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

Idea: using knowledge trees for NLIDBs.

Algorithm: having knowledge trees component within the new framework.

Direction Node

Goal Node

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

• Main components– Conversational Agents

– Knowledge Trees

– Conversation Manager

– Relational Database

Relational Database

KnowledgeTree

SQL statements

Context Script files

Conversational Agent

Rule Matching

Conversation Manager

Context Switching & Manage

Agent Response

Response Generation

User Query

Information Extraction

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

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

• An adaptive conversation-based NLIDB

• Dynamic knowledge trees

Special thanks “MMU Research Team”

Dr. Keeley Crockett Mr James O’Shea

Dr. Zuhair Bandar Dr. David Mclean

Questionsm.owda@mmu.ac.uk