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					 Guided Conversational Agents and
Knowledge Trees for Natural Language
  Interfaces to Relational Databases

Mr. Majdi Owda, Dr. Zuhair Bandar, Dr. Keeley
                 Crockett

 The Intelligent Systems Group, Department of
   Computing and Mathematics, Manchester
            Metropolitan University.
Background to Research
• Databases
   – Hierarchal Databases
   – Relational Databases *
   – Object Oriented Databases
• Artificial Intelligence
   – Knowledge Representation
       • Knowledge Trees *
   – Expert Systems
   – Natural Language Processing
       • Conversational Agents *
   – Machine Learning
• Human-Computer Interaction
   – Natural Language Interfaces *
• Introduction
    – Natural Language Interfaces to Databases
    – Guided Conversational Agents
    – Knowledge Trees
•   Proposed Framework
•   Developed Prototype
•   Conclusions and Future Work
•   Q/A
Contents
• Introduction
    – Natural Language Interfaces to Databases
    – Guided Conversational Agents
    – Knowledge Trees
•   Proposed Framework
•   Developed Prototype
•   Conclusions and Future Work
•   Q/A
Natural Language Interfaces to Databases
• Where the Complexity comes from !!
• Past Approaches
   –   Pattern-Matching
   –   Intermediate Language
   –   Syntax-Based Family
   –   Semantic-Grammar


The Problem: Creating Reliable Natural Language Interfaces to
  Relational Databases.
Contents
• Introduction
    – Natural Language Interfaces to Databases
    – Guided Conversational Agents
    – Knowledge Trees
•   Proposed Framework
•   Developed Prototype
•   Conclusions and Future Work
•   Q/A
Guided Conversation Agents
•   Alan Turing (Turing Test) 1950
•   Joseph Weizenbaum (Eliza) 1960s
•   Colboy (Parry) late 1960s
•   Wallace (Alice) 2000
•   MMU (InfoChat-Adam) 2001


Idea: use a guided conversational agent for NLIDBs.
Algorithm: having a guided conversational agent component
   trained to converse within a database domain knowledge.
    Guided Conversation Agents – Why
    InfoChat
•   Autonomous general purpose CA
•   Deals set of contexts
•   Direct the users towards a goal
•   Flexible and robust
•   Converse freely within a specific domain
•   Extract, manipulate, and store information
Contents
• Introduction
    – Natural Language Interfaces to Databases
    – Guided Conversational Agents
    – Knowledge Trees
•   Proposed Framework
•   Developed Prototype
•   Conclusions and Future Work
•   Q/A
Knowledge Trees
                                               Direction Node

                                                 Goal Node




Idea: using knowledge trees for NLIDBs.
Algorithm: having knowledge trees component within the new
   framework.
Knowledge Trees Benefits
• Easy way to revise and maintain the
  knowledge base
• Overcome the lacking of connectivity
  between CA and the Relational Database
• Road map for the conversational agent
  dialogue flow
• Direct the conversational agent towards the
  goal.
Contents
• Introduction
    – Natural Language Interfaces to Databases
    – Guided Conversational Agents
    – Knowledge Trees
•   Proposed Framework
•   Developed Prototype
•   Conclusions and Future Work
•   Q/A
Conversation-Based NLI-RDB Framework
  User Query                Agent Response


                                                     • Main components
                  Conversation
                   Manager
                                                       –   Conversational Agents
  Response                        Context
  Generation                     Switching
                                 & Manage              –   Knowledge Trees
                                                       –   Conversation Manager
  Knowledge                         Conversational     –   Relational Database
    Tree                               Agent

      SQL                               Rule
   statements                          Matching



    Context                           Information
   Script files                        Extraction




      Relational
      Database
Contents
• Introduction
    – Natural Language Interfaces to Databases
    – Guided Conversational Agents
    – Knowledge Trees
•   Proposed Framework
•   Developed Prototype
•   Conclusions and Future Work
•   Q/A
Conversation-Based NLI-RDB Prototype
Tools
Conversation-Based NLI-RDB Interface
Conversation-Based NLI-RDB Interface
Contents
• Introduction
    – Natural Language Interfaces to Databases
    – Guided Conversational Agents
    – Knowledge Trees
•   Proposed Framework
•   Developed Prototype
•   Conclusions and Future Work
•   Q/A
Conclusions
• Easy and flexible way in order to develop a
  Conversation-Based NLI-RDB
• General purpose framework which can be
  applied to a wide range of domains
• Utilizing dialogue interaction
• Knowledge trees are easy to create, structure,
  update, revise, and maintain
• Capability of handling simple and complex
  queries
Current & Future Work
 • An adaptive conversation-based NLIDB
 • Dynamic knowledge trees




Idea: There is still big room to do further research. 
Special thanks “MMU Research Team”



   Dr. Keeley Crockett   Mr James O’Shea




   Dr. Zuhair Bandar     Dr. David Mclean
Questions
 m.owda@mmu.ac.uk

				
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