Paper Info
Title
A New Framework for Discovering Knowledge from Two-Dimensional Structured Data Using Layout Formal Graph System
Abstract
We present a new framework for discovering knowledge from two-dimensional structured data by using Inductive Logic Programming. Two-dimensional graph structured data such as image or map data are widely used for representing relations and distances between various objects. First, we define a layout term graph suited for representing two-dimensional graph structured data. A layout term graph is a pattern consisting of variables and two-dimensional graph structures. Moreover, we propose Layout Formal Graph System (LFGS) as a new logic programming system having a layout term graph as a term. LFGS directly deals with graphs having positional relations just like first order terms. Second, we show that LFGS is more powerful than Layout Graph Grammar, which is a generating system consisting of a context-free graph grammar and positional relations. This indicates that LFGS has the richness and advantage of representing knowledge about two-dimensional structured data. Finally, we design a knowledge discovery system, which uses LFGS as a knowledge representation language and refutably inductive inference as a learning method. In order to give a theoretical foundation of our knowledge discovery system, we give the set of weakly reducing LFGS programs which is a sufficiently large hypothesis space of LFGS programs and show that the hypothesis space is refutably inferable from complete data.
Year
DOI
Venue
2000
10.1007/3-540-40992-0_11
ALT
Keywords
Field
DocType
new framework,two-dimensional structured data,knowledge discovery system,positional relation,two-dimensional graph,layout formal graph system,layout term graph,lfgs program,complete data,discovering knowledge,map data,two-dimensional graph structure,context-free graph grammar,inductive inference,knowledge representation,structured data,knowledge discovery,first order
Inductive logic programming,Knowledge representation and reasoning,Computer science,Theoretical computer science,Matching (graph theory),Artificial intelligence,Graph rewriting,Knowledge extraction,Clique-width,Abstract semantic graph,Machine learning,Graph (abstract data type)
Conference
Volume
ISSN
ISBN
1968
0302-9743
3-540-41237-9
Citations 
PageRank 
References 
3
0.44
12
Authors
5
Name
Order
Citations
PageRank
Tomoyuki Uchida125535.06
Yuko Itokawa241.14
Takayoshi Shoudai326931.89
Tetsuhiro Miyahara426732.75
Yasuaki Nakamura5105140.45