Topic
Knowledge representation and reasoning
About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².
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TL;DR: This work proposes the mixture of experts for case-based reasoning (MOE4CBR), a method that combines an ensemble of CBR classifiers with spectral clustering and logistic regression that achieves higher prediction accuracy and leads to the selection of a subset of features that have meaningful relationships with their class labels.
Abstract: Case-based reasoning (CBR) is a suitable paradigm for class discovery in molecular biology, where the rules that define the domain knowledge are difficult to obtain and the number and the complexity of the rules affecting the problem are too large for formal knowledge representation. To extend the capabilities of CBR, we propose the mixture of experts for case-based reasoning (MOE4CBR), a method that combines an ensemble of CBR classifiers with spectral clustering and logistic regression. Our approach not only achieves higher prediction accuracy, but also leads to the selection of a subset of features that have meaningful relationships with their class labels. We evaluate MOE4CBR by applying the method to a CBR system called TA3 - a computational framework for CBR systems. For two ovarian mass spectrometry data sets, the prediction accuracy improves from 80 percent to 93 percent and from 90 percent to 98.4 percent, respectively. We also apply the method to leukemia and lung microarray data sets with prediction accuracy improving from 65 percent to 74 percent and from 60 percent to 70 percent, respectively. Finally, we compare our list of discovered biomarkers with the lists of selected biomarkers from other studies for the mass spectrometry data sets.
97 citations
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01 Mar 1987TL;DR: The problem of modeling knowledge about the fault behavior of a system and utilizing this model for reasoning about and diagnosing failures is addressed and a solution that merges graph and fault-tree-based failure analysis with rule-oriented reasoning is presented.
Abstract: The problem of modeling knowledge about the fault behavior of a system and utilizing this model for reasoning about and diagnosing failures is addressed. A solution that merges graph and fault-tree-based failure analysis with rule-oriented reasoning is presented. Failure analysis is divided into two phases, a failure source location phase and a failure cause identification phase. Each phase consists of a failure model and a process that operates on it. The failure models for the first and second phases are based on lesel-structured fault propagation digraphs and augmented fault trees, respectively. The augmented fault tree (AFT) is a conceptual structure that encodes probabilistic, temporal, and heuristic information in addition to the causal aspects of failures modeled by conventional fault trees. The two models are combined to form a novel hierarchical failure knowledge representation scheme. Upper levels of this hierarchy are made up of the fault propagation digraphs. Each level represents a view of the system under a particular granularity, and the granularity increases with levels. This feature permits control over the resolution of fault diagnosis. The lowest level consists of a set of cause-consequence knowledge bases containing production rules. These production rules are derived from augmented fault trees and represent the cause-effect relations among failure events that lead to the corresponding subsystem's failure. A knowledge acquisition procedure to generate these failure models and failure analysis processes that operate on them are described. The methodology proposed is inherently parallel as the processes may operate on different levels independently.
97 citations
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TL;DR: The developed Feature-Oriented Modelling and Planning System, using artificial intelligence techniques, is discussed and the developed Inspection knowledge representation and planning logic are described and illustrated with examples.
97 citations
16 Jun 2008
TL;DR: These investigations integrate the experience gained through its use in industrial and academic projects, the progress of natural language processing as well as the evolution of the ontology engineering to present the kind of conceptual model built with this method, and its knowledge representation.
Abstract: Designed about ten years ago, the TERMINAE method and workbench for ontology engineering from texts have been going on evolving since then. Our investigations integrate the experience gained through its use in industrial and academic projects, the progress of natural language processing as well as the evolution of the ontology engineering. Several new methodological guidelines, such as the reuse of core ontologies, have been added to the method and implemented in the workbench. It has also been modified in order to be compliant to some recent standards such as the OWL knowledge representation.
The paper recalls the terminology engineering principles underlying TERMINAE and comments its originality. Then it presents the kind of conceptual model that is built with this method, and its knowledge representation. The method and the support provided by the workbench are detailed and illustrated with a case-study in law. With regard to the state of the art, TERMINAE is one of the most supervised methods in the trend of ontology learning. This option raises epistemological issues about how language and knowledge can be articulated and the distance that separate formal ontologies from learned conceptual models.
97 citations
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TL;DR: An architecture that integrates disparate reasoning, planning, sensation and mobility algorithms by composing them from strategies for managing mental simulations is described, demonstrating that knowledge representation and inference techniques enable more complex and flexible robot behavior.
97 citations