Semantic similarity methods in wordnet and their application to information retrieval on the web
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Semantic Similarity Methods in WordNet and Their Application to Information Retrieval on the Web PowerPoint PPT Presentation

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Semantic Similarity Methods in WordNet and Their Application to Information Retrieval on the Web. Giannis Varelas Epimenidis Voutsakis Paraskevi Raftopoulou Euripides G.M. Petrakis Evangelos Milios. Semantic Similarity.

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Semantic Similarity Methods in WordNet and Their Application to Information Retrieval on the Web

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Semantic Similarity Methods in WordNet andTheir Application to Information Retrieval onthe Web

Giannis Varelas

Epimenidis Voutsakis

Paraskevi Raftopoulou

Euripides G.M. Petrakis

Evangelos Milios

Semantic Similarity

Semantic Similarity

  • Semantic Similarity relates to computing the conceptual similarity between terms which are not lexicographically similar

    • “car” “automobile”

  • Map two terms to an ontology and compute their relationship in that ontology

Semantic Similarity


  • We investigate several Semantic Similarity Methods and we evaluate their performance


  • We propose the Semantic Similarity Retrieval Model (SSRM) for computing similarity between documents containing semantically similar but not necessarily lexicographically similar terms


Semantic Similarity


  • Tools of information representation on a subject

  • Hierarchical categorization of terms from general to most specific terms

    • object  artifact  construction  stadium

  • Domain Ontologies representing knowledge of a domain

    • e.g., MeSH medical ontology

  • General Ontologies representing common sense knowledge about the world

    • e.g., WordNet

Semantic Similarity


  • A vocabulary and a thesaurus offering a hierarchical categorization of natural language terms

  • More than 100,000 terms

  • An ontology of natural language terms

  • Nouns, verbs, adjectives and adverbs are grouped into synonym sets (synsets)

  • Synsets represent terms or concepts

    • stadium, bowl, arena, sports stadium – (a large structure for open-air sports or entertainments)

Semantic Similarity

WordNet Hierarchies

  • The synsets are also organized into senses

  • Senses: Different meanings of the same term

  • The synsets are related to other synsets higher or lower in the hierarchy by different types of relationships e.g.

    • Hyponym/Hypernym (Is-A relationships)

    • Meronym/Holonym (Part-Of relationships)

  • Nine noun and several verb Is-A hierarchies

Semantic Similarity

A Fragment of the WordNet Is-A Hierarchy

Semantic Similarity

Semantic Similarity

Semantic Similarity Methods

  • Map terms to an ontology and compute their relationship in that ontology

  • Four main categories of methods:

    • Edge counting: path length between terms

    • Information content: as a function of their probability of occurrence in corpus

    • Feature based: similarity between their properties (e.g., definitions) or based on their relationships to other similar terms

    • Hybrid: combine the above ideas

Semantic Similarity


  • Edge counting distance between “conveyance” and “ceramic” is 2

  • An information content method, would associate the two terms with their common subsumer and with their probabilities of occurrence in a corpus

Semantic Similarity

Semantic Similarity on WordNet

  • The most popular methods are evaluated

  • All methods applied on a set of 38 term pairs

  • Their similarity values are correlated with scores obtained by humans

  • The higher the correlation of a method the better the method is

Semantic Similarity


Semantic Similarity


  • Edge counting/Info. Content methods work by exploiting structure information

  • Good methods take the position of the terms into account

  • Higher similarity for terms which are close together but lower in the hierarchy e.g., [Li 2003]

  • Information Content is measured on WordNet rather than on corpus [Seco2002]

  • Similarity only for nouns and verbs

  • No taxonomic structure for other p.o.s

Semantic Similarity

Semantic Similarity

Semantic Similarity Retrieval Model (SSRM)

  • Classic retrieval models retrieve documents with the same query terms

  • SSRM will retrieve documents which also contain semantically similar terms

  • Queries and documents are initially assigned tfxidf weights

  • q=(q1,q2,…qN) , d=(d1,d2,…dN)

Semantic Similarity


  • Query term re-weighting

    similar terms reinforce each other

  • Query term expansion with synonyms and similar terms

  • Document similarity

Semantic Similarity

Query Term Expansion

Semantic Similarity


  • Specification of T ?

  • Large T may lead to topic drift

  • Word sense disambiguation for expanding with the correct sense

  • Expansion with co-concurring terms?

    • SVD, local/global analysis

  • Semantic similarity between terms of different parts of speech?

  • Work with compound terms (phrases)

Semantic Similarity

Evaluation of SSRM

  • SSRM is evaluated through intellisearcha system for information retrieval on the WWW

  • 1,5 Million Web pages with images

  • Images are described by surrounding text

  • The problem of image retrieval is transformed into a problem of text retrieval

Semantic Similarity

Semantic Similarity


  • Vector Space Model (VSM)

  • SSRM

  • Each method is represented by a precision/recall plot

  • Each point is the average precision/recall over 20 queries

  • 20 queries from the list of the most frequent Google image queries

Semantic Similarity

Experimental Results

Semantic Similarity

MeSH and MedLine

  • MeSH: ontology for medical and biological terms by the N.L.M.

    • 22,000 terms

  • MedLine: the premier bibliographic medical database of N.L.M.

    • 13 Million references

Semantic Similarity

Evaluation on MedLine

Semantic Similarity


  • Semantic similarity methods approximated the human notion of similarity reaching correlation up to 83%

  • SSRM exploits this information for improving the performance of retrieval

  • SSRM can work with any semantic similarity method and any ontology

Semantic Similarity

Future Work

  • Experimentation with more data sets (TREC) and ontologies

  • Extend SSRM to work with

    • Compound terms

    • More parts of speech (e.g., adverbs)

    • Co-occurring terms

    • More terms relationships in WordNet

    • More elaborate methods for specification of thresholds

Semantic Similarity

Try our system on the Web

  • Semantic Similarity System:

  • SRRM:

Semantic Similarity

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