming Environment (CP) from a single standalone application to a set of component modules to increase flexibility of the environment and to allow any one of the modules to be used by applications outside of the environment. This allows the CP modules, in particular CGIF, to be tested pragmaticly in real applications. The CGIF module is encapsulated as a component and has been given an API specification. This module implements the NMSU modified ICCS2001 version of the CGIF interchange format for Conceptual Graphs (CGs) that is part of the dpANS and is setup to link with applications written in C, C++, Java\(^{\textrm{TM}}\)and Visual Basic 6.0. Communication is possible to all these languages by designing the API of the module so that it can accept either standard C string or Unicode string within the function calls. Since modules are flexible units of code, the components of the CP Environment have been tested for use under most versions of Microsoft Windows and different flavors of Linux.

Collaboratory testbed partnerships as a knowledge capture challenge

The difficulty of maintaining effective testbed partnerships, among users of computer-based tools and builders of those tools, has been an obstacle to the success of science collaboratories. Testbed partnerships are as essential for knowledge-tool advancement as that advancement is to the support of effective partnerships, because knowledge tools must be instituted in a context that truly augments (rather than merely tries to replicate) human inference in collaborative contexts. We propose a game framework for engaging content-tool users and context-tool developers in testbed partnerships, and an architecture for modular knowledge-tool integration, as initial steps in the effective advancement of knowledge capture and improvement of testbed partnerships.

e-MGR: an architecture for symbolic plasticity

Abstract The e-MGR architecture was designed for symbolic problem solving in task environments where data are noisy and problems are ill-defined. e-MGR is an operator-based system which integrates problem-solving ideas from symbolic artificial intelligence (AI) and adaptive systems research.

Knowledge representation and control in “gm1”, an automated DNA sequence analysis system based on the MGR architecture

Abstract The identification of genes by DNA sequence analysis is a formally unspecified pattern recognition problem. Genes are identified in practice by constructing and evaluating models that represent the spatial relations between a number of components that can be identified by pattern matching. This is currently done interactively, with the aid of a variety of pattern matching and statistical analysis tools. gm1 automates gene identification by integrating the application of these tools with automated model generation. Models of genes are constructed by a task-specific algorithm implemented using the MGR architecture, a general automated problem solving architecture. The development of gm1 demonstrates the versatility of the MGR architecture as a tool for building automated systems for scientific data analysis.