Aggregated search: a new information retrieval paradigm

International audienceTraditional search engines return ranked lists of search results. It is up to the user to scroll this list, scan within different documents and assemble information that fulfill his/her information need. Aggregated search represents a new class of approaches where the information is not only retrieved but also assembled. This is the current evolution in Web search, where diverse content (images, videos, ...) and relational content (similar entities, features) are included in search results. In this survey, we propose a simple analysis framework for aggregated search and an overview of existing work. We start with related work in related domains such as federated search, natural language generation and question answering. Then we focus on more recent trends namely cross vertical aggregated search and relational aggregated search which are already present in current Web search

Investigating the document structure as a source of evidence for multimedia fragment retrieval

International audienceMultimedia objects can be retrieved using their context that can be for instance the text surrounding them in documents. This text may be either near or far from the searched objects. Our goal in this paper is to study the impact, in term of effectiveness, of text position relatively to searched objects. The multimedia objects we consider are described in structured documents such as XML ones. The document structure is therefore exploited to provide this text position in documents. Although structural information has been shown to be an effective source of evidence in textual information retrieval, only a few works investigated its interest in multimedia retrieval. More precisely, the task we are interested in this paper is to retrieve multimedia fragments (i.e. XML elements having at least one multimedia object). Our general approach is built on two steps: we first retrieve XML elements containing multimedia objects, and we then explore the surrounding information to retrieve relevant multimedia fragments. In both cases, we study the impact of the surrounding information using the documents structure.Our work is carried out on images, but it can be extended to any other media, since the physical content of multimedia objects is not used. We conducted several experiments in the context of the Multimedia track of the INEX evaluation campaign. Results showed that structural evidences are of high interest to tune the importance of textual context for multimedia retrieval. Moreover, the proposed approach outperforms state of the art approaches

When temporal expressions help to detect vital documents related to an entity

International audienceIn this paper we aim at filtering documents containing timely relevant information about an entity (e.g., a person, a place, an organization) from a document stream. These documents that we call vital documents provide relevant and fresh information about the entity. The approach we propose leverages the temporal information reflected by the temporal expressions in the document in order to infer its vitality. Experiments carried out on the 2013 TREC Knowledge Base Acceleration (KBA) collection show the effectiveness of our approach compared to state-of-the-art ones

Détection d'informations vitales pour la mise à jour de bases de connaissances

National audienceMettre à jour une base de connaissances est une problématique actuelle qui suit l'évolution permanente du web de données liées. De nombreuses approches ont été proposées afin d'extraire dans des documents textuels la connaissance à mettre à jour. Ces approches arrivent à maturité mais reposent sur l'hypothèse selon laquelle le corpus adéquat a déjà été constitué. Dans la majorité des cas, les documents à prendre en compte sont sélectionnés manuellement ce qui rend difficile une mise à jour exhaustive de la base. Dans cet article nous proposons une approche originale visant à identifier automatiquement dans un flux de documents du web les éléments pouvant apporter de la connaissance nouvelle sur des instances déjà représentées dans une base

IRIT at TREC Knowledge Base Acceleration 2013: Cumulative Citation Recommendation Task

International audienceThis paper describes the IRIT lab participation to the Cumulative Citation Recommendation task of the TREC 2013 Knowledge Base Acceleration Track. In this task, we are asked to implement a system which aims to detect “Vital” documents that a human would want to cite when updating the Wikipedia article for the target entity. Our approach is built on two steps. First, for each topic (entity), we retrieve a set of potential relevant documents containing at least one entity mention. These documents are then classified using a supervised learning algorithm to identify which ones are vital. We submitted three runs using different combinations of features. Obtained results are presented and discussed

Accelerating the update of knowledge base instances by detecting vital information from a document stream

International audienceIn this paper we aim at filtering documents containing timely relevant information about an entity (e.g., a person, a place, an organization) from a document stream. These documents that we call vital documents provide relevant and fresh information about the entity. The approach we propose leverages the temporal information reflected by the temporal expressions in the document in order to infer its vitality. Experiments carried out on the 2013 TREC Knowledge Base Acceleration (KBA) collection show the effectiveness of our approach compared to state-of-the-art ones

Apport du Web et du Web de Données pour la recherche d'attributs

National audienceNous nous intéressons dans cet article aux requêtes de type entité pour lesquelles on souhaite renvoyer un ensemble d’attributs (propriétés) et leurs valeurs. Ces attributs peuvent être collectés à partir de plusieurs sources et agrégés dans un seul document. Par exemple l’entité "France" peut avoir les attributs "Langue officielle: Français", "Villes:Paris, Toulouse, Lyon, ..." et "Population:65350000(en 2012)". Un attribut peut être monovalué ou multivalué, et peut éventuellement dépendre d’autres dimensions. Pour chercher les attributs d’une entité, nous avons exploité deux sources: les tables relationnelles du Web (issues du HTML) et le Web de Données. Afin d’évaluer le potentiel de ces sources, nous avons mis en place une évaluation utilisateur. Les analyses ont montré l’utilité de combiner ces deux sources pour répondre aux requêtes de type entité

IRIT, GeoComp, and LIUPPA at the TREC 2013 Contextual Suggestion Track

International audienceIn this paper we give an overview of the participation of the IRIT, GeoComp, and LIUPPA labs in the TREC 2013 Contextual Suggestion Track. Our framework combines existing geo-tools or services (e.g., Google Places, Yahoo! BOSS Geo Services, PostGIS, Gisgraphy, GeoNames) and ranks results according to features such as context-place distance, place popularity, and user preferences. We participated in the Open Web and ClueWeb12 sub-tracks with runs IRIT.OpenWeb and IRIT.ClueWeb