1. Designing a Multi-channel Legal Knowledge Service Center Using Data Analysis and Contact Center Technology
Amy J.C. Trappey1
Fu-Chiang Hsu1
Adam J-L. Hou1
Charles V. Trappey2, 3
Shang-Jyh Liu2
1National Tsing Hua University, Taiwan
2National Chiao Tung University, Taiwan
3Adjunct Professor, Queensland University of Technology,
Australia
Good afternoon! Ladies and gentleman, I would like to introduce our paper entitled as Designing a Multi-channel Legal Knowledge Service Center Using Data Analysis and Contact Center Technology. There are five authors in this research
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2. Outline Research Objectives Literature Review
System Analysis and Design
Conclusion
I would like to divide my presentation into four sections. Firstly the research objectives will be introduced.
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3. Research Objectives
Demonstrate an effective and integrated approach for tacit knowledge sharing and explicit knowledge exchanges in the business environment.
Develop contact center and data analysis methodologies to enhance the feasibility, versatility and customer-orientation of a legal service center.
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4. Outline Research Objectives Literature Review Knowledge Management
Contact Center Technology
Data Mining
A Multi-Channel Legal Knowledge Service Center Model
Conclusion
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5. Knowledge Management
Knowledge management is the process of creating values from intangible assets.
Feigenbaum et al. (1983) and Bebenham (1998) define knowledge engineering as a means to handle knowledge acquisition, representation, validation, inferences, explanation, and maintenance.
Hou, Trappey, et al (2003) propose an enterprise knowledge management model with a centralized knowledge base and authority control to improve knowledge transfer efficiency and ownership in a global value chain.
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6. Contact Center Technology
Contact centers are implemented to improve customer services (Rowley, Mostowfi and Lees, 2002).
Contact centers not only integrate multi-channels and provide customer service support but also improve quality management, contact routing, and knowledge management (Trappey, et al, 2004).
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7. Data Mining
Data mining generates reports that companies use to identify customers and their preferences based on transaction records (Berry and Linoff, 1997).
Clustering is a data mining method used to separate a heterogeneous population into a number of homogeneous subgroups without predefined classes (Berry and Linoff, 1997).
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8. Classification of Clustering Techniques
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9. Outline Research Objectives Literature Review
A Multi-Channel Legal Knowledge Service Center Model
System Architecture and Functions
Key Modules of Legal Knowledge Management (LKM) Server
Knowledge Document Management
Legal Knowledge Mining
Conclusion
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10. System Architecture and Functions (1/3)
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11. System Architecture and Functions (2/3)
System Functions – KM part
User management module
User account management
Member management
Privilege management
Legal document management module
Legal knowledge base creation
Legal knowledge document maintenance function
Legal knowledge upload function
Legal knowledge version control function
Legal document advanced management module
Key phrases extraction and search function
Key phrases correlation analysis function
Knowledge document automatic classification function
Knowledge document search function
Knowledge document cluster function
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12. System Architecture and Functions (3/3)
System Functions – CRM part
Computer telephony integration (CTI) module
Call scheduling and dispatching
Voice mail management
Inbound and outbound call management
Web application module
Web account management – instant pop-up interfaces
Call history - inbound calls, outbound calls and voice messages
Intelligent web-based functions
Web-based fax functions
Data analysis functions
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13. Knowledge Document Management (1/3)
This module manages the physical document upload, automated key phrases extraction, automated document classification, metadata management, and version control.
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14. Knowledge Document Management (2/3)
The following steps are employed to facilitate the explicit knowledge document management.
Step 1: Key phrases extraction
Step 2: Key phrases frequency measures
Step 3: Key phrases integration
Step 4: Key phrases correlation analysis
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15. Knowledge Document Management (3/3)
Key phrases correlation matrix
KP1
KP2
KP3
KP4
KP5
KP6
KP7
KP8
KP9
KP10 1
0.5
0.8
0.1
0.2
0.6 1　
0.7
0.3
0.2　
0.9 　1
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16. Legal Knowledge Mining (1/3)
Technologies clustering
Using the correlation table and K-means methodology, the system can automatically generate key phrases clusters where the technologies are divided among clusters. Finally, the RMSSTD and RS values are calculated to validate the clustering result.
Within-cluster variation
Between-cluster variation
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17. Legal Knowledge Mining (2/3)
Document clustering analysis
Key document groups can be formed using the matrix of key technologies and documents. Through clustering analysis, knowledge documents are categorized.
Doc Cluster 1
Doc Cluster 3
cluster
Doc Cluster 2
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18. Legal Knowledge Mining (3/3)
Technology maturity measurement (TMMi)
The maturity for specific technologies can be measured using the number of existing documents and their aggregation of related key phrases.
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19. Outline Research Objectives Literature Review
A Multi-Channel Legal Knowledge Service Center Model
Conclusion
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20. Conclusion
This paper presents an integrated legal knowledge service framework for legal knowledge sharing and exchange.
The clustering methods are developed to automate various knowledge analysis services.
Combined with contact center support, knowledge providers and knowledge recipients share knowledge effectively and efficiently.
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