The Event Bush Method in the Light of Typed Graphs Illustrated by Common Sense Reasoning

2018 ◽  
pp. 320-353 ◽  
Author(s):  
Uwe Wolter ◽  
Olga Korableva ◽  
Nikita Solovyov
Keyword(s):  
Common Sense ◽  
Knowledge Engineering ◽  
Syntactic Structure ◽  
Directed Graphs ◽  
Mathematical Foundation ◽  
Efficient Tool ◽  
Common Sense Reasoning ◽  
Syntactic Properties

The event bush method being an efficient tool for representation and engineering of dynamic knowledge still lacks a strict mathematical foundation. Many of the syntactic properties of event bushes, however, seem compliant with directed graphs and can be described by typed graphs (i.e., by homomorphisms between directed graphs). This chapter explores an opportunity to formalize the syntactic structure of event bushes by means of typed graphs and shows useful implications of this approach for knowledge engineering and representation.

scholarly journals A Natural Logic System for Large Knowledge Bases

2020 ◽  
Author(s):  
Troels Andreasen ◽  
Henrik Bulskov ◽  
Jørgen Fischer Nilsson
Keyword(s):  
Common Sense ◽  
Scaling Up ◽  
Inference Engine ◽  
Knowledge Bases ◽  
General Purpose ◽  
Software System ◽  
Database Application ◽  
Natural Logic ◽  
Common Sense Reasoning ◽  
Database Operations

This paper describes principles and structure for a software system that implements a dialect of natural logic for knowledge bases. Natural logics are formal logics that resemble stylized natural language fragments, and whose reasoning rules reflect common-sense reasoning. Natural logics may be seen as forms of extended syllogistic logic. The paper proposes and describes realization of deductive querying functionalities using a previously specified natural logic dialect called Natura-Log. In focus here is the engineering of an inference engine employing as a key feature relational database operations. Thereby the inference steps are subjected to computation in bulk for scaling-up to large knowledge bases. Accordingly, the system eventually is to be realized as a general-purpose database application package with the database being turned logical knowledge base.

Grammar of Dynamic Knowledge for Collaborative Knowledge Engineering and Representation

2015 ◽  
pp. 326-353 ◽  
Author(s):  
Cyril Pshenichny ◽  
Dmitry Mouromtsev
Keyword(s):  
Knowledge Representation ◽  
Collaborative Research ◽  
Knowledge Engineering ◽  
Shared Understanding ◽  
Mathematical Foundation ◽  
Engineering Knowledge ◽  
Changing Environments ◽  
Constructive Discussion ◽  
Collaborative Knowledge

Constructive discussion must lead to a shared understanding. This understanding is commonly expressed as text; however, for the purposes of collaborative research, the tools of knowledge engineering/knowledge representation look more appropriate. The problem with them is that, to the present day, they are developed largely for the tasks that imply fixed relations between things and their properties, termed here as static. However, collaborative research often deals with fields of knowledge that represent changing environments where these relations cannot be considered fixed, and the tools to capture scenarios of evolution (i.e. the dynamic tools of knowledge engineering) are far from that evolved as static ones, mainly due to the lack of strict logical or mathematical foundation for representation of dynamic knowledge. This chapter presents an attempt to formulate a unified grammar to encode the knowledge of changing environments in any field of science.

Common-sense reasoning, theories of

2018 ◽  
Author(s):  
John Horty
Keyword(s):  
Artificial Intelligence ◽  
Twentieth Century ◽  
Common Sense ◽  
Scientific Reasoning ◽  
Logical Framework ◽  
Applied Logic ◽  
Common Sense Reasoning ◽  
New Information ◽  
Logical Study ◽  
Reasoning Agent

The task of formalizing common-sense reasoning within a logical framework can be viewed as an extension of the programme of formalizing mathematical and scientific reasoning that has occupied philosophers throughout much of the twentieth century. The most significant progress in applying logical techniques to the study of common-sense reasoning has been made, however, not by philosophers, but by researchers in artificial intelligence, and the logical study of common-sense reasoning is now a recognized sub-field of that discipline. The work involved in this area is similar to what one finds in philosophical logic, but it tends to be more detailed, since the ultimate goal is to encode the information that would actually be needed to drive a reasoning agent. Still, the formal study of common-sense reasoning is not just a matter of applied logic, but has led to theoretical advances within logic itself. The most important of these is the development of a new field of ‘non-monotonic’ logic, in which the conclusions supported by a set of premises might have to be withdrawn as the premise set is supplemented with new information.

Common-sense reasoning and everyday activities

1993 ◽  
Vol 1 (2) ◽  
pp. 307-340 ◽  
Author(s):  
Yvonne Rogers
Keyword(s):  
Common Sense ◽  
Theory And Practice ◽  
Cooking Methods ◽  
Common Sense Reasoning ◽  
Everyday Activities ◽  
Knowledge Based ◽  
Theoretical Approaches ◽  
High Level ◽  
The Relationship ◽  
Selection Of

This paper is concerned with the nature of common-sense reasoning and understanding in relation to practical behaviour. It examines the relationship between intuitive knowledge based on everyday experience and institutionalized theory and practice. An analysis of the types of knowledge that guide the selection of actions and understanding in the domain of cooking practice is presented. Verbal transcripts were elicited from participants, with varying levels of experience, of the cooking methods they followed and their underlying rationale. The results suggest that individuals utilize various high level knowledge primitives in combination with pragmatic utility principles in their reasoning. The findings are discussed in the light of recent theoretical approaches concerned with the relationship between knowledge and inference.

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