research-article

Data service generation framework from heterogeneous printed forms using semantic link discovery

Authors:

Lihong JiangAuthors Info & Claims

Future Generation Computer Systems, Volume 79, Issue P2

Pages 514 - 527

https://doi.org/10.1016/j.future.2017.09.059

Published: 01 February 2018 Publication History

Abstract

Printed forms contain rich information in business process and daily life. However, tremendous heterogeneous printed forms containing same categories of information are difficult to manage and share, which lead to massive data in printed forms remaining waste. To automatically integrate and share these data remarkably improves the efficiency of enterprises, the key problem is how to extract heterogeneous data in printed forms and integrate them for quick use. To solve this issue, we propose a framework that discovers semantic links in printed forms and generates data services for easy data management and rapid data sharing in the enterprise systems. First, a multiple-OCR-based form recognition approach is proposed to make forms computer-readable. Next, forms are modeled into semi-structured data using structure-based semantic link discovery and refining with massive data. Then, a linked data model is built by table matching to align data. Finally, data services are generated based on the linked data model. A series of experiments on printed resumes are conducted, and the results illustrate our framework performs well in recognition rate, link discovery accuracy, data compression ratio and data resource accuracy. A prototype system is presented to illustrate the feasibility of the proposed framework. A complete and feasible framework from printed forms to data service is proposed.An automatic form data extraction and structuration approach is presented.A usable prototype system integrating heterogeneous printed resumes is implemented.

References

[1]

C. Patel, A. Patel, D. Patel, Optical character recognition by open source OCR Tool tesseract: A case study, Int. J. Comput. Appl., 55 (2012) 50-56.

[2]

N. Pernelle, F. Sas, D. Symeonidou, An automatic key discovery approach for data linking, Web Semant. Sci. Serv. Agents World Wide Web, 23 (2013) 16-30.

Digital Library

[3]

Q. Zheng, H. Chen, T. Yu, G. Pan, Collaborative semantic association discovery from linked data, in: IEEE International Conference on Information Reuse & Integration, 2009, IEEE, 2009, pp. 394-399.

Digital Library

[4]

Y. Peng, J. Wei, Springer Berlin Heidelberg, 2015.

[5]

K.L. Skillen, L. Chen, C.D. Nugent, M.P. Donnelly, W. Burns, I. Solheim, Ontological user modelling and semantic rule-based reasoning for personalisation of Help-On-Demand services in pervasive environments, Future Gener. Comput. Syst., 34 (2014) 97-109.

Digital Library

[6]

D. Ritze, O. Lehmberg, C. Bizer, Matching html tables to dbpedia, in: Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics, ACM, 2015, pp. 10.

Digital Library

[7]

J. Fan, M. Lu, B.C. Ooi, W.-C. Tan, M. Zhang, A hybrid machine-crowdsourcing system for matching web tables, in: 2014 IEEE 30th International Conference onData Engineering, IEEE, 2014, pp. 976-987.

[8]

Z. Zhang, Towards efficient and effective semantic table interpretation, in: International Semantic Web Conference, 2014, pp. 487502.

Digital Library

[9]

S.A. Mcilraith, T.C. Son, H. Zeng, Semantic web services, IEEE Intell. Syst., 16 (2011) 46-53.

Digital Library

[10]

H. Cai, C. Xie, L. Jiang, L. Fang, C. Huang, An ontology-based semantic configuration approach to constructing data as a Service for enterprises, Enterp. Inf. Syst., 10 (2016) 325-348.

Digital Library

[11]

M. Laitkorpi, P. Selonen, T. Systa, Towards a model-driven process for designing restful web services, in: IEEE International Conference on Web Services, ICWS 2009, Los Angeles, Ca, Usa, 6-10 July 2009, pp. 173180.

Digital Library

[12]

B. You, X.R. Liu, N. Li, Y.S. Yan, Using information content to evaluate semantic similarity on hownet, in: Eighth International Conference on Computational Intelligence and Security 2012, pp. 142145.

Digital Library

[13]

X. Yu, L. Peng, Z. Huang, H. Zhuge, A framework for automated construction of resource space based on background knowledge, Future Gener. Comput. Syst., 32 (2014) 222231.

[14]

A. Ting, M.K.H. Leung, Form recognition using linear structure, Pattern Recognit., 32 (1999) 645-656.

[15]

X.U. Jun-Gang, Overview of data extraction,transformation and loading, Comput. Sci. (2011).

[16]

C.-F. Lin, C.-Y. Hsiao, Structural recognition for table-form documents using relaxation techniques, Int. J. Pattern Recognit. Artif. Intell., 12 (1998) 985-1005.

[17]

J. Hirayama, H. Shinjo, T. Takahashi, T. Nagasaki, Development of template-free form recognition system, in: 2011 International Conference on Document Analysis and Recognition, IEEE, 2011, pp. 237-241.

Digital Library

[18]

P. Emilie, B. Yolande, B. Abdel, Use of semantic and physical constraints in bayesian networks for form recognition, in: International Conference on Document Analysis and Recognition, 2011, pp. 946950.

Digital Library

[19]

T. Kasar, T.K. Bhowmik, A. Belaid, Table information extraction and structure recognition using query patterns, in: 2015 13th International Conference on Document Analysis and Recognition, IEEE, 2015, pp. 1086-1090.

Digital Library

[20]

D. Ritze, O. Lehmberg, Y. Oulabi, C. Bizer, Profiling the potential of web tables for augmenting cross-domain knowledge bases, in: Proceedings of the 25th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, 2016, pp. 251261.

Digital Library

[21]

S.A.R. Dezfouli, J. Habibi, S.H. Yeganeh, Semantic web services for handling data heterogeneity in an e-business framework, in: Advances in Computer Science and Engineering, Springer, 2008, pp. 453-460.

[22]

C. Yu, L. Huang, CluCF: a clustering CF algorithm to address data sparsity problem, Serv. Oriented Comput. Appl., 11 (2017) 1-13.

Digital Library

[23]

A. Alsaig, V. Alagar, M. Mohammad, W. Alhalabi, A user-centric semantic-based algorithm for ranking services: design and analysis, Serv. Oriented Comput. Appl., 11 (2017) 1-20.

Digital Library

Cited By

Degirmenci KRecker J(2023)Breaking bad habitsInformation and Management10.1016/j.im.2023.10377860:4Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.im.2023.103778
Zhang YHuang LLu JXiao G(2019)An Automatic Data Service Generation Approach for Cross-origin DatasetsWeb Engineering10.1007/978-3-030-19274-7_27(374-390)Online publication date: 11-Jun-2019
https://dl.acm.org/doi/10.1007/978-3-030-19274-7_27
Zhang YCai HXu BVasilakos AHuang C(2018)Data driven business rule generation based on fog computingFuture Generation Computer Systems10.1016/j.future.2018.07.00389:C(494-505)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1016/j.future.2018.07.003

Recommendations

A framework for semantic link discovery over relational data
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Discovering links between different data items in a single data source or across different data sources is a challenging problem faced by many information systems today. In particular, the recent Linking Open Data (LOD) community project has highlighted ...
An optimized data integration model based on reverse cleaning for heterogeneous multi-media data

With the continuous development of information technology, various multi-media data are constantly emerging and presents the characteristics of autonomous and heterogeneous, how to integrate and analysis data more correctly and efficiently has become a ...
A declarative framework for semantic link discovery over relational data
WWW '09: Proceedings of the 18th international conference on World wide web

In this paper, we present a framework for online discovery of semantic links from relational data. Our framework is based on declarative specification of the linkage requirements by the user, that allows matching data items in many real-world scenarios. ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Future Generation Computer Systems

Future Generation Computer Systems Volume 79, Issue P2

February 2018

310 pages

ISSN:0167-739X

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 February 2018

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 30 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Degirmenci KRecker J(2023)Breaking bad habitsInformation and Management10.1016/j.im.2023.10377860:4Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.im.2023.103778
Zhang YHuang LLu JXiao G(2019)An Automatic Data Service Generation Approach for Cross-origin DatasetsWeb Engineering10.1007/978-3-030-19274-7_27(374-390)Online publication date: 11-Jun-2019
https://dl.acm.org/doi/10.1007/978-3-030-19274-7_27
Zhang YCai HXu BVasilakos AHuang C(2018)Data driven business rule generation based on fog computingFuture Generation Computer Systems10.1016/j.future.2018.07.00389:C(494-505)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1016/j.future.2018.07.003

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents