Overview of the TREC 2014 Federated Web Search Track

The TREC Federated Web Search track facilitates research in topics related to federated web search, by providing a large realistic data collection sampled from a multitude of online search engines. The FedWeb 2013 challenges of Resource Selection and Results Merging challenges are again included in FedWeb 2014, and we additionally introduced the task of vertical selection. Other new aspects are the required link between the Resource Selection and Results Merging, and the importance of diversity in the merged results. After an overview of the new data collection and relevance judgments, the individual participants’ results for the tasks are introduced, analyzed, and compared

Overview of the TREC 2013 Federated Web Search Track

The TREC Federated Web Search track is intended to promote research related to federated search in a realistic web setting, and hereto provides a large data collection gathered from a series of online search engines. This overview paper discusses the results of the first edition of the track, FedWeb 2013. The focus was on basic challenges in federated search: (1) resource selection, and (2) results merging. After an overview of the provided data collection and the relevance judgments for the test topics, the participants’ individual approaches and results on both tasks are discussed. Promising research directions and an outlook on the 2014 edition of the track are provided as well

Explicit diversification of event aspects for temporal summarization

During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building on recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-the-art explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014, and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical novelty-based diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness

Real Time Web Search Framework for Performing Efficient Retrieval of Data

With the rapidly growing amount of information on the internet, real-time system is one of the key strategies to cope with the information overload and to help users in finding highly relevant information. Real-time events and domain-specific information are important knowledge base references on the Web that frequently accessed by millions of users. Real-time system is a vital to product and a technique must resolve the context of challenges to be more reliable, e.g. short data life-cycles, heterogeneous user interests, strict time constraints, and context-dependent article relevance. Since real-time data have only a short time to live, real-time models have to be continuously adapted, ensuring that real-time data are always up-to-date. The focal point of this manuscript is for designing a real-time web search approach that aggregates several web search algorithms at query time to tune search results for relevancy. We learn a context-aware delegation algorithm that allows choosing the best real-time algorithms for each query request. The evaluation showed that the proposed approach outperforms the traditional models, in which it allows us to adapt the specific properties of the considered real-time resources. In the experiments, we found that it is highly relevant for most recently searched queries, consistent in its performance, and resilient to the drawbacks faced by other algorithms

Overview of the TREC 2014 Federated Web Search Track

The TREC Federated Web Search track facilitates research in topics related to federated web search, by providing a large realistic data collection sampled from a multitude of online search engines. The FedWeb 2013 challenges of Resource Selection and Results Merging challenges are again included in FedWeb 2014, and we additionally introduced the task of vertical selection. Other new aspects are the required link between the Resource Selection and Results Merging, and the importance of diversity in the merged results. After an overview of the new data collection and relevance judgments, the individual participants’ results for the tasks are introduced, analyzed, and compared

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

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