GraFar - Repository of the Faculty of Civil Engineering
Faculty of Civil Engineering of the University of Belgrade
    • English
    • Српски
    • Српски (Serbia)
  • English 
    • English
    • Serbian (Cyrilic)
    • Serbian (Latin)
  • Login
View Item 
  •   GraFar
  • GraFar
  • Radovi istraživača / Researcher's publications
  • View Item
  •   GraFar
  • GraFar
  • Radovi istraživača / Researcher's publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Crawling the construction web - A machine-learning approach without negative examples

No Thumbnail
Authors
Kovačević, Miloš
Davidson, Colin H.
Article (Published version)
Metadata
Show full item record
Abstract
Professionals and craftsmen in the construction sector make an intensive use of information in their decision-making processes but only make limited use of the abundant information, that is potentially available to them, particularly on the web. Consequently, designs are impoverished, construction is defective, and innovation is delayed. To facilitate convivial access to focused information, we have developed a question-and-answer (Q-A) system (reported elsewhere). To support this system, we have developed an automated crawler that permits the establishment of a bank of relevant Pages, adopted to the needs of this particular industry-user community. It is based on the in which all intelligent decision unit is trained to distinguish between nontopic and informative pages. We show that standard approaches which use both positive and negative classes are sensitive to the noise in the negative class. We propose different techniques for learning without negative examples, since initially on...e only has limited, positive information labeled by human experts; they are evaluated. Our crawler that, uses the positive examples-based learning (PEBL) framework is able to collect construction-oriented pages with high precision and discovery rate. It can also be used to build domain-specific collections of pages in different scientific or professional contexts.

Source:
Applied Artificial Intelligence, 2008, 22, 5, 459-482

DOI: 10.1080/08839510802028447

ISSN: 0883-9514

WoS: 000256188700004

Scopus: 2-s2.0-46149113729
[ Google Scholar ]
1
1
URI
http://grafar.grf.bg.ac.rs/handle/123456789/196
Collections
  • Radovi istraživača / Researcher's publications
  • Катедра за геодезију и геоинформатику
Institution
GraFar

DSpace software copyright © 2002-2015  DuraSpace
About GraFar - Repository of the Faculty of Civil Engineering | Send Feedback

OpenAIRERCUB
 

 

All of DSpaceInstitutionsAuthorsTitlesSubjectsThis institutionAuthorsTitlesSubjects

Statistics

View Usage Statistics

DSpace software copyright © 2002-2015  DuraSpace
About GraFar - Repository of the Faculty of Civil Engineering | Send Feedback

OpenAIRERCUB