# Constructing Fuzzy Thesaurus for WWW

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Published on December 6, 2007

Author: lusi

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Constructing Fuzzy Thesaurus for WWW: Application to BeMySearch :  Constructing Fuzzy Thesaurus for WWW: Application to BeMySearch M. De Cock, S. Guadarrama and M. Nikravesh 2003 BISC FLINT-CIBI BISC, UC Berkeley Content:  Content Introduction Basic Concepts Term Weighting The WTW-approach Association Rules Fuzzy Terms Examples Conclusion Introduction:  Introduction During 70s-80s: Small text collections. Structured Databases. Information Retrieval methods. Now: Huge multimedia collections. Unstructured Web. Fuzzy Retrieval methods. Fuzzy Thesaurus:  Fuzzy Thesaurus Is a couple (T, R) consisting of a set T of terms and a set R of binary fuzzy relations. Examples of binary fuzzy relations are: Similarity Broader Narrower Part Of Instance Of … Basic Concepts:  Basic Concepts Document-Term relation: Crisp W: D x T -> {0,1} Fuzzy W: D x T -> [0,1] Term-Term Relation R: Man-Made: Dictionaries, Synonyms, Ontologies,… Computer-Made: WTW, Association Rules, Similarity and Inclusion Measures,… Term Weighting:  Term Weighting Local terms weights (lij): Binary (fij) Logarithmic log(1+fij) Normalized ((fij)+(fij/maxkfkj))/2 Term frequency. fij Global terms weights (gi): None 1 Entropy 1+( j(pijlog(pij))/log(n)) Term Weighting:  Term Weighting IDF log(n/j(fij)) GfIdf (j fij)/(j(fij)) Normal 1/(jf2ij)0.5 Probabilistic Inverse log((n-j(fij))/j(fij)) Document normalization None 1 Cosine (j(gilij)2)-0.5 Document-Term Matrix W:  Document-Term Matrix W Binary [ 1 0 0 0 0 1 0 0 0 1 ] [ 0 1 0 1 0 0 0 1 1 1 ] [ 1 1 0 0 1 0 0 0 1 0 ] [ 0 0 1 0 0 0 1 0 0 1 ] [ 0 0 1 0 1 1 0 0 1 0 ] [ 0 0 0 0 0 0 1 0 1 0 ] [ 1 0 0 1 0 0 0 1 0 1 ] [ 0 1 0 0 1 1 1 0 0 0 ] [ 0 0 0 1 0 0 0 1 0 0 ] [ 1 0 0 1 1 0 0 1 1 0 ] TF-IDF [ 0.6 0 0 0 0 0.6 0 0 0 0.6 ] [ 0 0.5 0 0.5 0 0 0 0.5 0.5 0.5 ] [ 0.5 0.5 0 0 0.5 0 0 0 0.5 0 ] [ 0 0 0.6 0 0 0 0.6 0 0 0.6 ] [ 0 0 0.5 0 0.5 0.5 0 0 0.5 0 ] [ 0 0 0 0 0 0 0.7 0 0.7 0 ] [ 0.5 0 0 0.5 0 0 0 0.5 0 0.5 ] [ 0 0.5 0 0 0.5 0.5 0.5 0 0 0 ] [ 0 0 0 0.7 0 0 0 0.7 0 0 ] [ 0.5 0 0 0.5 0.5 0 0 0.5 0.5 0 ] Crisp Document-Term Matrix:  Crisp Document-Term Matrix Fuzzy Document-Term Matrix:  Fuzzy Document-Term Matrix The WT.W approach:  The WT.W approach Term-Term Matrix WTW:  Term-Term Matrix WTW [ 0.1033 0.0250 0 0.0450 0.0450 0.0333 0 0.0450 0.0450 0.0583 ] [ 0.0250 0.0700 0 0.0200 0.0500 0.0250 0.0250 0.0200 0.0450 0.0200 ] [ 0 0 0.0583 0 0.0250 0.0250 0.0333 0 0.0250 0.0333 ] [ 0.0450 0.0200 0 0.1150 0.0200 0 0 0.1150 0.0400 0.0450 ] [ 0.0450 0.0500 0.0250 0.0200 0.0950 0.0500 0.0250 0.0200 0.0700 0 ] [ 0.0333 0.0250 0.0250 0 0.0500 0.0833 0.0250 0 0.0250 0.0333 ] [ 0 0.0250 0.0333 0 0.0250 0.0250 0.1083 0 0.0500 0.0333 ] [ 0.0450 0.0200 0 0.1150 0.0200 0 0 0.1150 0.0400 0.0450 ] [ 0.0450 0.0450 0.0250 0.0400 0.0700 0.0250 0.0500 0.0400 0.1400 0.0200 ] [ 0.0583 0.0200 0.0333 0.0450 0 0.0333 0.0333 0.0450 0.0200 0.1117 ] WTW Term-Term Matrix:  WTW Term-Term Matrix Association Rules:  Association Rules The Rows correspond to documents. The Columns correspond to terms. We want to find association rules between terms. Rules A=>B, are defined by: Confidence or Relative Cardinality:  Confidence or Relative Cardinality Compositional Approach:  Compositional Approach Sup-Prod Composition:  Sup-Prod Composition Fuzzy Terms:  Fuzzy Terms Meaning of term is a fuzzy set of documents. µ(t)= 0.8/d1+ 0.2/d2+ 0.0/d3+… Meaning of a document is a fuzzy set of terms. (d)= 0.1/t1+ 0.0/t2+ 0.8/t3+… Another interpretation of the document-term matrix: W = [µ(t1) µ(t2) µ(t3) …] WT = [(d1) (d2) (d3) …] Fuzzy Sets:  Fuzzy Sets Inclusion measures: Similarity measures: Term-Document Matrix WT:  Term-Document Matrix WT Fuzzy [0.6 0.0 0.5 0.0 0.0 0.0 0.5 0.0 0.0 0.4] [0.0 0.4 0.5 0.0 0.0 0.0 0.0 0.5 0.0 0.0] [0.0 0.0 0.0 0.6 0.5 0.0 0.0 0.0 0.0 0.0] [0.0 0.4 0.0 0.0 0.0 0.0 0.5 0.0 0.7 0.4] [0.0 0.0 0.5 0.0 0.5 0.0 0.0 0.5 0.0 0.4] [0.6 0.0 0.0 0.0 0.5 0.0 0.0 0.5 0.0 0.0] [0.0 0.0 0.0 0.6 0.0 0.7 0.0 0.5 0.0 0.0] [0.0 0.4 0.0 0.0 0.0 0.0 0.5 0.0 0.7 0.4] [0.0 0.4 0.5 0.0 0.5 0.7 0.0 0.0 0.0 0.4] [0.6 0.4 0.0 0.6 0.0 0.0 0.5 0.0 0.0 0.0] Crisp [ 1 0 0 0 0 1 0 0 0 1 ] [ 0 1 0 1 0 0 0 1 1 1 ] [ 1 1 0 0 1 0 0 0 1 0 ] [ 0 0 1 0 0 0 1 0 0 1 ] [ 0 0 1 0 1 1 0 0 1 0 ] [ 0 0 0 0 0 0 1 0 1 0 ] [ 1 0 0 1 0 0 0 1 0 1 ] [ 0 1 0 0 1 1 1 0 0 0 ] [ 0 0 0 1 0 0 0 1 0 0 ] [ 1 0 0 1 1 0 0 1 1 0 ] 2-D Projection of W:  2-D Projection of W Fuzzy terms 1, 9:  Fuzzy terms 1, 9 Similarity using Min:  Similarity using Min Similarity using Prod:  Similarity using Prod Fuzzy Terms 2:  Fuzzy Terms 2 Meaning of term is a fuzzy set of terms. µ(t)= 0.5/t1+ 1.0/t2+ 0.2/t3+… Meaning of a document is a fuzzy set of documents. (d)= 0.1/d1+ 0.5/d2+ 0.1/d3+… Another interpretation of the term-term and document-document matrix: T = [µ(t1) µ(t2) µ(t3) …] D = [(d1) (d2) (d3) …] Application to BeMySearch:  Application to BeMySearch Query Expansion. Query Refinement. Re-Ranking. Navigation. User Profile. … Conclusions:  Conclusions General Framework: WTW Association Rules Fuzzy relation composition Fuzzy terms Relies on a fuzzy document-term relation. Traditionally probabilistic approach. Necessity of really fuzzy approach. Future Work:  Future Work More Relations: Sentence - Term Paragraph - Sentence Document - Paragraph Document – Document Clustering techniques Cluster of documents or paragraphs or sentences. Cluster of terms. Questions & Comments:  Questions & Comments

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December 5, 2020

December 5, 2020

December 5, 2020

December 5, 2020

December 5, 2020

December 5, 2020

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