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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. Introduction

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slide1

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.

slide2

Introduction

    • Natural Language Interfaces to Databases
    • Guided Conversational Agents
    • Knowledge Trees
  • Proposed Framework
  • Developed Interface Tools
  • Conclusions and Future Work
  • Q/A
slide3

Contents

  • Introduction
    • Natural Language Interfaces to Databases
    • Guided Conversational Agents
    • Knowledge Trees
  • Proposed Framework
  • Developed Interface Tools
  • Conclusions and Future Work
  • Q/A
slide4

Natural Language Interfaces to Databases

  • Where the Complexity comes from !!
  • Past Approaches
    • Pattern-Matching
    • IntermediateLanguage
    • Syntax-Based Family
    • Semantic-Grammar

The Challenge: Creating Simple & Reliable Natural Language Interfaces to Relational Databases.

slide5

Contents

  • Introduction
    • Natural Language Interfaces to Databases
    • Guided Conversational Agents
    • Knowledge Trees
  • Proposed Framework
  • Developed Interface Tools
  • Conclusions and Future Work
  • Q/A
slide6

Conversation Agents

  • Initial Idea -- Alan Turing (Turing Test) 1950
  • First System -- Joseph Weizenbaum (Eliza) 1960s
  • 1st Robust System -- Colboy (Parry) late 1960s
  • 1st reusable, general purpose system -- Wallace (Alice) 2000
  • MMU (InfoChat-Adam) 2001

Idea: use a guided conversational agent for NLIDBs.

slide7

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
slide8

Contents

  • Introduction
    • Natural Language Interfaces to Databases
    • Guided Conversational Agents
    • Knowledge Trees
  • Proposed Framework
  • Developed Interface Tools
  • Conclusions and Future Work
  • Q/A
slide9

Knowledge Trees

  • Easy to revise & maintain
  • connect CA & R-DB
  • Road map for CA dialogue flow
  • Direct CA towards the goal

Direction Node

Goal Node

Idea: using knowledge trees for NLIDBs.

slide10

Contents

  • Introduction
    • Natural Language Interfaces to Databases
    • Guided Conversational Agents
    • Knowledge Trees
  • Proposed Framework
  • Developed Interface Tools
  • Conclusions and Future Work
  • Q/A
slide11

User Query

Agent Response

Conversation Manager

Response Generation

Context Switching & Manage

Knowledge

Tree

Conversational Agent

SQL statements

Rule Matching

Information Extraction

Context Script files

Relational Database

Conversation-Based NLI-RDB Framework

  • Main components
    • Conversational Agents
    • Knowledge Trees
    • Conversation Manager
    • Relational Database
slide12

Contents

  • Introduction
    • Natural Language Interfaces to Databases
    • Guided Conversational Agents
    • Knowledge Trees
  • Proposed Framework
  • Developed Interface Tools
  • Conclusions and Future Work
  • Q/A
slide16

Contents

  • Introduction
    • Natural Language Interfaces to Databases
    • Guided Conversational Agents
    • Knowledge Trees
  • Proposed Framework
  • Developed Interface Tools
  • Conclusions and Future Work
  • Q/A
slide17

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
slide18

Current & Future Work

  • An adaptive conversation-based NLIDB
  • Dynamic knowledge trees

Idea: There is still big room to do further research. 

slide19

Questions

m.owda@mmu.ac.uk