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Journals
Information
Special Issues
Selected Papers from Conference on Document Information Processing (CDIP) 2014
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Selected Papers from Conference on Document Information Processing (CDIP) 2014

A special issue of Information (ISSN 2078-2489).

Deadline for manuscript submissions: closed (1 January 2015)

Special Issue Editors

Prof. Lawrence J. Henschen

E-Mail Website
Guest Editor
Electrical and Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA
Interests: document formatting systems; human-computer interfaces; embedded systems
Special Issues, Collections and Topics in MDPI journals
Prof. Ning Li

E-Mail Website
Guest Editor
Computer School, Beijing Information Science and Technology University, 35 Bei-Si-Huan-Zhong-Lu, Beijing 100101, China
Interests: document information processing; xml; information technology standardization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the carrier of information and knowledge, the document has permeated through our society. Private letters, government documents, business letters, contracts or agreements, historical archives, scientific papers, product manuals, media webpage, electronic books: all of these are documents. Document information processing technology covers document presentation, editing, transformation, storage, retrieval, publishing, printing, publication, digital copyright, data stream integration and intelligent document, etc. In recent years, people have paid special attention to new trends in technical development, such as structural presentation of document information; standardization and interoperability of document format; document processing under cloud computing; document information mining; mobile reading; combination of semantic web and intelligent document, documents and big data, document reflowing and knowledge service, etc.

Organized and hosted by the China Computer Federation Technical Committee on Chinese Information Technology, and Beijing Information Science and Technology University, 2014 Conference on Document Information Processing (CDIP 2014) aims to gather elites in the field of document information processing in the academic and industrial circles to conduct wide academic communication, discuss the direction of theoretical and application development, introduce new technologies and methods of document information processing, show the latest research findings and jointly promote the development and application of document information processing theory and technology.

We present the Special Issue of CDIP 2014 in this collection of excellent papers submitted to the conference. It is believed that they will outline recent development in the relevant fields.

Prof. Dr. Ning Li
Prof. Dr. Lawrence J. Henschen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


Keywords

  • document information processing
  • document modelling
  • document presentation
  • document understanding
  • knowledge mining
  • document processing
  • cloud office
  • intelligent document
  • document dataset
  • document application

Published Papers (4 papers)

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Research

738 KiB  
Article
Recommender System for E-Learning Based on Semantic Relatedness of Concepts
by Mao Ye, Zhi Tang, Jianbo Xu and Lifeng Jin
Information 2015, 6(3), 443-453; https://0-doi-org.brum.beds.ac.uk/10.3390/info6030443 - 04 Aug 2015
Cited by 7 | Viewed by 4602
Abstract
Digital publishing resources contain a lot of useful and authoritative knowledge. It may be necessary to reorganize the resources by concepts and recommend the related concepts for e-learning. A recommender system is presented in this paper based on the semantic relatedness of concepts [...] Read more.
Digital publishing resources contain a lot of useful and authoritative knowledge. It may be necessary to reorganize the resources by concepts and recommend the related concepts for e-learning. A recommender system is presented in this paper based on the semantic relatedness of concepts computed by texts from digital publishing resources. Firstly, concepts are extracted from encyclopedias. Information in digital publishing resources is then reorganized by concepts. Secondly, concept vectors are generated by skip-gram model and semantic relatedness between concepts is measured according to the concept vectors. As a result, the related concepts and associated information can be recommended to users by the semantic relatedness for learning or reading. History data or users’ preferences data are not needed for recommendation in a specific domain. The technique may not be language-specific. The method shows potential usability for e-learning in a specific domain. Full article
(This article belongs to the Special Issue Selected Papers from Conference on Document Information Processing (CDIP) 2014)
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983 KiB  
Article
ODQ: A Fluid Office Document Query Language
by Xuhong Liu, Ning Li, Yunmei Shi and Xia Hou
Information 2015, 6(2), 275-286; https://0-doi-org.brum.beds.ac.uk/10.3390/info6020275 - 11 Jun 2015
Viewed by 4708
Abstract
Fluid office documents, as semi-structured data often represented by Extensible Markup Language (XML) are important parts of Big Data. These office documents have different formats, and their matching Application Programming Interfaces (APIs) depend on developing platform and versions, which causes difficulty in custom [...] Read more.
Fluid office documents, as semi-structured data often represented by Extensible Markup Language (XML) are important parts of Big Data. These office documents have different formats, and their matching Application Programming Interfaces (APIs) depend on developing platform and versions, which causes difficulty in custom development and information retrieval from them. To solve this problem, we have been developing an office document query (ODQ) language which provides a uniform method to retrieve content from documents with different formats and versions. ODQ builds common document model ontology to conceal the format details of documents and provides a uniform operation interface to handle office documents with different formats. The results show that ODQ has advantages in format independence, and can facilitate users in developing documents processing systems with good interoperability. Full article
(This article belongs to the Special Issue Selected Papers from Conference on Document Information Processing (CDIP) 2014)
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880 KiB  
Article
An MVC-based Intelligent Document Model Using UIML
by Yunmei Shi, Xuhong Liu, Ning Li and Xia Hou
Information 2015, 6(2), 122-133; https://0-doi-org.brum.beds.ac.uk/10.3390/info6020122 - 27 Mar 2015
Viewed by 4830
Abstract
Aiming at the common problems of intelligent document platform-dependency, this paper proposes an MVC-based (Model View Controller-based) intelligent document model using UIML (User Interface Markup Language). The model is made on the basis of the previous work of our team, and the difference [...] Read more.
Aiming at the common problems of intelligent document platform-dependency, this paper proposes an MVC-based (Model View Controller-based) intelligent document model using UIML (User Interface Markup Language). The model is made on the basis of the previous work of our team, and the difference is that the new model separates user interface and interaction descriptions from the view component to make the intelligent document model much more independent of platform and programming language. To verify the intelligent document model, we implemented a prototype, which can support intelligent operations. The test result shows that our approach is correct. The model not only follows MVC framework, but also provides good flexibility and independence. Full article
(This article belongs to the Special Issue Selected Papers from Conference on Document Information Processing (CDIP) 2014)
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3479 KiB  
Article
Evaluate the Interoperability of Document Format: Based on Translation Practice of OOXML and UOF
by Yaohu Lin, Xuelian Lin, Ning Li and Yongmin Mu
Information 2015, 6(2), 111-121; https://0-doi-org.brum.beds.ac.uk/10.3390/info6020111 - 27 Mar 2015
Cited by 2 | Viewed by 4589
Abstract
Taking both OOXML and UOF standards as examples, we empirically evaluate the interoperability of office document formats from the view of translation practice. With the aim of covering the complete feature set of OOXML and UOF, a novel UOF-Open XML Translator is developed [...] Read more.
Taking both OOXML and UOF standards as examples, we empirically evaluate the interoperability of office document formats from the view of translation practice. With the aim of covering the complete feature set of OOXML and UOF, a novel UOF-Open XML Translator is developed in this study. Thorough experiments demonstrate that our translator implements bidirectional conversion of 80.4% features perfectly and 9.9% features with acceptable discrepancy. Regarding the remaining 9.7% features, more efforts would be taken in future work. Full article
(This article belongs to the Special Issue Selected Papers from Conference on Document Information Processing (CDIP) 2014)
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