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    For paper For paper Presentation Transcript

    • Presented By : Aamir Mushtaq Jesal Mistry Kapil Tekwani Neville Shah Visual Representation of Knowledge Articles as Dynamic Interactive Connected Graph Nodes
      • Introduction
      • ACM Keywords
      • Some Wikipedia Statistics
      • Algorithm used
      • Mathematical Model
      • Feasibility Analysis
      • Proposed UI
      • References
      • Paper Publications
      • Motivation of the Project undertaken
      • Questions
      Overview
    • Introduction
      • Online knowledge articles have become increasingly popular
      • Eg - Wikipedia is used by students, educators, professionals etc
      • Problem faced:
        • Article topics to be studied are not easy to understand
        • Take too much time
        • Have too much content
      • Possible solution: Create a graphical visualization of knowledge articles.
      • Enables users to obtain an easily understandable overview of an article
      • H. Information Systems
        • H.2 Database Management
          • H.2.8 Database Applications
            • Data Mining
        • H.3 Information Storage and Retrieval
          • H.3.3 Information Search and Retrieval
            • Information filtering
            • Query formulation
          • H.3.5 Online Information Services
            • Web based services
        • H.5 Information Interfaces and Presentation
          • H.5.4 Hypertext/Hypermedia
            • Architecture
            • Navigation
      • I. Computing Methodologies
        • I.2 Artificial Intelligence
          • I.2.7 Natural Language Processing
              • Text Analysis
          • I.2.8 Problem Solving, Control Methods & Search
            • Dynamic Programming
            • Graph & Tree Search Strategies
      ACM Keywords
    • Some Wikipedia Statistics
      • No. of page views on average, per month (Dec 2011, en) = 7.958 Billion
      • Translating to approx. 265 M/d or 11.1 M/h or 184 k / m or 3.1 k/s
      • Assume 5 m/article translates to ~ 920 k minutes
    • Algorithm Used
      • Input to system = URL of Knowledge article (consider Wikipedia)
      • Select the document from Wikipedia dump / Scrape corresponding to the input URL. (document = Natural language words + Keywords + Links)
      • Eliminate Natural language words.
      • Count section-wise occurrences of keywords, store using tables and calculate weight. Ex: weight of particular keyword in doc = 0.7*cs1+0.5*cs2+0.3*cs3
      • Create a table for Links in that document, if there is a link for a particular keyword it will add to the weight of that keyword.
      • Create a threshold for keywords or links to be displayed based on weight.
    • Algorithm Used (cont’d) 7. Depending on current depth, pre-decided window size to select top keyword/links for next level. Example: 20 for 0 th level, 10 for 1 st level, 5 for 2 nd level.(tuning required) 8. For efficient searching of accurate data we will be working across the depth i.e. at next levels if the keyword (present in previous level doc) is occurring many times (say 100), it will add weight to the corresponding keyword in the previous table. 9 Output will be graphical representation of keywords. If node (keyword) is a link, it will be connected to another node (keyword) of next level else stop at that level.
    • Mathematical Model Let S be the system.   S = {U inp , U, D, Q, W t , K w , T Kw,S , T Kw,Wg , T U,Kw , T U,Kw,Wg }   U inp = URL identifier (input to the system)   D = database of the WWW, containing webpages as documents d i . D = {d 1 , d 2 , d 3 ,..., d n } where d i is a WWW document (webpage).   Q = set of all possible queries. Q = {q 1 , q 2 , q 3, ..., q n } where q i is any given query to be fired on the database. W t = set of words of a particular document. W t = {w 1 , w2,..., wn} where w i ϵ d i, for 1<= i <= n   K w = set of keywords ⊆ W t, obtained after F el K w = {k 1 , k 2 ,…, k m } where k i ⊆ W t , for 1<= i <= m   U = extracted URLs from document d i U = {u 1 , u 2 ,..., u n } where u i ϵ d i
    • Mathematical Model T Kw,S = table of keywords and sectional counts, obtained after F cnt T Kw,S = {<k 1 , sA 1 , sB 1 , sC 1 >, <k 2 , sA 2 , sB 2 , sC 2 >, …, <k m , sA 3 , sB 3 , sC 3 >} T Kw,Wg = table of keywords and associated weights, obtained after F w T Kw,Wg = {<k 1 ,wg 1 >, <k 2 , wg 2 >, … ,<k m , wg m >}   T U,Kw = table of urls in U mapped with the keywords and weights table T Kw,Wg obtained after F map T U,Kw = {<u n­ , k m , wg m >} U t is a mapping of keywords and their respective <U>
    • Mathematical Model Functions: F el (W T {<w 1 , w 2 , ... , w n >}) = K W F el eliminates all natural language elements from the <W T > part and resultant set of words are the keywords that are identified in the <K W > list / set. F cnt ( K w {<k 1 , k 2 , ... , k n >}) = T Kw,S F cnt returns an array of tuples of keywords and their respective sectional counts {<k m , s1, s2, s3>} which would be used in the calculation of weights of keywords. And provide the T Kw,S as input of F w . F w ( T Kw,S {<k m , sA m , sB m , sC m >}) = T Kw,Wg F w takes the T Kw,S obtained by the function F cnt as input and calculates the weight associated with each keyword and returns array of tuples of keywords and weights. {<k m , wg m >} F map ( U{<u 1 , u 2 , … u n >} ,T Kw,Wg {<k 1 ,wg 1 >, <k 2 ,wg 2 > ,…,<k m , wg m >}) = T U,Kw,Wg F map takes the U< u 1 , u 2 ...u n > and T Kw,Wg <k m , wg m > as input and it maps the keywords with the respective Urls in the d i and returns an array of urls with their mapped keywords and Urls. F win (lvl) = {<5> v <10> v <20>} F win is a window function that returns the size of the window that is dependent on the depth/ level that we are in.
    • Feasibility Analysis
      • NP – Hard:
        • Number of keywords and links not known while scanning wiki
        • Processing power at server not determined in advance
        • Ranking algorithm exponential in nature
        • Solution not determined in polynomial time
      • NP – Complete:
        • Assign ranks to keywords and links, using ranking algorithm
        • Use threshold value to limit links
        • Approximate processing power calculated to scan documents
        • Thus converted NP – Hard to NP – Complete
    • Proposed UI – shows the output of a search
      • Backward References:
        • Schonhofen, P.; &quot;Identifying Document Topics Using the Wikipedia Category Network,&quot; Web Intelligence, 2006. WI 2006. IEEE/WIC/ACM International Conference on , vol., no., pp.456-462, 18-22 Dec. 2006
        • Cheong-Iao Pang; Biuk-Aghai, R.P.; , &quot;Map-like Wikipedia overview visualization,&quot; Collaboration Technologies and Systems (CTS), 2011 International Conference on , vol., no., pp.53-60, 23-27 May 2011
        • Boukhelifa, N.; Chevalier, F.; Fekete, J.; , &quot;Real-time aggregation of Wikipedia data for visual analytics,&quot; Visual Analytics Science and Technology (VAST), 2010 IEEE Symposium on , vol., no., pp.147-154, 25-26 Oct. 2010
        • Lamberti, F.; Sanna, A.; Demartini, C.; , &quot;A Relation-Based Page Rank Algorithm for Semantic Web Search Engines,&quot; Knowledge and Data Engineering, IEEE Transactions on , vol.21, no.1, pp.123-136, Jan. 2009
        • Prato, A.; Ronchetti, M.; , &quot;Using Wikipedia as a Reference for Extracting Semantic Information from a Text,&quot; Advances in Semantic Processing, 2009. SEMAPRO '09. Third International Conference on , vol., no., pp.56-61, 11-16 Oct. 2009
      References
      • Forward References:
        • Cheong-Iao Pang; Biuk-Aghai, R.P.; , &quot;Map-like Wikipedia overview visualization,&quot; Collaboration Technologies and Systems (CTS), 2011 International Conference on , vol., no., pp.53-60, 23-27 May 2011.
        • Pirrone, R.; Pipitone, A.; Russo, G.; “Semantic sense extraction from Wikipedia pages,” Human System Interactions (HSI), 2010 3 rd Conference on, vol., no., pp. 543-547, 13-15 May 2010
        • Wikipedia data from Wikipedia links: http://stats.wikimedia.org/EN/TablesPageViewsMonthlyCombined.htm
        • Wikipedia database download in xml format: http://dumps.wikimedia.org/ derived from http://en.wikipedia.org/wiki/wikipedia:Database_Download
        • Wikitools from mediaWiki in url: http://en.wikipedia.org/wiki/MediaWiki
        • Wikipedia Categorization from Wikipedia website: http://en.wikipedia.org/wiki/Wikipedia:Categorization
      References (cont’d)
    • Paper Publications
      • Paper Title:
      • Visual Representation of Knowledge Articles as Dynamic Interactive Connected Graph Nodes.
      • Name of Conference where paper submitted:
        • European Modeling Symposium 2011, EMS2011
        • Informatics and Computational Intelligence 2011, ICI2011
        • Education and e-learning conference 2011, EeL2011
        • International Conference on Information Systems and Technologies 2012, ICIST2012
      • Name of Conference where paper Accepted:
        • European Modeling Symposium 2011, EMS2011
        • Informatics and Computational Intelligence 2011, ICI2011
        • Education and e-learning conference 2011, EeL2011
        • International Conference on Information Systems and Technologies 2012, ICIST2012
      • Name of Journal where paper Accepted:
        • International Foundation for Modern Education and Scientific Research (INFOMESR)
    • Motivation of the Project undertaken
      • Project Motivation:
        • Provide a user friendly solution to problem mentioned in introduction
        • Project overall saves man hours (a picture is worth a thousand words)
        • Visualization and interactivity enhances interest and makes learning fun
        • Knowledge articles assimilated easily and quickly.
        • Overview of a topic obtained with minimum reading
        • Time spent reading minimised
      • Personal Motivation
        • Learn new technologies
        • Learn SDLC
        • Project management skills
    • Thank You Any Questions?