Paper Info
Title
Knowledge Representation Using a Modified Earley's Algorithm
Abstract
Attribute grammars (AGs) have been proven to be valuable tools in knowledge engineering applications. In this paper, we formalize knowledge representation problems in their AG equivalent form and we extend the Earley's parsing algorithm in order to evaluate simultaneously attributes based on semantic rules related to logic programming. Although Earley's algorithm can not be extended to handle attribute evaluation computations for all possible AGs, we show that the form of AGs created for equivalent logic programs and the related attribute evaluation rules are such that allow their use for knowledge representation. Hence, a fast one-pass left to right AG evaluator is presented that can effectively be used for logic programs. We also suggest a possible software/hardware implementation for the proposed approach based on existing hardware parsers for Earley's algorithm, which work in coordination with a conventional RISC microprocessor and can assist in the creation of small-scale applications on intelligent embedded systems with optimized performance.
Year
DOI
Venue
2004
10.1007/978-3-540-24674-9_34
Lecture Notes in Computer Science
Keywords
Field
DocType
knowledge representation,attribute grammar,knowledge engineering,embedded system
Rule-based machine translation,Attribute grammar,Programming language,Computer science,Artificial intelligence,Knowledge base,Logic programming,Knowledge representation and reasoning,Expert system,Algorithm,Knowledge engineering,Parsing,Machine learning
Conference
Volume
ISSN
Citations 
3025
0302-9743
0
PageRank 
References 
Authors
0.34
13
3
Name
Order
Citations
PageRank
Christos Pavlatos1164.74
Ioannis Panagopoulos2112.60
George K. Papakonstantinou315961.88