COMP 6703 eScience Project Slide 1 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Ontology-Driven Text Mining for Digital Forensics.

Slides:



Advertisements
Similar presentations
Haystack: Per-User Information Environment 1999 Conference on Information and Knowledge Management Eytan Adar et al Presented by Xiao Hu CS491CXZ.
Advertisements

Chapter 5: Introduction to Information Retrieval
R2 Library Features and Functionality Overview. The R2 Library  The R2 Library is an electronic database that enables access to digital book content.
Improved TF-IDF Ranker
COMP423 Intelligent Agents. Recommender systems Two approaches – Collaborative Filtering Based on feedback from other users who have rated a similar set.
Dialogue – Driven Intranet Search Suma Adindla School of Computer Science & Electronic Engineering 8th LANGUAGE & COMPUTATION DAY 2009.

Information Retrieval Concerned with the: Representation of Storage of Organization of, and Access to Information items.
COMP 6703 eScience Project Commercial Wiki of Academic Journal  Student : Yin Chen  Client/Technical Supervisor : Mr Tom Worthington  Academic Supervisor.
FACT: A Learning Based Web Query Processing System Hongjun Lu, Yanlei Diao Hong Kong U. of Science & Technology Songting Chen, Zengping Tian Fudan University.
Modern Information Retrieval Chapter 1 Introduction.
Information retrieval Finding relevant data using irrelevant keys Example: database of photographic images sorted by number, date. DBMS: Well structured.
TextMOLE: Text Mining Operations Library and Environment Daniel B. Waegel and April Kontostathis, Ph.D. Ursinus College Collegeville PA.
INEX 2003, Germany Searching in an XML Corpus Using Content and Structure INEX 2003, Germany Yiftah Ben-Aharon, Sara Cohen, Yael Grumbach, Yaron Kanza,
1 CS 502: Computing Methods for Digital Libraries Lecture 11 Information Retrieval I.
Personalized Ontologies for Web Search and Caching Susan Gauch Information and Telecommunications Technology Center Electrical Engineering and Computer.
Large-Scale Content-Based Image Retrieval Project Presentation CMPT 880: Large Scale Multimedia Systems and Cloud Computing Under supervision of Dr. Mohamed.
Databases & Data Warehouses Chapter 3 Database Processing.
Query Relevance Feedback and Ontologies How to Make Queries Better.
Research paper: Web Mining Research: A survey SIGKDD Explorations, June Volume 2, Issue 1 Author: R. Kosala and H. Blockeel.
1 The BT Digital Library A case study in intelligent content management Paul Warren
Information Need Question Understanding Selecting Sources Information Retrieval and Extraction Answer Determina tion Answer Presentation This work is supported.
Computer Basics & Keyboarding. What Is A Computer? An electronic device operating under the control of instructions stored in its own memory unit An electronic.
Dr. Susan Gauch When is a rock not a rock? Conceptual Approaches to Personalized Search and Recommendations Nov. 8, 2011 TResNet.
PERSONALIZED SEARCH Ram Nithin Baalay. Personalized Search? Search Engine: A Vital Need Next level of Intelligent Information Retrieval. Retrieval of.
Xiaoying Gao Computer Science Victoria University of Wellington Intelligent Agents COMP 423.
Thanks to Bill Arms, Marti Hearst Documents. Last time Size of information –Continues to grow IR an old field, goes back to the ‘40s IR iterative process.
Query Expansion By: Sean McGettrick. What is Query Expansion? Query Expansion is the term given when a search engine adding search terms to a user’s weighted.
1 Information Retrieval Acknowledgements: Dr Mounia Lalmas (QMW) Dr Joemon Jose (Glasgow)
Match the following names of the items to the pictures on slides 1 to 28. barcode scanner CD-ROM OR DVD-ROM CPU desktop Mac desktop PC drawing pad and.
Xiaoying Gao Computer Science Victoria University of Wellington Intelligent Agents COMP 423.
Math Information Retrieval Zhao Jin. Zhao Jin. Math Information Retrieval Examples: –Looking for formulas –Collect teaching resources –Keeping updated.
Chapter 6: Information Retrieval and Web Search
GUIDED BY DR. A. J. AGRAWAL Search Engine By Chetan R. Rathod.
IT-522: Web Databases And Information Retrieval By Dr. Syed Noman Hasany.
Computer Basics & Keyboarding. What Is A Computer? An electronic device operating under the control of instructions stored in its own memory unit An electronic.
1 Mining the Web to Determine Similarity Between Words, Objects, and Communities Author : Mehran Sahami Reporter : Tse Ho Lin 2007/9/10 FLAIRS, 2006.
Modern Information Retrieval Presented by Miss Prattana Chanpolto Faculty of Information Technology.
Basics of Information Retrieval and Query Formulation Bekele Negeri Duresa Nuclear Information Specialist.
Introduction to Information Retrieval Example of information need in the context of the world wide web: “Find all documents containing information on computer.
Information Retrieval CSE 8337 Spring 2007 Introduction/Overview Some Material for these slides obtained from: Modern Information Retrieval by Ricardo.
Performance Measurement. 2 Testing Environment.
Information Retrieval
Advantages of Query Biased Summaries in Information Retrieval by A. Tombros and M. Sanderson Presenters: Omer Erdil Albayrak Bilge Koroglu.
Web Search and Text Mining Lecture 5. Outline Review of VSM More on LSI through SVD Term relatedness Probabilistic LSI.
Comparing Document Segmentation for Passage Retrieval in Question Answering Jorg Tiedemann University of Groningen presented by: Moy’awiah Al-Shannaq
Intelligent Database Systems Lab Presenter: CHANG, SHIH-JIE Authors: Longzhuang Li, Yi Shang, Wei Zhang 2002.ACM. Improvement of HITS-based Algorithms.
1 CS 430: Information Discovery Lecture 8 Automatic Term Extraction and Weighting.
Xiaoying Gao Computer Science Victoria University of Wellington COMP307 NLP 4 Information Retrieval.
Integrated Departmental Information Service IDIS provides integration in three aspects Integrate relational querying and text retrieval Integrate search.
Selecting Relevant Documents Assume: –we already have a corpus of documents defined. –goal is to return a subset of those documents. –Individual documents.
1 CS 8803 AIAD (Spring 2008) Project Group#22 Ajay Choudhari, Avik Sinharoy, Min Zhang, Mohit Jain Smart Seek.
IR Homework #2 By J. H. Wang Apr. 13, Programming Exercise #2: Query Processing and Searching Goal: to search for relevant documents Input: a query.
COMP423 Intelligent Agents. Recommender systems Two approaches – Collaborative Filtering Based on feedback from other users who have rated a similar set.
Information Retrieval (in Practice)
DATA MINING Introductory and Advanced Topics Part III – Web Mining
Operating System Orientation CS3135
Information Retrieval and Web Search
Multimedia Information Retrieval
Information Retrieval
Thanks to Bill Arms, Marti Hearst
موضوع پروژه : بازیابی اطلاعات Information Retrieval
Text Categorization Document classification categorizes documents into one or more classes which is useful in Information Retrieval (IR). IR is the task.
Evaluation of IR Performance
CSE 635 Multimedia Information Retrieval
Introduction to Information Retrieval
Combining Keyword and Semantic Search for Best Effort Information Retrieval  Andrew Zitzelberger 1.
Information Retrieval and Web Design
Information Retrieval and Web Design
Information Retrieval
Presentation transcript:

COMP 6703 eScience Project Slide 1 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Ontology-Driven Text Mining for Digital Forensics Supervisors: Dr. Warren Jin and Dr. Nianjun Liu Project coordinator: Dr. Peter Strazdins Student’s Name: Phan T. Son Student ID#: u

COMP 6703 eScience Project Slide 2 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Outline Motivation Goals Performance Conclusion

COMP 6703 eScience Project Slide 3 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Outline Motivation Goals Performance Conclusion

COMP 6703 eScience Project Slide 4 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Motivation > Basic IR … mouse … … PC … … computer … …laptop… … ticket … … … PC … … … … mouse … corpus result Query: PC … PC … … computer … …laptop… … ticket … … … PC … … … ordering

COMP 6703 eScience Project Slide 5 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Ontology-based IR Query: PC desktop laptop notebook personal computer

COMP 6703 eScience Project Slide 6 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Motivation > Ontology-based IR … mouse … … PC … … computer … …laptop… … ticket … … … PC … … … … mouse … corpus result Query: PC … PC … … computer … …laptop… … ticket … … … PC … … …

COMP 6703 eScience Project Slide 7 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Motivation > Ontology-based IR Query: notebook -computer

COMP 6703 eScience Project Slide 8 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Outline Motivation Goals Performance Conclusion

COMP 6703 eScience Project Slide 9 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Goals  Importing s from different Mail User Agents (MUAs).  Retrieving Information: keyword based search, and ontology based search.

COMP 6703 eScience Project Slide 10 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Structure  Importing processing  Document processing  Query processing  Importing processing  Document processing  Query processing

COMP 6703 eScience Project Slide 11 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Importing

COMP 6703 eScience Project Slide 12 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Document processing

COMP 6703 eScience Project Slide 13 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Query processing +pc –laptop compaq +: required - : prohibited whitespace: optional

COMP 6703 eScience Project Slide 14 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Query: mouse trackball computer mouse Query processing

COMP 6703 eScience Project Slide 15 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Query processing electronic device mouse device rodent trackball house mouse field mouse … … … WordNet compute r mouse …

COMP 6703 eScience Project Slide 16 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Structure Importing processing Document processing Query processing result Lexical database index

COMP 6703 eScience Project Slide 17 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Outline Motivation Goals Performance Conclusion

COMP 6703 eScience Project Slide 18 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Performance > Measure Precision = #(relevant items retrieved) #(retrieved items) Recall = #(relevant items retrieved) #(relevant items)

COMP 6703 eScience Project Slide 19 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Performance > Measure

COMP 6703 eScience Project Slide 20 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Performance > Typical Query: laptop

COMP 6703 eScience Project Slide 21 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Performance > Overall

COMP 6703 eScience Project Slide 22 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Outline Motivation Goals Performance Conclusion

COMP 6703 eScience Project Slide 23 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son A similarity function An implementation Conclusion

COMP 6703 eScience Project Slide 24 Ontology-Driven Text Mining for Digital Forensics © 2007 Phan Son Thank you!