Developing an improved focused crawler for the IDEAL project Ward Bonnefond, Chris Menzel, Zack Morris, Suhas Patel, Tyler Ritchie, Mark Tedesco, Franklin.

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Presentation transcript:

Developing an improved focused crawler for the IDEAL project Ward Bonnefond, Chris Menzel, Zack Morris, Suhas Patel, Tyler Ritchie, Mark Tedesco, Franklin Zheng

Refresher - IDEAL project  Integrating Digital Event Archiving and Library  Finding webpages related to an event (i.e. natural disaster)  Store found webpages locally for parsing and analysis

Refresher - Enhanced focus crawler  Extract key words and key concepts (i.e. date, location, type of disaster)  Construct trees based on these words and concepts  Develop algorithm to compare different trees and their relationships  Make this process accessible via a web application

Refresher - Project components 1. Tree construction and visual representation 2. Event representation (i.e. key words and key concepts) versus actual event (i.e. webpage) 3. Integrating updated modules into the existing focused crawler

Refresher - Original Implementation Start with a list of seed URLs Web-crawler crawls through list of URLs Outputs a score for each URL based on keyword matchings Searches the webpage for other URLs Adds any good URLs found to the list

Final Results  Front-End  User can enter multiple seed URLS and other important attributes (date, location, type of disaster, etc.)  Constructs a visual tree representation of the articles found via the web application  Loads back-end results using JavaScript (previously PHP)  Back-end  Constructs a full event tree from query created by the user  Intelligently extracts location, date, type of disaster, etc. using natural language processing as opposed to keyword analysis

Scoring Algorithm  Date: 20%  Year (365) + Month (30) + Day (1)  i.e. Year has 365 times the weight of a Day  Location, Type of Event, Event Name, etc.: 80%  Replaces the previously used tree-edit distance  New scoring algorithm provides more accurate articles that using only tree- edit distance did not find

Start with a list of seed URLs Web-crawler crawls through list of URLs Outputs a score for each URL based on custom scoring algorithm Searches the webpage for other URLs Adds any good URLs found to the list Final Implementation

Current Back End Example Data  Base Focused Crawler  | | /27/donald-sterling-photos-magic-johnson-matt-kemp-v-stiviano- racist- audio/2/ | | /2014/04/27/donald-sterling-photos-magic-johnson-matt-kemp- v-stiviano-racist- audio/1/ | | /2014/04/27/donald-sterling-photos-magic-johnson-matt-kemp- v-stiviano-racist- audio/20/ | | m/2014/04/27/donald-sterling-photos-magic-johnson-matt- kemp-v-stiviano-racist- audio/14/ | | m/2014/04/27/donald-sterling-photos-magic-johnson-matt- kemp-v-stiviano-racist- audio/11/ | | m/2014/04/27/donald-sterling-photos-magic-johnson-matt- kemp-v-stiviano-racist- audio/10/ | | m/2014/04/27/donald-sterling-photos-magic-johnson-matt- kemp-v-stiviano-racist- audio/12/ | | m/2014/04/27/donald-sterling-photos-magic-johnson-matt- kemp-v-stiviano-racist- audio/13/ | | m/2014/04/27/donald-sterling-photos-magic-johnson-matt- kemp-v-stiviano-racist- audio/15/ | | m/2014/04/27/donald-sterling-photos-magic-johnson-matt- kemp-v-stiviano-racist-audio/16/  Visited: 100Accepted: 81 Extended Focused Crawler |0.52| /Kim-Kardashian-eBays-old-clothes-help-Philippines-typhoon- victims--donates-just-10-proceeds- charity.html |1.0| /philippines-typhoon- haiyan/en/ | | encies/crisis/philippines-typhoon-haiyan/crop-damages- map/en/ | | cies/crisis/philippines-typhoon-haiyan/impact-assessment- map/en/ | | cies/crisis/philippines-typhoon-haiyan/input-distribution- map/en/ |1.0| hilippines-typhoon-haiyan/seed-distribution-map/en/ Visited: 100Accepted: 73

Current Front-End Example

Current Front End – Tree View

Extras – Poster

Extras – Second Place Letter

Questions?