Tagonto Otm

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  • 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. Reect 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 oers:
  • 7. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Tagonto Overview Tagonto can be described as a folksonomy aggregator which oers: Tagonto Functionalities A tag-based search engine. Ontology-based query renement. 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 oers: Tagonto Functionalities A tag-based search engine. Ontology-based query renement. 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) rene the query and repeat from 2.
  • 9. Introduction Tagonto Overview Matching and Disambiguation Tagonto Implementation Conclusion and Future Work Matching tags and concepts Denition: 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 Denition: 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. Denition: Matching A matching between O and F is dened as a relation MF 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.