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Introduction     Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work




                                 TagOnto
               Improving Search and Navigation by Combining
                        Ontologies and Social Tags

               S. Bindelli1 , C. Criscione2 , C. A. Curino3 , M. L. Drago3 , D. Eynard3 ,G. Orsi3

                                               1 Trussardi Company
                                              2 Secure Network S.r.l.
                                              3 Politecnico di Milano



                                          ADI Workshop (OTM 2008)
                                               Monterrey (Mexico)


                                             November 9, 2008
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                                  Outline

       Introduction


       Tagonto Overview


       Matching and Disambiguation


       Tagonto Implementation


       Conclusion and Future Work
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                             Introduction
       Aim: Improve web search and navigation
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                             Introduction
       Aim: Improve web search and navigation
       The “high road”: The Semantic Web
           • Mediates the access to existing sources by means of explicit
               representation of data semantics (i.e., RDF and OWL).
           • High switching costs when moving from traditional
               technologies.
           • Implementers with considerable skills.
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                             Introduction
       Aim: Improve web search and navigation
       The “high road”: The Semantic Web
           • Mediates the access to existing sources by means of explicit
               representation of data semantics (i.e., RDF and OWL).
           • High switching costs when moving from traditional
               technologies.
           • Implementers with considerable skills.

       The “low road”: Folksonomies
           • Low commitment technology.
           • Reflect collective intelligence and emergent semantics.
           • Tipically unstructured and uncontrolled.
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                       Tagonto Overview
       Tagonto can be described as a folksonomy aggregator which offers:
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                       Tagonto Overview
       Tagonto can be described as a folksonomy aggregator which offers:
       Tagonto Functionalities
           • A tag-based search engine.
           • Ontology-based query refinement.
           • Visual, ontology-based navigation of tags.
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                       Tagonto Overview
       Tagonto can be described as a folksonomy aggregator which offers:
       Tagonto Functionalities
           • A tag-based search engine.
           • Ontology-based query refinement.
           • Visual, ontology-based navigation of tags.

       Search process
          1. Load a domain ontology O (metrics pre-computation).
          2. Search (keyword-based).
          3. Navigate the results.
          4. (optional) add/remove/modify tags associated to Web
             resources.
          5. (optional) refine the query and repeat from 2.
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                              Matching tags and concepts
       Definition: Folksonomy
       A Folksonomy in TagOnto is represented as a set of pairs

                                    F = {(t1 , r1 ), . . . , (tn , rm )}

       where ti is a term and rj is a web resource.
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                              Matching tags and concepts
       Definition: Folksonomy
       A Folksonomy in TagOnto is represented as a set of pairs

                                    F = {(t1 , r1 ), . . . , (tn , rm )}

       where ti is a term and rj is a web resource.

       Definition: Matching
           • A matching between O and F is defined as a relation
                                                   M⊆F ×C
               allowing multiple associations among tags and concepts.
           • ∀m ∈ M we associate a similarity degree
                                               s : F × C → [0, 1]
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                        Matching Process
       Given a folksonomy F and an ontology O, Tagonto:

     1. accesses the tags in F
           • Web 2.0 APIs.
           • RSS feeds parsing.
           • Page scraping.
     2. matches the tags in F with
        ontology concepts and instances.
     3. for each tag, computes a set of
        related (co-occurrent) tags.
     4. disambiguates multiple matchings
        by updating their similarity
        degrees.
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                        Matching Process
       Given a folksonomy F and an ontology O, Tagonto:

     1. accesses the tags in F
           • Web 2.0 APIs.
           • RSS feeds parsing.
           • Page scraping.
     2. matches the tags in F with
        ontology concepts and instances.
     3. for each tag, computes a set of
        related (co-occurrent) tags.
     4. disambiguates multiple matchings
        by updating their similarity
        degrees.
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                  Matching Computation
       Tagonto relies on an ontology mapper (X-SOM) to compute the
       matchings

                             Language-based                       Semantic
                           Levenshtein Distance            Google Noise Correction
                              Jaro Distance                  Wordnet Similarity
                            Jaccard Similarity               Ontology Structure
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                  Matching Computation
       Tagonto relies on an ontology mapper (X-SOM) to compute the
       matchings

                             Language-based                       Semantic
                           Levenshtein Distance            Google Noise Correction
                              Jaro Distance                  Wordnet Similarity
                            Jaccard Similarity               Ontology Structure

       where:
           • Google Noise: uses the Google “did you mean?” functionality.
           • WordNet Similarity: computes the Leacock-Chodorow
               distance metric in WordNet.
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                          Disambiguation
       The disambiguation process is carried out in two steps:
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                          Disambiguation
       The disambiguation process is carried out in two steps:
       Co-occurrent tags retrieval
           • Using ontology relationships.
           • Neighbors in the tag-clouds.
           • Google Tag-indexes.
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                          Disambiguation
       The disambiguation process is carried out in two steps:
       Co-occurrent tags retrieval
           • Using ontology relationships.
           • Neighbors in the tag-clouds.
           • Google Tag-indexes.

       Disambiguation
          1. Simple filters: e.g., top-k, treshold, etc.
          2. Semantic filters (i.e., ontology-based disambiguation)
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                           Ontology-based disambiguation
       Definition: Root concepts
       Any concept in O associated to tags in F by means of M
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                           Ontology-based disambiguation
       Definition: Root concepts
       Any concept in O associated to tags in F by means of M

       For each multiple matching m ∈ M, Tagonto:
           • matches co-occurrent tags with the concepts in the ontology.
           • constructs a vector of connectivity degrees v, such that v[i] is
               equal to the number of concepts associated to co-occurrent
               tags and connected to the root concept i in the ontology.
                                                v[i]
           • computes a correction factor i = max(v) .
           • if i ≥ avg(v) then increase the matching degree of the
               matching associated to i by a factor α · i ; decrease of the
               same factor otherwise.
           • selects the matching with maximum similarity degree.
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                            Architecture I
       TagontoLIB:
           • Matching algorithms
           • Disambiguation

       TagontoNET:
           • Core search engine functionalities.
           • Ontology loading.
           • Plugin-based communication interfaces with folksonomies.
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                           Architecture II
       TagontoWEB:
           • Results Navigation
               • by co-occurent tags.
               • by navigating ontology concepts.
           • Tags maintenance.
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                           User Interface
Introduction           Tagonto Overview   Matching and Disambiguation        Tagonto Implementation    Conclusion and Future Work



                                                   Performance I
       We measured Tagonto’s response time during:
                 • Ontology loading

                 800                                                       800

                 700                                                       700

                 600                                                       600

                 500                                                       500
       time(s)




                                                                 time(s)
                 400                                                       400
                 300                                                       300
                 200                                                       200
                 100                                                       100
                  0                                                          0
                       0   200 400 600 800 1000 1200 1400 1600                   0   200   400   600    800    1000   1200
                             #CONCEPTS + #INSTANCES                                          INSTANCES
                                 + #PROPERTIES
Introduction   Tagonto Overview            Matching and Disambiguation    Tagonto Implementation   Conclusion and Future Work



                                                    Performance II
           • Matching generation and resources retrieval


                             100

                                  80
               response time(s)




                                  60

                                  40

                                  20

                                   0
                                       0   50       100        150        200        250       300       350
                                                                  trial
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                              Conclusion and Future Work
       Contributions
           • A search engine which combines ontologies and tags.
           • A library to compute matchings between tags and ontology
               concepts.
           • A service-oriented architecture for folksonomy querying and
               aggregation.
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                              Conclusion and Future Work
       Contributions
           • A search engine which combines ontologies and tags.
           • A library to compute matchings between tags and ontology
               concepts.
           • A service-oriented architecture for folksonomy querying and
               aggregation.

       Future Work
           • Dynamic ontology loading.
           • Automatic tagging of Web resources.
Introduction   Tagonto Overview   Matching and Disambiguation   Tagonto Implementation   Conclusion and Future Work



                                               Thank you
                                 More information at:
                http://kid.dei.polimi.it/mediawiki/index.php/TagOnto




                                                 Questions?

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Tagonto Otm

  • 1. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work TagOnto Improving Search and Navigation by Combining Ontologies and Social Tags S. Bindelli1 , C. Criscione2 , C. A. Curino3 , M. L. Drago3 , D. Eynard3 ,G. Orsi3 1 Trussardi Company 2 Secure Network S.r.l. 3 Politecnico di Milano ADI Workshop (OTM 2008) Monterrey (Mexico) November 9, 2008
  • 2. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Outline Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work
  • 3. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Introduction Aim: Improve web search and navigation
  • 4. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Introduction Aim: Improve web search and navigation The “high road”: The Semantic Web • Mediates the access to existing sources by means of explicit representation of data semantics (i.e., RDF and OWL). • High switching costs when moving from traditional technologies. • Implementers with considerable skills.
  • 5. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Introduction Aim: Improve web search and navigation The “high road”: The Semantic Web • Mediates the access to existing sources by means of explicit representation of data semantics (i.e., RDF and OWL). • High switching costs when moving from traditional technologies. • Implementers with considerable skills. The “low road”: Folksonomies • Low commitment technology. • Reflect collective intelligence and emergent semantics. • Tipically unstructured and uncontrolled.
  • 6. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Tagonto Overview Tagonto can be described as a folksonomy aggregator which offers:
  • 7. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Tagonto Overview Tagonto can be described as a folksonomy aggregator which offers: Tagonto Functionalities • A tag-based search engine. • Ontology-based query refinement. • Visual, ontology-based navigation of tags.
  • 8. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Tagonto Overview Tagonto can be described as a folksonomy aggregator which offers: Tagonto Functionalities • A tag-based search engine. • Ontology-based query refinement. • Visual, ontology-based navigation of tags. Search process 1. Load a domain ontology O (metrics pre-computation). 2. Search (keyword-based). 3. Navigate the results. 4. (optional) add/remove/modify tags associated to Web resources. 5. (optional) refine the query and repeat from 2.
  • 9. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Matching tags and concepts Definition: Folksonomy A Folksonomy in TagOnto is represented as a set of pairs F = {(t1 , r1 ), . . . , (tn , rm )} where ti is a term and rj is a web resource.
  • 10. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Matching tags and concepts Definition: Folksonomy A Folksonomy in TagOnto is represented as a set of pairs F = {(t1 , r1 ), . . . , (tn , rm )} where ti is a term and rj is a web resource. Definition: Matching • A matching between O and F is defined as a relation M⊆F ×C allowing multiple associations among tags and concepts. • ∀m ∈ M we associate a similarity degree s : F × C → [0, 1]
  • 11. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Matching Process Given a folksonomy F and an ontology O, Tagonto: 1. accesses the tags in F • Web 2.0 APIs. • RSS feeds parsing. • Page scraping. 2. matches the tags in F with ontology concepts and instances. 3. for each tag, computes a set of related (co-occurrent) tags. 4. disambiguates multiple matchings by updating their similarity degrees.
  • 12. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Matching Process Given a folksonomy F and an ontology O, Tagonto: 1. accesses the tags in F • Web 2.0 APIs. • RSS feeds parsing. • Page scraping. 2. matches the tags in F with ontology concepts and instances. 3. for each tag, computes a set of related (co-occurrent) tags. 4. disambiguates multiple matchings by updating their similarity degrees.
  • 13. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Matching Computation Tagonto relies on an ontology mapper (X-SOM) to compute the matchings Language-based Semantic Levenshtein Distance Google Noise Correction Jaro Distance Wordnet Similarity Jaccard Similarity Ontology Structure
  • 14. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Matching Computation Tagonto relies on an ontology mapper (X-SOM) to compute the matchings Language-based Semantic Levenshtein Distance Google Noise Correction Jaro Distance Wordnet Similarity Jaccard Similarity Ontology Structure where: • Google Noise: uses the Google “did you mean?” functionality. • WordNet Similarity: computes the Leacock-Chodorow distance metric in WordNet.
  • 15. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Disambiguation The disambiguation process is carried out in two steps:
  • 16. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Disambiguation The disambiguation process is carried out in two steps: Co-occurrent tags retrieval • Using ontology relationships. • Neighbors in the tag-clouds. • Google Tag-indexes.
  • 17. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Disambiguation The disambiguation process is carried out in two steps: Co-occurrent tags retrieval • Using ontology relationships. • Neighbors in the tag-clouds. • Google Tag-indexes. Disambiguation 1. Simple filters: e.g., top-k, treshold, etc. 2. Semantic filters (i.e., ontology-based disambiguation)
  • 18. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Ontology-based disambiguation Definition: Root concepts Any concept in O associated to tags in F by means of M
  • 19. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Ontology-based disambiguation Definition: Root concepts Any concept in O associated to tags in F by means of M For each multiple matching m ∈ M, Tagonto: • matches co-occurrent tags with the concepts in the ontology. • constructs a vector of connectivity degrees v, such that v[i] is equal to the number of concepts associated to co-occurrent tags and connected to the root concept i in the ontology. v[i] • computes a correction factor i = max(v) . • if i ≥ avg(v) then increase the matching degree of the matching associated to i by a factor α · i ; decrease of the same factor otherwise. • selects the matching with maximum similarity degree.
  • 20. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Architecture I TagontoLIB: • Matching algorithms • Disambiguation TagontoNET: • Core search engine functionalities. • Ontology loading. • Plugin-based communication interfaces with folksonomies.
  • 21. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Architecture II TagontoWEB: • Results Navigation • by co-occurent tags. • by navigating ontology concepts. • Tags maintenance.
  • 22. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work User Interface
  • 23. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Performance I We measured Tagonto’s response time during: • Ontology loading 800 800 700 700 600 600 500 500 time(s) time(s) 400 400 300 300 200 200 100 100 0 0 0 200 400 600 800 1000 1200 1400 1600 0 200 400 600 800 1000 1200 #CONCEPTS + #INSTANCES  INSTANCES + #PROPERTIES
  • 24. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Performance II • Matching generation and resources retrieval 100 80 response time(s) 60 40 20 0 0 50 100 150 200 250 300 350 trial
  • 25. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Conclusion and Future Work Contributions • A search engine which combines ontologies and tags. • A library to compute matchings between tags and ontology concepts. • A service-oriented architecture for folksonomy querying and aggregation.
  • 26. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Conclusion and Future Work Contributions • A search engine which combines ontologies and tags. • A library to compute matchings between tags and ontology concepts. • A service-oriented architecture for folksonomy querying and aggregation. Future Work • Dynamic ontology loading. • Automatic tagging of Web resources.
  • 27. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Thank you More information at: http://kid.dei.polimi.it/mediawiki/index.php/TagOnto Questions?