Automated Question Answering Suggestion Using User Expert and Semantic Information การแนะนำการตอบคำถามอัตโนมัติ โดยใช้ข้อมูลผู้เชี่ยวชาญ และข้อมูลเชิง ภาษา Mr. Seksan Poltree ID Code Asst. Prof. Kanda Runapongsa Saikaew Advisor [Need to include slide number]

Searching Knowledge And Information

Discussion, a Question-Answer is a classical methodology!

Web Question Answering And traditional Web Forums

What's a Problem? Topics and posts duplication Post same topic without search Question and answer quality Complicated algorithm Ambiguous meaning

Objectives 1. Create a system ranking based on semantic information for quality ranking instead of only quantity ranking 2. Increase question and answer based quality and reduce topic duplication

Related Works 1. Building Experience Database from Personal Opinion and Web Document [specify cite reference number] [need to specify the strengths and weaknesses and the difference between the proposed work and each related work] Experience Database User Generated Contents Topics Experiencer Event Type Factuality Source -- W

Related Works(2) 2. Effects of Earlier Messages on Later Messages Domain : Maxnet Feeling Contribution

3. Web-Service-Based Architecture for Question Answering 4. Automated Question Answering using Semantic Web Services Related Works(3) NLP Analyzer Web Service Semantic Web Service Web Services Caller Natural Language Template Answer

Related Works(4) 5. Page Ranking Algorithms : Survey Page Rank (PR) Google Page Rank Weighted Page Rank (WPR) PR but give Weight for some Page Page Content Rank (PCR) Relevancy page using Neuron Network HITS Sampling and then find HUB and Authority

Related Works(5) 6. Relational-Based Page Rank Algorithm for Semantic Web Search Engines Keywords Pre-Search Result Ontology sub-graph Travel.O WL Final Result

Research Tools HARVESTMAN WEB SPIDER NLTK Python Programming Language [Need to specify why choosing these tools] Django Framework : Python MVC Harvestman Web Spider : Open source Crawler PyLucene : Indexer and Searching DB Neo4J an open source graph database (include Lucene) NLTK : Python Natural Language Toolkit OWL library

System Design NLTK AJAX/Web Query Interface Semantic Question Analyzer (Orchid Tagging/ Sentence Structure) Quality Ranking Indexed Database Neo 4j Messages Crawler & Web Miner Query Session Semantic Message Type Classifier & Domain Specific (RDF/OWL) Message Quality Ranking (User Rank/ Post Rank) HARVESTMAN WEB SPIDER Suggestion Module (Domain & Related) NLTK Type a Post Update Search Suggest Query exist posts

Data Collection Using Resource Description Framework (RDF) to embed semantic information in to the posts. Using Web Ontology Language(OWL) to store ontology information, and XML to store description information. Using Lucene indexing format for searching and ranking database including updated results [Should have the example of system design, data collection, and data result analysis by giving two posts that are the same and go through the process of the system that will detect such duplication]

Data Result Analysis Data structure and statistical analysis Number of system and suggestion usage Newly created topics and suggestion selective ratio Number of knowledge and Information message types Output Ontology

Limitations of Study Using only one web forum as a main resource, searching in local web forum database Considering semantic information, sentence structure, and ontology representation based on only Thai language

Anticipated outcomes Fewer duplicated questions in the web forum using the implemented automated question answering system Higher quality suggestion for posting contents

Research Plan and Procedures

References [1] K. Inui, S. Abe, K. Zuo, K. Morita and C. Sao, Experience Mining: Building a Large- Scale Database of Personal Experiences and Opinions from Web Documents. IEEE/WIC/ACM International Conference on Web Inteligence and Intelligent Agent Technology,2008. [2] G. W. Chen and M. M. Chiu, Online Discussion Process: Effects of Earlier Messages'Evolutions, Knowledge Content, Social Cues and Personal Information on Later Messages. In preceeding of Sixth International Conference on Advanced Learning Technologies, [3] Zhe Chen and Dunwei Wen, A New Web-Service-Based Architecture for Question Answering. In preceeding of IEEE International Conference on Natural Language Processing and Knowledge Engineering, [4] M. Jang, J. Sohn, and H. Cho, Automated Question Answering using Semantic Web Services. IEEE Asia-Pacific Services Computer Conference, [5] N. Duhan, A. K. Shama, and K. K. Bhatia, Page Rank Algorithms: A Survey. IEEE International Adcaned Computing Conference (IACC), 2009.

Special Thanks for Resources