research-article

Semantic mining on customer survey

Authors:

Yang Yu,

Jiangbo DangAuthors Info & Claims

I-SEMANTICS '12: Proceedings of the 8th International Conference on Semantic Systems

Pages 72 - 79

https://doi.org/10.1145/2362499.2362510

Published: 05 September 2012 Publication History

Get Access

Abstract

Business intelligence aims to support better business decision-making. Customer survey is priceless asset for intelligent business decision-making. However, business analysts usually have to read hundreds of textual comments and tabular data in survey to manually dig out the necessary information to feed business intelligence models and tools. This paper introduces a business intelligence system to solve this problem by extensively utilizing Semantic Web technologies. Ontology based knowledge extraction is the key to extract interesting terms and understand the logic concept of them. All knowledge extracted forms a semantic knowledge base. Flexible user queries and intelligent analysis can be easily issued to the system over the semantic data store through standard protocol. Besides resolving problems in theory, we designed a flexible, intuitive user interaction interface to explain and present the analysis result for business analysts. Through the real usage of this system, it is validated that our system gives good solution for semantic mining on customer survey for business intelligence.

References

[1]

R. A. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1999.

Digital Library

Google Scholar

[2]

D. Brownell. SAX2. O'Reilly Series. O'Reilly, 2002.

Digital Library

Google Scholar

[3]

P. Buitelaar, P. Cimiano, S. Racioppa, and M. Siegel. Ontology-based Information Extraction with SOBA, pages 2321--2324. Citeseer, 2006.

Google Scholar

[4]

P. Cimiano, S. Handschuh, and S. Staab. Towards the self-annotating web. WWW "04, pages 462--471, New York, NY, USA, 2004. ACM.

Digital Library

Google Scholar

[5]

D. W. Embley. Toward semantic understanding: an approach based on information extraction ontologies. In Proceedings of the 15th Australasian database conference, pages 3--12, Darlinghurst, Australia, 2004.

Digital Library

Google Scholar

[6]

A. Esuli and F. Sebastiani. Sentiwordnet: A publicly available lexical resource for opinion mining. In In Proceedings of the 5th Conference on Language Resources and Evaluation, pages 417--422, 2006.

Google Scholar

[7]

Y. Li and K. Bontcheva. Hierarchical, perceptron-like learning for ontology-based information extraction. WWW "07, pages 777--786, New York, USA, 2007.

Digital Library

Google Scholar

[8]

A. Maedche, G. Neumann, and S. Staab. Intelligent exploration of the web. pages 345--359. 2003.

Digital Library

Google Scholar

[9]

B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing, pages 79--86, Stroudsburg, PA, USA, 2002.

Digital Library

Google Scholar

[10]

H. Saggion, A. Funk, D. Maynard, and K. Bontcheva. Ontology-based information extraction for business intelligence. In Proceedings of the 6th international The semantic web, ISWC'07, pages 843--856, Berlin, Heidelberg, 2007. Springer-Verlag.

Digital Library

Google Scholar

[11]

M. Vargas-Vera, E. Motta, J. Domingue, S. Buckingham Shum, and M. Lanzoni. Knowledge extraction by using an ontology-based annotation tool. Knowledge Creation Diffusion Utilization, pages 5--12, 2001.

Google Scholar

[12]

F. Wu and D. S. Weld. Automatically refining the wikipedia infobox ontology. WWW "08, pages 635--644, New York, NY, USA, 2008. ACM.

Digital Library

Google Scholar

[13]

C.-N. Ziegler, M. Skubacz, and M. Viermetz. Mining and exploring unstructured customer feedback data using language models and treemap visualizations. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01, WI-IAT "08, pages 932--937, Washington, DC, USA, 2008.

Digital Library

Google Scholar

Cited By

View all

Grossmann WMoser C(2016)Big Data—Integration and Cleansing Environment for Business Analytics with DICEDomain-Specific Conceptual Modeling10.1007/978-3-319-39417-6_5(103-123)Online publication date: 10-Jul-2016
https://doi.org/10.1007/978-3-319-39417-6_5
Lau RLi CLiao S(2014)Social analyticsDecision Support Systems10.1016/j.dss.2014.05.00565:C(80-94)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1016/j.dss.2014.05.005

Index Terms

Semantic mining on customer survey
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics

Recommendations

A Survey on Association Rule Mining
ACCT '15: Proceedings of the 2015 Fifth International Conference on Advanced Computing & Communication Technologies

Task of extracting useful and interesting knowledge from large data is called data mining. It has many aspects like clustering, classification, association mining, outlier detection, regression etc. Among them association rule mining is one of the ...
Semantic Web Mining

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: More and more researchers are working on improving the results of Web Mining by ...
A survey of incremental high-utility itemset mining

Traditional association rule mining has been widely studied. But it is unsuitable for real-world applications where factors such as unit profits of items and purchase quantities must be considered. High-utility itemset mining HUIM is designed to find ...

Comments

Information & Contributors

Information

Published In

I-SEMANTICS '12: Proceedings of the 8th International Conference on Semantic Systems

September 2012

215 pages

ISBN:9781450311120

DOI:10.1145/2362499

Editors:
Harald Sack
Hasso-Plattner-Institut für Softwaresystemtechnik GmbH
,
Tassilo Pellegrini
University of Applied Sciences St. Pölten
,
Program Chairs:
Valentina Presutti
Institute for Cognitive Science and Technology, Rome - Italy
,
H. Sofia Pinto
Technical University of Lisbon - Portugal

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 September 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

I-SEMANTICS '12

I-SEMANTICS '12: I-SEMANTICS 2012 - 8th International Conference on Semantic Systems

September 5 - 7, 2012

Graz, Austria

Acceptance Rates

Overall Acceptance Rate 40 of 182 submissions, 22%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
263
Total Downloads

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

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Grossmann WMoser C(2016)Big Data—Integration and Cleansing Environment for Business Analytics with DICEDomain-Specific Conceptual Modeling10.1007/978-3-319-39417-6_5(103-123)Online publication date: 10-Jul-2016
https://doi.org/10.1007/978-3-319-39417-6_5
Lau RLi CLiao S(2014)Social analyticsDecision Support Systems10.1016/j.dss.2014.05.00565:C(80-94)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1016/j.dss.2014.05.005

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

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

A Survey on Association Rule Mining

Semantic Web Mining

A survey of incremental high-utility itemset mining

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations