Évènements

30 mai
30/05/2016 10:00

RECHERCHE

Soutenance de thèse : Mazen ALSAREM

Semantic Snippets via Query-Biased Ranking of Linked Data Entities

Doctorant : Mazen ALSAREM

Laboratoire INSA : LIRIS UMR 5205
Ecole doctorale : ED 512 Informatique et Mathématiques

In our knowledge-driven society, the acquisition and the transfer of knowledge play a principal role. Web search engines are somehow tools for knowledge acquisition and transfer from the web to the user. The search engine results page (SERP) consists mainly of a list of links and snippets (excerpts from the results). The snippets are used to express, as efficiently as possible, the way a web page may be relevant to the query. As an extension of the existing web, the semantic web or ``web 3.0'' is designed to convert the presently available web of unstructured documents into a web of data consumable by both human and machines. The resulting web of data and the current web of document coexist and interconnect via multiple mechanisms, such as the embedded

structured data, or the automatic annotation. In this thesis, we introduce a new interactive artifact for the SERP: the ``semantic snippet''. Semantic snippets rely on the coexistence of the two webs to facilitate the transfer of knowledge to the user thanks to a semantic contextualization of the user's information need. It makes apparent the relationships between the information need and the more relevant entities present in the web page.The generation of semantic snippets is mainly based on the automatic annotation of the LOD's entities in web pages. The annotated entities have different level of importance, usefulness and relevance. Even with state of the art solutions for the

automatic annotations of LOD entities within web pages, there is still a lot of noise in the form of erroneous or off-topic annotations. Therefore, we propose a query-biased algorithm (LDRANK) for the  ranking  of  these  entities.  LDRANK  adopts  a  strategy  based  on  the  linear  consensual combination of several sources of prior knowledge (any form of contextual knowledge, like the textual  descriptions  for  the  nodes  of  the  graph)  to  modify  a  PageRank-like  algorithm.  For generating semantic snippets, we use LDRANK to find the more relevant entities in the web page. Then, we use a supervised learning algorithm to link each selected entity to excerpts from the web

page that highlight the relationship between the entity and the original information need. In order to evaluate our semantic snippets, we integrate them in ENsEN (Enhanced Search Engine), a software system that enhances the SERP with semantic snippets.Finally, we use crowdsourcing to evaluate the usefulness and the efficiency of ENsEN.

Informations complémentaires

  • http://liris.cnrs.fr/
  • Bâtiment Blaise Pascal, salle de réunion 501.337 - 9 avenue Jean Capelle, Villeurbanne