Agile sensor tasking for CoIST using natural language knowledge representation and reasoning

2014 ◽  
Author(s):  
David Braines ◽  
Geeth de Mel ◽  
Chris Gwilliams ◽  
Christos Parizas ◽  
Diego Pizzocaro ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Natural Language ◽  
Knowledge Representation And Reasoning ◽  
Language Knowledge ◽  
Sensor Tasking

scholarly journals Uniform knowledge representation for language processing in the B2 system

1997 ◽  
Vol 3 (2) ◽  
pp. 123-145 ◽  
Author(s):  
SUSAN W. MCROY ◽  
SYED S. ALI ◽  
SUSAN M. HALLER
Keyword(s):  
Knowledge Representation ◽  
Natural Language ◽  
Bayesian Network ◽  
Language Processing ◽  
Semantic Network ◽  
Information Source ◽  
Knowledge Representation And Reasoning ◽  
Decision Support Model ◽  
Domain Specific ◽  
Management Domain

We describe the natural language processing and knowledge representation components of B2, a collaborative system that allows medical students to practice their decision-making skills by considering a number of medical cases that differ from each other in a controlled manner. The underlying decision-support model of B2 uses a Bayesian network that captures the results of prior clinical studies of abdominal pain. B2 generates story-problems based on this model and supports natural language queries about the conclusions of the model and the reasoning behind them. B2 benefits from having a single knowledge representation and reasoning component that acts as a blackboard for intertask communication and cooperation. All knowledge is represented using a propositional semantic network formalism, thereby providing a uniform representation to all components. The natural language component is composed of a generalized augmented transition network parser/grammar and a discourse analyzer for managing the natural language interactions. The knowlege representation component supports the natural language component by providing a uniform representation of the content and structure of the interaction, at the parser, discourse, and domain levels. This uniform representation allows distinct tasks, such as dialog management, domain-specific reasoning, and meta-reasoning about the Bayesian network, to all use the same information source, without requiring mediation. This is important because there are queries, such as Why?, whose interpretation and response requires information from each of these tasks. By contrast, traditional approaches treat each subtask as a “black-box” with respect to other task components, and have a separate knowledge representation language for each. As a result, they have had much more difficulty providing useful responses.

Knowledge representation for natural language processing in implemented systems

1997 ◽  
Vol 3 (2) ◽  
pp. 97-101 ◽  
Author(s):  
SYED S. ALI ◽  
ŁUCJA IWAŃSKA
Keyword(s):  
Natural Language Processing ◽  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
General Purpose ◽  
Knowledge Representation And Reasoning ◽  
Special Issue ◽  
Wide Range ◽  
Processing Mechanisms

This special issue presents the state-of-the-art in implemented, general-purpose Natural Language Processing (NLP) systems that use nontrivial Knowledge Representation and Reasoning (KRR). These systems use full-scale implementations of traditional KRR techniques as well as some newer knowledge-related processing mechanisms that have been developed specifically to meet the needs of natural language processing. The papers cover a wide range of natural language inputs, knowledge and formalisms, application domains and processing tasks, illustrating the key role that knowledge representation plays in all types of NLP systems.

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