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A Semantic Model of Selective Dissemination of Information for Digital Libraries

A Semantic Model of Selective Dissemination of Information for Digital Libraries Authors: J. M. Morales-del-Castillo¹, R. Pedraza-Jiménez², A. A. Ruiz³, E. Peis⁴, and E. Herrera-Viedma⁵. Basic Ideea.

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A Semantic Model of Selective Dissemination of Information for Digital Libraries

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  1. A Semantic Model of Selective Dissemination of Information for Digital Libraries Authors: J. M. Morales-del-Castillo¹, R. Pedraza-Jiménez², A. A. Ruiz³, E. Peis⁴, and E. Herrera-Viedma⁵

  2. Basic Ideea • Develop a multi-agent Selective Dissmination of Information (SDI) platform capable of generating alerts and recommandations of documents for users, according to their personal profiles • Appling Semantic Web technologies for achiving more efficient information managment and improving agent-agent and user-agent communication 2

  3. SDI Components • Thesaurus • Enables organizing the most relevant concepts in a specific domain, by defining semantic relations between them. • User profiles • Structured representations that contain personal data, interest and preferences of users. • RSS feeds • Used as “current awareness bulletins” to generate personalized bibliographic alerts • Recommendation log file • Each document in the repository has an associated log file that includes the listing of evaluations assigned to that resource by different users 3

  4. Thesaurus The creation of a thesaurus includes four phases: • Pre-processing of documents • Prepare the document parametrization by removing the elements regarded as superfluous in 3 stages: • Eliminate all the tags (HTML, XML, etc) • Standardization of the words in the document including removing texts articles, determiners, auxiliary verbs, conjunctions, prepositions, … • Stemming all the terms left using the WordNet algorithm(Morphy) • Parameterizing the selected terms • Final terms are quantified by assigning weights obtained by the application of the scheme term frequency – inverse document frequency (tf-idf)

  5. Thesaurus • Conceptualizing their lexical stems • The associated meaning of each term (lemma) are extract by searching them on WordNet, which returns a group of synsets associated to each word (including hypernyms and hyperonyms) • Generating a lattice or graph that shows the relation between the identified concepts • Using formal concept analysis techniques for finding relations from the generated groups, where each node in the graph represents a descriptor(namely a group of synonyms terms) • Clustering of documents depending on the terms(and synonyms) including links to those with which has any relation(hyponymy or hyperonymy) Once the thesaurus is obtained by identifying its terms and the underlying relation between them, it is represented using SKOS vocabulary.

  6. User profiles • Defined with Friend of a Friend(FOAF) vocabulary (generated at registration time) • Containing personal data, interests and preferences of users • 2 Parts: • Public profile: data related to the user's identity and affiliation • Private profile: user interests and preferences about the topic of the alerts he or she wishes to receive • Users must specify keywords and concepts that best define their information needs • This keywords are then compared with the concepts in the thesaurus; if there is an exact math, the introduced term will be return, otherwise the lexically most similar term. • The return term will be suggested to the user and added to its preferences, if this term satisfy he user expectations.

  7. Profile and RSS feeds generation process

  8. Alert generation process

  9. Questions

  10. References • 1. J. M. Morales-del-Castillo:Assistant Professor of Information Science, Library and Information Science Department, University of Granada, Spain • 2. R. Pedraza-Jiménez:Assistant Professor of Information Science, Journalism and Audiovisual Communication Department, Pompeu Fabra University, Barcelona, Spain • 3. A. A. Ruíz: Full Professor of Information Science, Library and Information Science Department, University of Granada. • 4. E. Peis: is Full Professor of Information Science, Library and Information Science Department, University of Granada. • 5. E. Herrera-Viedma: Senior Lecturer in Computer Science, Computer Science and Artificial Intelligence Department, University of Granada.

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