A Domain Independent Framework for Extracting Linked Semantic Data from Tables

2012 ◽  
pp. 16-33 ◽  
Author(s):  
Varish Mulwad ◽  
Tim Finin ◽  
Anupam Joshi
Keyword(s):  
Semantic Data ◽  
Domain Independent

scholarly journals BIM-Integrated Collaborative Robotics for Application in Building Construction and Maintenance

Robotics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 2
Author(s):  
Camilla Follini ◽  
Valerio Magnago ◽  
Kilian Freitag ◽  
Michael Terzer ◽  
Carmen Marcher ◽  
...  
Keyword(s):  
Semantic Information ◽  
Building Information Modeling ◽  
A Priori ◽  
Building Construction ◽  
Information Modeling ◽  
Robotic Systems ◽  
Design Phase ◽  
Collaborative Robotics ◽  
Semantic Data ◽  
Building Information

The application of robotics in construction is hindered by the site environment, which is unstructured and subject to change. At the same time, however, buildings and corresponding sites can be accurately described by Building Information Modeling (BIM). Such a model contains geometric and semantic data about the construction and operation phases of the building and it is already available at the design phase. We propose a method to leverage BIM for simple yet efficient deployment of robotic systems for construction and operation of buildings. With our proposed approach, BIM is used to provide the robot with a priori geometric and semantic information on the environment and to store information on the operation progress. We present two applications that verify the effectiveness of our proposed method. This system represents a step forward towards an easier application of robots in construction.

Cubix: A Visual Analytics Tool for Conceptual and Semantic Data

2012 ◽  
Author(s):  
Cassio Melo ◽  
Alexander Mikheev ◽  
Benedicte Le-Grand ◽  
Marie-Aude Aufaure
Keyword(s):  
Visual Analytics ◽  
Semantic Data

Collective Choice and Mutual Knowledge Structures

1998 ◽  
Vol 01 (02n03) ◽  
pp. 221-236 ◽  
Author(s):  
Diana Richards ◽  
Brendan D. McKay ◽  
Whitman A. Richards
Keyword(s):  
Complex System ◽  
Social Choice ◽  
Group Decision ◽  
Knowledge Structure ◽  
Collective Choice ◽  
Sense Data ◽  
Mutual Knowledge ◽  
Interacting Agents ◽  
Aggregation Of Information ◽  
Domain Independent

The conditions under which the aggregation of information from interacting agents results in a stable or an unstable collective outcome is an important puzzle in the study of complex systems. We show that if a complex system of aggregated choice respects a mutual knowledge structure, then the prospects of a stable collective outcome are considerably improved. Our domain-independent results apply to collective choice ranging from perception, where an interpretation of sense data is made by a collection of perceptual modules, to social choice, where a group decision is made from a set of preferences held by individuals.

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