Learning Structure and Schemas from Documents

The rapidly growing volume of available digital documents of various formats and the possibility to access these through Internet-based technologies, have led to the necessity to develop solid methods to properly organize and structure documents in large digital libraries and repositories. Due to th...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Biba, Marenglen (Επιμελητής έκδοσης), Xhafa, Fatos (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011.
Σειρά:Studies in Computational Intelligence, 375
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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020 |a 9783642229138  |9 978-3-642-22913-8 
024 7 |a 10.1007/978-3-642-22913-8  |2 doi 
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245 1 0 |a Learning Structure and Schemas from Documents  |h [electronic resource] /  |c edited by Marenglen Biba, Fatos Xhafa. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg,  |c 2011. 
300 |a XVIII, 442 p. 98 illus.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
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490 1 |a Studies in Computational Intelligence,  |x 1860-949X ;  |v 375 
505 0 |a From the content: Learning Structure and Schemas from Heterogeneous Domains in Networked Systems Surveyed -- Handling Hierarchically Structured Resources Addressing Interoperability Issues in Digital Libraries -- Administrative Document Analysis and Structure -- Automatic Document Layout Analysis through Relational Machine Learning -- Dataspaces: where structure and schema meet. 
520 |a The rapidly growing volume of available digital documents of various formats and the possibility to access these through Internet-based technologies, have led to the necessity to develop solid methods to properly organize and structure documents in large digital libraries and repositories. Due to the extremely large volumes of documents and to their unstructured form, most of the research efforts in this direction are dedicated to automatically infer structure and schemas that can help to better organize huge collections of documents and data.   This book covers the latest advances in structure inference in heterogeneous collections of documents and data. The book brings a comprehensive view of the state-of-the-art in the area, presents some lessons learned and identifies new research issues, challenges and opportunities for further research agenda and developments.  The selected chapters cover a broad range of research issues, from theoretical approaches to case studies and best practices in the field.   Researcher, software developers, practitioners and students interested in the field of learning structure and schemas from documents will find the comprehensive coverage of this book useful for their research, academic, development and practice activity. 
650 0 |a Engineering. 
650 0 |a Artificial intelligence. 
650 0 |a Computational intelligence. 
650 1 4 |a Engineering. 
650 2 4 |a Computational Intelligence. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
700 1 |a Biba, Marenglen.  |e editor. 
700 1 |a Xhafa, Fatos.  |e editor. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783642229121 
830 0 |a Studies in Computational Intelligence,  |x 1860-949X ;  |v 375 
856 4 0 |u http://dx.doi.org/10.1007/978-3-642-22913-8  |z Full Text via HEAL-Link 
912 |a ZDB-2-ENG 
950 |a Engineering (Springer-11647)