Towards Contextual and Structural Relevance Feedback in XML Retrieval

1. Towards Contextual and Structural Relevance Feedback in XML Retrieval Lobna Hlaoua IRIT (Institut de Recherche en Informatique de Toulouse) Equipe SIG-RI (Systèmes d’Informations Généralisées) 118, route de Narbonne - 31062 Toulouse cedex 04

2. Outline • Context • Relevance Feedback in XML Retrieval • Contextual Relevance Feedback • Structural Relevance Feedback • Conclusion & prospects

3. -- --- ------ ----- ------ -- --- ------ ----- ------ -- --- ------ ----- ------ -- --- ------ ----- ------ -- --- ------ ----- ------ -- --- ------ ----- ------ -- --- ------ ----- ------ -- --- ------ ----- ------ Context: XML Retrieval Traditional IR - Document is atomic unit - user can be submerged by noisy subjects

4. <book date-publi=‘2000’> • <title> Ontologies</title> • <author> J.Dupond</author> • <chapter> • <title> history of ontology</title> • <section num= "1" > • <title> Introduction</title> • <para> ...ontology should be seen only as an interdiscipline... </para> • </section> • <section num= " 2 " > • <title> What is Ontology</title> • <para> ...An ontology is an explicit specification • of a conceptualization... </para> • <para>In the philosophical sense, we may refer • to an ontology as a particular system of • categories accounting for a certain vision • of the world….. </para> • </section> • </chapter> • <chapter>….</chapter> • </book> Context: XML Retrieval XML Retrieval - different granularities

5. <article date-publi=‘2000’> • <title> Ontologies</title> • <author> J.Dupond</author> • <chapter> • <title> history of ontology</title> • <section num= "1" > • <title> Introduction</title> • <para> ...ontology should be seen only as an • interdiscipline... </para> • </section> • <section num= " 2 " > • <title> What is Ontology</title> • <para> ...An ontology is an explicit specification • of a conceptualization... </para> • <para>In the philosophical sense, we may refer • to an ontology as a particular system of • categories accounting for a certain vision • of the world….. </para> • </section> • </chapter> • <chapter>….</chapter> • </article> • <section num= " 2 " > • <title> What is Ontology</title> • <para> ...An ontology is an explicit specification • of a conceptualization... </para> • <para>In the philosophical sense, we may refer • to an ontology as a particular system of • categories accounting for a certain vision • of the world….. </para> • </section> • <para>In the philosophical sense, we may refer • to an ontology as a particular system of • categories accounting for a certain vision • of the world….. </para> Context: XML Retrieval • <article date-publi=‘2000’> • <title> Ontologies</title> • <author> J.Dupond</author> • <chapter> • <title> history of ontology</title> • <section num= "1" > • <title> Introduction</title> • <para> ...ontology should be seen only as an interdiscipline... </para> • </section> • <section num= " 2 " > • <title> What is Ontology</title> • <para> ...An ontology is an explicit specification • of a conceptualization... </para> • <para>In the philosophical sense, we may refer • to an ontology as a particular system of • categories accounting for a certain vision • of the world….. </para> • </section> • </chapter> • <chapter>….</chapter> • </article> - CO (Content Only) Ex: • « ontologies case study » • - CAS (Content And Structure) Ex: • «//article[about(.,ontologies)] • //para[about(., ontologies case • study)] » • « //article[about(.,ontologies)] • //sec[about(., ontologies case • study)] » «//article[about(.,ontologies)]»

6. Relevance Feedback (RF) • RF in traditional IR consists in enriching the initial query using terms extracted from relevant documents. • How RF can be used in XML retrieval ?

7. Relevance Feedback (RF) in XML Retrieval • Problems • How extracting terms from retrieved elements having different semantic • element could (title, section, paragraph, etc.) • … whereas in IR only document units are considered • How structural constraints can be extracted from relevant elements • How enriching XML queries : • adding structural constraints And/OR keywords in both CO and CAS queries?

8. Our approach • Contextual RF • expand the query with expressive words according to the context of the judged component from different granularities. • Structural RF • select the more appropriate generative structure from judged components and adding to CO query.

9. Relevant components Extraction of expressive terms Initial query Process of RF in XML Retrieval Results RC+ NRC Extraction of relevant structure Relevance Feedback Reformulated query

10. Objective: select the more expressive words Let’s consider Er={er1, er2, ..., erk,... erm} , erk={l1,..., lj,.ln} assign a score to terms (ti) occurring in each leaf node (lj) of the relevant elements. Compute the score of terms of in each element (erk). Select the best terms according to number of occurrence in relevant elements. Contextual Relevance Feedback

11. Structural Relevance Feedback • Objective is to select the more appropriate generative structure • retrieve the smallest common ancestor • attribute scores for each structure Si is the structure of relevant element having a joint base with the candidate structure n is a number of the relevant elements d is the distance which separates nodes  is a constant vaying in [0,1]

12. Example of RF in CO query • Initial query Q: “information retrieval” • Structural RF • we suppose that relevant structures have the following scores: • /book/chapter/section/subsection (0.4) • /book/chapter/section/subsection/para (0.4) • /book/chapter/section/title (0.2) • Q2: “article//sec[about(., information retrieval)] ”

13. Conclusion & prospects • Outlined the problem of Relevance Feedback in XML Retrieval. • New challenge in IR : up until now only very few related works • Our investigations can be considered as a first step of a long hard work. • The main idea behind theses investigations: • how keywords and structural constraints can be selected and added to CO queries. • Experiments will be carried out “soon” in INEX framework

14. THANK YOU

15. Contextual Relevance Feedback book title author chapter chapter date-publi=2000 num=2 title Search engine J. Dupont title section section num=2 Web access title para num=1 title para para Internet knowledge …. Search engine Yahoo... Introduction Google….. Leaf node Node

16. Contextual Relevance Feedback ? book ? title author chapter chapter date-publi=2000 ? title Search engine J. Dupont title section section ? Subsection Subsection Web access title para title ? para para Internet knowledge …. Search engine Introduction Yahoo... Google…..

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18. Structural Relevance Feedback 1 book 5 2 3 4 title author chapter chapter 9 6 7 ... 8 title title section section 10 11 12 13 14 para title title para para anc[9] des[4] sca[11,13]