Supporting Start-to-Finish Development of Knowledge Bases
TLDR
Protos is a knowledge-acquisition tool that adjusts the training it expects and assistance it provides as its knowledge grows and is addressed in the description of a second tool, KI, that evaluates new information to determine its consequences for existing knowledge.Abstract:
Developing knowledge bases using knowledge-acquisition tools is difficult because each stage of development requires performing a distinct knowledge-acquisition task. This paper describes these different tasks and surveys current tools that perform them. It also addresses two issues confronting tools for start-to-finish development of knowledge bases. The first issue is how to support multiple stages of development. This paper describes Protos, a knowledge-acquisition tool that adjusts the training it expects and assistance it provides as its knowledge grows. The second issue is how to integrate new information into a large knowledge base. This issue is addressed in the description of a second tool, KI, that evaluates new information to determine its consequences for existing knowledge.read more
Citations
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Book ChapterDOI
Open Mind Common Sense: Knowledge Acquisition from the General Public
TL;DR: The first fielded system, which enabled the construction of a 450,000 assertion commonsense knowledge base, is described and evaluated and how the second-generation system addresses weaknesses discovered in the first.
Journal ArticleDOI
Concept learning and heuristic classification in weak-theory domains
TL;DR: This paper describes a successful approach to concept learning for heuristic classification that has been applied to the domain of clinical audiology and achieved a competence level equaling that of human experts and far surpassing that of other machine learning programs.
Journal ArticleDOI
Theory refinement combining analytical and empirical methods
Dirk Ourston,Raymond J. Mooney +1 more
TL;DR: A comprehensive system for automatic theory (knowledge base) refinement combining analytical and empirical methods that applies to classification tasks employing a propositional Hornclause domain theory.
Journal ArticleDOI
Using background knowledge in case-based legal reasoning: a computational model and an intelligent learning environment
TL;DR: An evaluation study showed that arguments about the significance of distinctions based on this model help predict the outcome of cases in the area of trade secrets law, confirming the quality of these arguments.
Journal ArticleDOI
Machine learning from examples: inductive and lazy methods
TL;DR: Important approaches to inductive learning methods such as propositional and relational learners, with an emphasis in Inductive Logic Programming based methods, are reported, as well as to lazy methodssuch as instance-based and case-based reasoning.
References
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Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Journal ArticleDOI
Information retrieval by constrained spreading activation in semantic networks
Paul R. Cohen,Rick Kjeldsen +1 more
TL;DR: GRANT as mentioned in this paper is an expert system for finding sources of funding given research proposals, which makes inferences about the goals of the user and thus finds information that the user did not explicitly request but that is likely to be useful.
Journal ArticleDOI
Expertise transfer and complex problems: using AQUINAS as a knowledge-acquisition workbench for knowledge-based systems
TL;DR: Aquinas, an expanded version of the Expertise Transfer System (ETS), is a knowledge-acquisition workbench that combines ideas from psychology and knowledge-based systems research to support knowledge- Acquisition tasks.
Journal ArticleDOI
Generic tasks for knowledge-based reasoning: the “right” level of abstraction for knowledge acquisition
Tom Bylander,B. Chandrasekaran +1 more
TL;DR: The proposal and the interaction problem call into question many generally held beliefs about expert systems such as the belief that the knowledge base should be separated from the inference engine.
Proceedings Article
LEAP: a learning apprentice for VLSI design
TL;DR: In this paper, the authors define Learning Apprentice Systems as the class of interactive knowledge-based consultants that directly assimilate new knowledge by observing and analyzing the problem solving steps contributed by their users through their normal use of the system.