1. 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
5/5/2019
ACM WIDM'2005

2. 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
5/5/2019
ACM WIDM'2005

3. Objectives
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
5/5/2019
ACM WIDM'2005

4. Ontologies 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
5/5/2019
ACM WIDM'2005

5. WordNet
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)
5/5/2019
ACM WIDM'2005

6. 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
5/5/2019
ACM WIDM'2005

7. A Fragment of the WordNet Is-A Hierarchy
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ACM WIDM'2005

8. 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
5/5/2019
ACM WIDM'2005

9. Example 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
5/5/2019
ACM WIDM'2005

10. 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
5/5/2019
ACM WIDM'2005

11. Evaluation Method Type Correlation Rada 1989 Edge Counting 0.59
Wu 1994
0.74
Li 2003
0.82
Leackok 1998
Richardson 1994
0.63
Resnik 1999
Info. Content
0.79
Lin 1993
Lord 2003
Jiang 1998
0.83
Tversky 1977
Feature Based
0.73
Rodriguez 2003
Hybrid
0.71
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ACM WIDM'2005

12. Observations
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 et.al. 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
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ACM WIDM'2005

13.
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ACM WIDM'2005

14. 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)
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ACM WIDM'2005

15. SSRM Query term re-weighting similar terms reinforce each other
Query term expansion with synonyms and similar terms
Document similarity
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ACM WIDM'2005

16. Query Term Expansion
5/5/2019
ACM WIDM'2005

17. Observations 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)
5/5/2019
ACM WIDM'2005

18. Evaluation of SSRM
SSRM is evaluated through intellisearch a 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
5/5/2019
ACM WIDM'2005

19.
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ACM WIDM'2005

20. Methods 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
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ACM WIDM'2005

21. Experimental Results
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ACM WIDM'2005

22. 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
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ACM WIDM'2005

23. Evaluation on MedLine
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ACM WIDM'2005

24. Conclusions
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
5/5/2019
ACM WIDM'2005

25. 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
5/5/2019
ACM WIDM'2005

26. Try our system on the Web
Semantic Similarity System:
SRRM:
5/5/2019
ACM WIDM'2005