Search (22 results, page 1 of 2)

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Abstract

Klassifikation von bibliografischen Einheiten ist für einen systematischen Zugang zu den Beständen einer Bibliothek und deren Aufstellung unumgänglich. Bislang wurde diese Aufgabe von Fachexperten manuell erledigt, sei es individuell nach einer selbst entwickelten Systematik oder kooperativ nach einer gemeinsamen Systematik. In dieser Arbeit wird ein Verfahren zur Automatisierung des Klassifikationsvorgangs vorgestellt. Dabei kommt das Verfahren des fallbasierten Schließens zum Einsatz, das im Kontext der Forschung zur künstlichen Intelligenz entwickelt wurde. Das Verfahren liefert für jedes Werk, für das bibliografische Daten vorliegen, eine oder mehrere mögliche Klassifikationen. In Experimenten werden die Ergebnisse der automatischen Klassifikation mit der durch Fachexperten verglichen. Diese Experimente belegen die hohe Qualität der automatischen Klassifikation und dass das Verfahren geeignet ist, Fachexperten bei der Klassifikationsarbeit signifikant zu entlasten. Auch die nahezu vollständige Resystematisierung eines Bibliothekskataloges ist - mit gewissen Abstrichen - möglich.

Date

22. 8.2009 19:51:28
23. 8.2009 9:46:44

Series

Zeitschrift für Bibliothekswesen und Bibliographie : Sonderband ; 96

Source

Wissen bewegen - Bibliotheken in der Informationsgesellschaft / 97. Deutscher Bibliothekartag in Mannheim, 2008. Hrsg. von Ulrich Hohoff und Per Knudsen. Bearb. von Stefan Siebert

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Date

20. 6.1999 18:40:23

Footnote

Contribution to a special issue on using context in computer applications

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Abstract

The resolution of cases in law depends on the generation of metaphors by analogy. It progresses by association, affinity and juxtaposition of two divergent ideas in an integrative approach. To argue a case, a legal expert cannot limit himself to the perception of isolated facts, but instead must find affinities between fields expressing more cohesion in law. In this paper, it is argued that the legal specialist relies on abstract categorization to discover a precedent and thereby create a metaphorical link that serves in the argumentation stage, and also later on in the resolution of the case. On this basis, a model of case reasoning is charted that integrates a model of analogical reasoning

Source

Knowledge organization. 23(1996) no.3, S.130-134

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Abstract

CBR ist eng gekoppelt mit Lernen, verstanden als Anreicherung von Wissen. Wissen wird unterteilt in Faktenwissen und Handhabungs-/Orientierungswissen. Letzteres verfügbar zu haben wird als primäres Ziel von Lernen und damit auch von CBR herausgearbeitet. Die Übertragbarkeitsproblematik, d.h. die Nutzung von gespeichertem Erfahrungswissen (alte Fälle und ihre Lösungen) zur Lösung neuer Problemstellungen wird an einem eingängigen Beispiel (Dunckers Bestrahlungsproblem illustriert). Abschließend wird der CBR-Zyklus kurz vorgestellt

Series

Fortschritte in der Wissensorganisation; Bd.4

Source

Analogie in der Wissensrepräsentation: Case-Based Reasoning und räumliche Modelle. 4. Tagung der deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Trier, 17.-20. Oktober 1995. Hrsg.: H. Czap u.a

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Abstract

Klassifikation von bibliografischen Einheiten ist für einen systematischen Zugang zu den Beständen einer Bibliothek und deren Aufstellung unumgänglich. Bislang wurde diese Aufgabe von Fachexperten manuell erledigt, sei es individuell nach einer selbst entwickelten Systematik oder kooperativ nach einer gemeinsamen Systematik. In dieser Arbeit wird ein Verfahren zur Automatisierung des Klassifikationsvorgangs vorgestellt. Dabei kommt das Verfahren des fallbasierten Schließens zum Einsatz, das im Kontext der Forschung zur künstlichen Intelligenz entwickelt wurde. Das Verfahren liefert für jedes Werk, für das bibliografische Daten vorliegen, eine oder mehrere mögliche Klassifikationen. In Experimenten werden die Ergebnisse der automatischen Klassifikation mit der durch Fachexperten verglichen. Diese Experimente belegen die hohe Qualität der automatischen Klassifikation und dass das Verfahren geeignet ist, Fachexperten bei der Klassifikationsarbeit signifikant zu entlasten. Auch die nahezu vollständige Resystematisierung eines Bibliothekskataloges ist - mit gewissen Abstrichen - möglich.

Imprint

Berlin : Humboldt-Universität / Institut für Bibliotheks- und Informationswissenschaft

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Abstract

Mangelndes methodisches Bewußtsein in der Künstlichen Intelligenz hat zur Folge, daß Methoden, die auf einer einzigen zunächst vielversprechenden Idee beruhen, zugunsten anderer fallengelassen werden, sowie sich ernste Schwierigkeiten ergeben. Doch für Methoden gilt nicht das Alles-oder-Nichts-Prinzip; Ziel muß es daher sein, verschiedene sich hinsichtlich ihrer Anwendbarkeit einander ergänzende Methoden aufeinander abzustimmen und zu einem arbeitsfähigen System zu vereinen; das trifft insbesondere auf das bereits vom Ansatz her auf Methodenvielfalt angelegte fallbezogene Schlußfolgern zu. Die von den Kognitionswissenschaften gebotenen Voraussetzungen sind jedoch für solch ein Vorhaben nicht günstig. Es herrscht ein Theorien- und Modellwirrwar, das zu einem Wirrwar von Begriffen, Sichtweisen und Verfahren geführt hat. Die vorliegende Arbeit skizziert einen Ausweg aus dieser unbefriedigenden Situation. Sie orientiert sich an den Fragen 'welche Aufgaben sollen mit fallbezogenem Schlußfolgern gelöst werden?', 'durch welche Merkmale ist dieser Ansatz charakterisiert?' und beschäftigt sich abschließend mit den sich aus den Antworten ergebenden Folgerungen

Series

Fortschritte in der Wissensorganisation; Bd.4

Source

Analogie in der Wissensrepräsentation: Case-Based Reasoning und räumliche Modelle. 4. Tagung der deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Trier, 17.-20. Oktober 1995. Hrsg.: H. Czap u.a

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Abstract

Ein wesentlicher Vorteil des CBR im Vergleich zu generativen Ansätzen ist ein geringer Bedarf an das zu formalisierende Wissen. Insbesondere ist es möglich trotz unvollständigen Anpassungswissens vollständige Lösungen zu finden. Dieser Beitrag beschreibt, wodurch sich Anpassungswissen von Regelwissen unterscheidet und wieviel Anpassungswissen man unbedingt benötigt. Die Leistungsfähigkeit eines wissensarmen CBR-Ansatzes wird am Beispiel der Anpassung toplogischer Strukturen diskutiert

Series

Fortschritte in der Wissensorganisation; Bd.4

Source

Analogie in der Wissensrepräsentation: Case-Based Reasoning und räumliche Modelle. 4. Tagung der deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Trier, 17.-20. Oktober 1995. Hrsg.: H. Czap u.a

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Abstract

Case Based Reasoning (CBR) is discussed in connection with a wide variety of knowledge aspects. Obviously knowledge acquisition under conditions of rapid and increasingly disruptive change will necessarily rely on the proper use of case experience. The following remarks emerged from practice oriented teaching/learning in the domians of social sciences, practical philosophy, didactics and epistemology. The main interest will be directed to methodical concepts of knowledge transfer, in particular to didactics and learning within the teaching/learning system

Series

Fortschritte in der Wissensorganisation; Bd.4

Source

Analogie in der Wissensrepräsentation: Case-Based Reasoning und räumliche Modelle. 4. Tagung der deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Trier, 17.-20. Oktober 1995. Hrsg.: H. Czap u.a

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Abstract

Investigates the use of formal mathematical models in the design of interactive systems and argues for the development of generic models that describe the behaviour of a class of interactive systems. It is possible to construct a generic model of a class of interactive systems at an intermediate level of abstraction. Such a model would offer wider reusability than detailed specifications of a single system, but greater expressiveness and support for software development than fully generate abstract models. Reviews a number of existing models in the literature and presents a generic model of interactive case memories, a class of systems used in case-based reasoning

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Abstract

Case Based Reasoning is a very important research trend in Artificial Intelligence and can be a powerful approach in the solution of complex problems characterized by heterogeneous knowledge. In this paper we present an ongoing research project where CBR is exploited to support the identification of enterprises potentially going to bankruptcy, through a comparison of their balance indexes with the ones of similar and already closed firms. In particular, the paper focuses on how developing similarity measures for strings can be profitably supported by metadata models of case structures and semantic methods like Query Expansion.

Pages

S.22-29

Series

Communications in computer and information science; 478

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Abstract

In most search systems, mapping queries with documents employs techniques such as vector space model, naïve Bayes, Bayesian theorem etc. to classify resulting documents. In this research studies, we are proposing the use of the concept of basic categories to representing the user's level of knowledge based on the concepts he employed during his search activities, so that the system could propose adapted results based on the observed user's level of knowledge. Our hypothesis is that this approach will enhance the decision support system for solving decisional problems in which information retrieval constitutes the backbone technical problem.

Series

Advances in knowledge organization; vol. 14

Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

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Abstract

Introduces a new method for continuous case-based reasoning, and discusses its applications to the dynamic selection, modification and acquisition of robot bahaviours in an autonomous navigation system, SINS (self-improving navigation system): The computer program and the underlying method are systematically evaluated through statistical analysis of results from several empirical studies. Discusses case-based reasoning issues addressed by this research

Date

6. 3.1997 16:22:15

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Abstract

Proposes a new architecture combining rule-based reasoning (RBR), case based reasoning (CBR) and knowledge acquisition technology in a system which solves pattern search problems. Details the pattern search problem, the system architecture and functions, error repair method via case-based reasoning, the knowledge acquisition method, system evaluation, and discusses related work

Date

6. 3.1997 16:22:15

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Date

6. 3.1997 16:22:15

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Footnote

Rez. in: JASIS 49(1998) no.8, S.762-764 (P.G. Underwood)

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Abstract

This paper reportes a study that investigated the effects of conceptual description and search practice on users' mental models and information seeking in a case-based reasoning retrieval (CBR) system with a best match search mechanism. This study also found examined how the presence of a mental model affects the users' search performance and satisfaction in this system. The results of this study revealed that the conceptual description and search practice treatments do not have significantly different effects on the types of user's mental models, search correctness, and search satisfaction. However, the search practice group spent significantly less time than the conceptual description group in finding the results. Qualitative analysis for the subjects' post mental models revealed that subjects in the conceptual description group seem to have more complete mental models of the best match system than those in the search practice group. This study also that subjects with the best match mental models have significantly higher search correctness and search result satisfaction than subjects without the best match mental models. However, the best match mental models do not guarantee less search time in finding the results. This study did not find a significant correlation among search time, search correctness and search satisfaction. The study concludes with suggestions for future research and implications for system developers who are interested in CBR retrieval systems.

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Abstract

Reports on a methodology that lets case-based reasoning use decision-theoretic approaches to deal with unknown proble features and how to make decisions in the presence of these unknowns. Implements the methodology in a case-based design assistant that helps chemists design pharmaceuticals

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Abstract

Gives an overview of CBR with emphasis on European ctivites in the field. Identifies major open problems of CBR associated with: retrieval/selection, memory organization, matching, adaptation/evaluation, forgetting and integration with other techniques

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Abstract

We present 2 techniques for reasoning from cases to solve classification tasks: induction and case-based reasoning. We contrast the 2 technologies (that are often confused) and show how they complement each other. Based on this, we describe how they are integrated in one single platform for reasoning from cases: the INRECA system

Series

Studies in classification, data analysis, and knowledge organization

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Abstract

Presents a fuzzy constraint satisfaction system which can be used in a distributive environment where large problems can be broken down into smaller constraint networks for easier processing. Identifies contexts which exist within the constraint satisfaction system. Context based reasoning is identified both within and among constraint networks. Outlines the motviation behind the research and describes the fuzzy constraint satisfaction system FuzCon. Points out 3 mappings of the context-based reasoning 'ist' operator to fuzzy constraints and presents an example of designing a printed wiring board

Footnote

Contribution to a special issue on using context in computer applications