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Semantic Access to Data from the Web

Semantic Access to Data from the Web. Raquel Trillo * , Laura Po + , Sergio Ilarri * , Sonia Bergamaschi + and E. Mena *. +. *. Distributed Information Systems G roup , http://sid.cps.unizar.es University of Zaragoza, Spain.

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Semantic Access to Data from the Web

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  1. Semantic Access to Data from the Web Raquel Trillo*, Laura Po+, Sergio Ilarri*, Sonia Bergamaschi+ and E. Mena* + * Distributed Information Systems Group, http://sid.cps.unizar.es University of Zaragoza, Spain Databases Group, http://www.dbgroup.unimo.it/ Univ. Of Modena e Reggio Emilia, Italy 1st International Workshop on Interoperability through Semantic Data and Service Integration (ISDSI’09) Cagmoli (Genova), Italy, 25th June 2009

  2. Outline • Introduction. • Basic Architecture of the system: • Discovering the Semantics of User Keywords. • Semantics-Guided Data Retrieval. • Improvements to the Basic Architecture: • Probabilistic Word Sense Disambiguation. • Retrieval of Synonyms of User Keywords. • Conclusions and Future Work. ISDSI’09 Cagmoli (Italy), 25th June 2009

  3. Introduction • Search engines have become the best allies of users. • They index most no hidden Web. • They succeed when users ask for popular information on the Web. • Traditional Search engines are based on syntactic techniques (no semantics): • Polysemous Words: with several meanings (senses/interpretations). • Example: Mouse (animal, Mickey Mouse, input device, etc). • Synonymous Words: Different representations (words) with the same meaning. • Example: automobile or car • Example: lorry or truck ISDSI’09 Cagmoli (Italy), 25th June 2009

  4. Introduction Lorry 4,760,000 Truck 172,000,000 ISDSI’09 Cagmoli (Italy), 25th June 2009

  5. Introduction: Semantic Search • Semantic Search engines can overcome the problems of traditional search engines. • Consider the semantics of keywords and not only its representation (how they are written). • Our proposal: • Classify the results of traditional search in different categories by considering their possible meanings. • Considers the synonyms of the user keywords to retrieve more pages. ISDSI’09 Cagmoli (Italy), 25th June 2009

  6. Introduction: Web Clustering • Along last decades, different techniques to cluster documents have appeared: • Traditional clustering algorithms cannot be applied to search result clustering. • Features that a clustering for web search should: • Separate relevant pages for the user from irrelevant ones. • Provide browsable summaries of each cluster. • Be applied to snippets and not to whole pages. • Be incremental and provide results ASAP. • Allow the overlapping between groups. ISDSI’09 Cagmoli (Italy), 25th June 2009

  7. Outline Introduction. Basic Architecture of the system: Discovering the Semantics of User Keywords: Obtaining the possible keyword senses (meanings). Selecting the most probable sense of each user keyword. Semantics-Guided Data Retrieval: Lexical annotations of results of a traditional search. Categorization of results. Improvements to the Basic Architecture: Conclusions and Future Work. ISDSI’09 Cagmoli (Italy), 25th June 2009

  8. Basic Architecture of the system Discovering the semantics of User keywords Extraction of keyword senses Keywords Possible keyword senses Disambiguation of keyword senses Selected senses Semantics-Guided Data Retrieval • Goal: Discover the intended meaning of each user keyword. • How: Word Sense Disambiguation Algorithm performs in two phases: • Phase 1: Discover the possible meanings (senses) from semantic resources such as Ontologies, Thesaurus, etc. • Phase 2: For each keyword select one intended meaning by considering the context. Search keywords in traditional search engines Hits (results of a traditional SE) Lexical annotation of hits: title and snippet Annotated Hits by considering the Possible Keyword ss Categorization of hits Clusters or categories of hits Selection of the most probable intended category Semantic Cluster of Hits

  9. Obtaining the Possible Keyword Senses of each User Keyword • Consulting a well-known general-pupose shared thesaurus such as WordNet: • Advantages: It is fast and provides a reliable set of senses. • Disadvantages: It does not cover with the same detail different domains of knowledge. Ex: The meaning of developer as “sb who designs and implements software” does not appear. • Consulting the knowledge stored in different pools of ontologies available on the Web and using synonym probability measures to remove redundant interpretations: • Advantages: The more ontologies consulted, the more chances to find the semantics assigned by the user. • Disadvantages: It could introduce noise and irrelevant information. ISDSI’09 Cagmoli (Italy), 25th June 2009

  10. Obtaining the Possible Keyword Senses of each User Keyword • Option 1: Consulting a well-known general-pupose shared thesaurus such as WordNet. • Option 2: Consulting the knowledge stored in different pools of ontologies and using synonym probability measures to remove redundant interpretations. • The trade-off between the two approaches is not totally clear: • Implement both options beginning by the Wordnet one. • Perform experimental evaluation to decide which approach to consider. ISDSI’09 Cagmoli (Italy), 25th June 2009

  11. Selecting the most probable sense of each User Keyword Discovering the semantics of User keywords Extraction of keyword senses Possible keyword senses Disambiguation of keyword senses Selected senses • Goal: Select the most probable intended meaning for each user keyword. • How: Using Word Sense Disambiguation techniques: • Many features can be considered in the context of written document, but here the process is more complex. • No syntax of whole sentences, few keywords (<5), etc. ISDSI’09 Cagmoli (Italy), 25th June 2009

  12. Selecting the most probable sense of each User Keyword • Try to emulate the behaviour of a human by considering the possible meanings of the rest of keywords: • If star appears in the context “Star Hollywood”, then the most probable intended meaning is “famous actor/actress”. • If star appears in the context “Star Sky”, then the most probable intended meaning is “celestial body”. • The architecture proposed does not depend on a particular Word Sense Disambiguation technique: • Probabilistic Word Sense Disambiguation techniques that combine different algorithms. ISDSI’09 Cagmoli (Italy), 25th June 2009

  13. Outline Introduction. Basic Architecture of the system: Discovering the Semantics of User Keywords: Obtaining the possible keyword senses (meanings). Selecting the most probable sense of each user keyword. Semantics-Guided Data Retrieval: Lexical annotations of results of a traditional search. Categorization of results. Improvements to the Basic Architecture: Conclusions and Future Work. ISDSI’09 Cagmoli (Italy), 25th June 2009

  14. Semantics-Guided Data Retrieval • Goal: Select hits relevant for the user and filter irrelevant ones. • Phase 1: Retrieval by using traditional techniques. • Phase 2 and 3: Lexical annotations of hits and classification of them by using Word Sense Disambiguation. • Phase 4: Selection of the category corresponding to the selected senses. • How: Keywords Semantics-Guided Data Retrieval Selected senses Search keywords in traditional search engines Possible keyword senses Hits (results of a traditional SE) Lexical annotation of hits: title and snippet Annotated Hits by considering the Possible Keyword ss Categorization of hits Cluster or categories of hits Selection of the most probable intended category Semantic Cluster of Hits

  15. Goal: Associated to each user keyword that appears in each returned hit (title, URL and snippets) a meaning by considering the possible the meaning of the keyword. • Cleasing each hit to remove stopwords and mark without semantic information. • Performing WSD by considering the context of the words (its neighbour words in a window). • How: Hits (results of a traditional SE). For each hit title, URL and Snippet Cleasing of hits Possible keyword senses Lexical Annotation Annotated Hits by considering the Possible Keyword Senses Lexical Annotation of the Results of a Traditional Search Engine

  16. Lexical Annotation of the Results of a Traditional Search Engine • Only information from snippets is used to perform the lexical anotation • New senses for words appears but only when they are widespreaded they are integrated in semantic resources Hits (results of a traditional SE). For each hit title, URL and Snippet Cleasing of hits Possible keyword senses Lexical Annotation Annotated Hits by considering the Possible Keyword Senses

  17. Categorization of the Annotated Results • Goal: Associated to annotated hit a category. • How: • Defining the categories considering the possible keyword senses. • Associated to each hit a category by considering its annotations. K1 (Hollywood): S11 K2 (Star): S21(Celestial body), S22 (Actor/Actres) C1(S11, S21): Hit1, … C2(S1U, S21): Hit4, ... C3(S11, S22): Hit2, Hit3,... C4(S1U, S22): ... C5(S1U, S2U): ... Hit1(s11, s21), Hit2 (s11, s22), Hit3(s11, s22), Hit4(s11, ?),… ISDSI’09 Cagmoli (Italy), 25th June 2009

  18. Categorization of the Annotated Results • Select the category (cluster) that correspond to the selected senses of the user. • The hits of each category are orderd following the ranking return by the search engine. C1(S11, S21): Hit1, … C2(S1U, S21): Hit4, ... C3(S11, S22): Hit2, Hit3,... C4(S1U, S22): ... C5(S1U, S2U): ... C6(S11, S2U): ... Unknown Sense for Hollywood Unknown Sense for star ISDSI’09 Cagmoli (Italy), 25th June 2009

  19. Problems of Basic Architecture • Problem 1: The system only selects the most probable intended category but the user can be interested in other one. • Problem 2: Sometimes, even for a human it is very difficult to decide which is the meaning which is being used for a word. • Problem 3: The system is not considering the synonyms of the keywords ISDSI’09 Cagmoli (Italy), 25th June 2009

  20. Outline Introduction. Basic Architecture of the system: Discovering the Semantics of User Keywords. Semantics-Guided Data Retrieval. Improvements to the Basic Architecture: Probabilistic Word Sense Disambiguation. Retrieval of Synonyms of User Keywords. Conclusions and Future Work. ISDSI’09 Cagmoli (Italy), 25th June 2009

  21. Probabilistic Word Sense Disambiguation • Show more intrepretations to the user: Instead of only showing to the user the category corresponding to the most probable senses, showing him/her all the categories sorted by considering the probability associated to each category. C1(S11, S21): Hit1, … C2(S1U, S21): Hit4, ... C3(S11, S22): Hit2, Hit3,... C4(S1U, S22): ... C5(S1U, S2U): ... C6(S11, S2U): ... C3(S11, S22): Hit2, Hit3,... C4(S1U, S22): ... C1(S11, S21): Hit1, … C2(S1U, S21): Hit4, ... C6(S11, S2U): ... C5(S1U, S2U): ... ISDSI’09 Cagmoli (Italy), 25th June 2009

  22. Probabilistic Word Sense Disambiguation • Probabilistic Word Sense Disambiguation: • It is based on a probabilistic combination of different WSD algorithms so the process is not affected by the effectiveness of a single algorithm. • Associate a probability to each lexical annotation that indicates the reliability level of the annotation. • So, each hit will be associated to several categories with a certain probability. 0,05 0,75 0,20

  23. Retrieval of Synonyms of User Keywords • Probabilistic Word Sense Disambiguation: • Associate to each hit the product of the probabilities of its annotations and use this value to rank the hits clasiffied inside a category (group of cluster). • Enrichment of the clusters with retrieval of synonyms of the senses that represent that category. C3(S11 (hollywod), S22(star)): Hit2, Hit3,... Celebrity, actor/actress ISDSI’09 Cagmoli (Italy), 25th June 2009

  24. Outline Introduction. Basic Architecture of the system: Discovering the Semantics of User Keywords. Semantics-Guided Data Retrieval. Improvements to the Basic Architecture: Probabilistic Word Sense Disambiguation. Retrieval of Synonyms of User Keywords. Conclusions and Future Work. ISDSI’09 Cagmoli (Italy), 25th June 2009

  25. Related Work • There exist several techniques for clustering the results of a web search, but most of them based only on statistics techniques. • Some approaches consider semantics, such as: • Hao et al. 2008: Uses only WordNet and assumes a predefined set of categories. • Hemayati et al. 2007: Limited to queries with a single keyword and does not allow overlapping categories. ISDSI’09 Cagmoli (Italy), 25th June 2009

  26. Conclusions and Future Work • We have proposed an architecture to group the results of a standard search engine in different categories: • The categories are defined by the senses of the input keywords. • The system has desirable features in this kind of systems. • Non-popular searches do not remain hidden. • Next steps: • Implementation of the system proposed. • Design a set of experiments with users to evaluate it. ISDSI’09 Cagmoli (Italy), 25th June 2009

  27. Semantic Access to Data from the Web Raquel Trillo*, Laura Po+, Sergio Ilarri*, Sonia Bergamaschi+, E. Mena* + * http://www.dbgroup.unimo.it/ Univ. Of Modena e Reggio Emilia http://sid.cps.unizar.es Univ. Zaragoza Grazie Mille! Thank you very much! Questions and suggestions. 1st International Workshop on Interoperability through Semantic Data and Service Integration ISDSI’09 Cagmoli (Genova), Italy, 25th June 2009

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