Transcript Document
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 – Natural Language Interfaces to Databases – Guided Conversational Agents – Knowledge Trees • • • • Proposed Framework Developed Interface Tools Conclusions and Future Work Q/A Contents • Introduction – Natural Language Interfaces to Databases – Guided Conversational Agents – Knowledge Trees • • • • Proposed Framework Developed Interface Tools 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 Challenge: Creating Simple & Reliable Natural Language Interfaces to Relational Databases. Contents • Introduction – Natural Language Interfaces to Databases – Guided Conversational Agents – Knowledge Trees • • • • Proposed Framework Developed Interface Tools Conclusions and Future Work Q/A 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. 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 Interface Tools Conclusions and Future Work Q/A 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. Contents • Introduction – Natural Language Interfaces to Databases – Guided Conversational Agents – Knowledge Trees • • • • Proposed Framework Developed Interface Tools Conclusions and Future Work Q/A Conversation-Based NLI-RDB Framework User Query Agent Response • Main components Conversation Manager Response Generation Knowledge Tree Context Switching & Manage Conversational Agent SQL statements Rule Matching Context Script files Information Extraction Relational Database – – – – Conversational Agents Knowledge Trees Conversation Manager Relational Database Contents • Introduction – Natural Language Interfaces to Databases – Guided Conversational Agents – Knowledge Trees • • • • Proposed Framework Developed Interface Tools Conclusions and Future Work Q/A Conversation-Based NLI-RDB Interface Tools – Knowledge Tree Builder Conversation-Based NLI-RDB Interface Tools – User Interface Conversation-Based NLI-RDB Interface Tools – User Interface Contents • Introduction – Natural Language Interfaces to Databases – Guided Conversational Agents – Knowledge Trees • • • • Proposed Framework Developed Interface Tools 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. Questions [email protected]