Tagonto Otm

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Tagonto Otm

  1. 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. 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. 3. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Introduction Aim: Improve web search and navigation
  4. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 15. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Disambiguation The disambiguation process is carried out in two steps:
  16. 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. 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. 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. 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. 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. 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. 22. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work User Interface
  23. 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. 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. 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. 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. 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?

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