Please use this identifier to cite or link to this item:
Title: RULIE : rule unification for learning information extraction
Authors: Dingli, Alexiei
Busuttil, Dale P.
Seychell, Dylan
Keywords: Information retrieval -- Automation
Natural language processing (Computer science)
Corpora (Linguistics)
Computer algorithms
Machine learning
Issue Date: 2011
Publisher: International Joint Conferences on Artificial Intelligence
Citation: Dingli, A., Busuttil, D., & Seychell, D. (2011). RULIE : rule unification for learning information extraction. The 22nd International Joint Conference on Artificial Intelligence (IJCAI-11), Barcelona. 61-66.
Abstract: In this paper we are presenting RULIE (Rule Unification for Learning Information Extraction), an adaptive information extraction algorithm which works by employing a hybrid technique of Rule Learning and Rule Unification in order to extract relevant information from all types of documents which can be found and used in the semantic web. This algorithm combines the techniques of the LP2 and the BWI algorithms for improved performance. In this paper we are also presenting the experimen- tal results of this algorithm and respective details of evaluation. This evaluation compares RULIE to other information extraction algorithms based on their respective performance measurements and in almost all cases RULIE outruns the other algorithms which are namely: LP2 , BWI, RAPIER, SRV and WHISK. This technique would aid current techniques of linked data which would eventually lead to fullier realisation of the semantic web.
Appears in Collections:Scholarly Works - FacICTAI

Files in This Item:
File Description SizeFormat 
OA Conference paper - RULIE Rule Unification for Learning Information Extraction..72-77.pdfRULIE : rule unification for learning information extraction547.59 kBAdobe PDFView/Open

Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.