Paper Info
Title
User Friendly Automatic Construction of Background Knowledge: Mode Construction from ER Diagrams
Abstract
One of the key advantages of Inductive Logic Programming systems is the ability of the domain experts to provide background knowledge as modes that allow for efficient search through the space of hypotheses. However, there is an inherent assumption that this expert should also be an ILP expert to provide effective modes. We relax this assumption by designing a graphical user interface that allows the domain expert to interact with the system using Entity Relationship diagrams. These interactions are used to construct modes for the learning system. We evaluate our algorithm on a probabilistic logic learning system where we demonstrate that the user is able to construct effective background knowledge on par with the expert-encoded knowledge on five data sets.
Year
DOI
Venue
2017
10.1145/3148011.3148027
K-CAP 2017: Knowledge Capture Conference Austin TX USA December, 2017
Field
DocType
ISSN
Inductive logic programming,Data set,Information retrieval,Feature selection,Subject-matter expert,Computer science,Graphical user interface,Probabilistic logic,User Friendly,Entity–relationship model
Conference
Proceedings of the Knowledge Capture Conference (2017) 30:1-30:8
ISBN
Citations 
PageRank 
978-1-4503-5553-7
4
0.57
References 
Authors
8
4
Name
Order
Citations
PageRank
Alexander L. Hayes140.57
Mayukh Das295.03
Phillip Odom3295.09
Sriraam Natarajan448249.32