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

Improving the effectiveness and efficiency of web-based search tasks for policy workers

We adapt previous literature on search tasks for developing a domain-specific search engine that supports the search tasks of policy workers. To characterise the search tasks we conducted two rounds of interviews with policy workers at the municipality of Utrecht, and found that they face different challenges depending on the complexity of the task. During simple tasks, policy workers face information overload and time pressures, especially during web-based searches. For complex tasks, users prefer finding domain experts within their organisation to obtain the necessary information, which requires a different type of search functionality. To support simple tasks, we developed a web search engine that indexes web pages from authoritative sources only. We tested the hypothesis that users prefer expert search over web search for complex tasks and found that supporting complex tasks requires integrating functionality that enables finding internal experts within the broader web search engine. We constructed representative tasks to evaluate the proposed system’s effectiveness and efficiency, and found that it improved user performance. The search functionality developed could be standardised for use by policy workers in different municipalities within the Netherlands

Improving the Effectiveness and Efficiency of Web-Based Search Tasks for Policy Workers

We adapt previous literature on search tasks for developing a domain-specific search engine that supports the search tasks of policy workers. To characterise the search tasks we conducted two rounds of interviews with policy workers at the municipality of Utrecht, and found that they face different challenges depending on the complexity of the task. During simple tasks, policy workers face information overload and time pressures, especially during web-based searches. For complex tasks, users prefer finding domain experts within their organisation to obtain the necessary information, which requires a different type of search functionality. To support simple tasks, we developed a web search engine that indexes web pages from authoritative sources only. We tested the hypothesis that users prefer expert search over web search for complex tasks and found that supporting complex tasks requires integrating functionality that enables finding internal experts within the broader web search engine. We constructed representative tasks to evaluate the proposed system’s effectiveness and efficiency, and found that it improved user performance. The search functionality developed could be standardised for use by policy workers in different municipalities within the Netherlands

Semantic multimedia modelling & interpretation for search & retrieval

With the axiomatic revolutionary in the multimedia equip devices, culminated in the proverbial proliferation of the image and video data. Owing to this omnipresence and progression, these data become the part of our daily life. This devastating data production rate accompanies with a predicament of surpassing our potentials for acquiring this data. Perhaps one of the utmost prevailing problems of this digital era is an information plethora. Until now, progressions in image and video retrieval research reached restrained success owed to its interpretation of an image and video in terms of primitive features. Humans generally access multimedia assets in terms of semantic concepts. The retrieval of digital images and videos is impeded by the semantic gap. The semantic gap is the discrepancy between a user’s high-level interpretation of an image and the information that can be extracted from an image’s physical properties. Content- based image and video retrieval systems are explicitly assailable to the semantic gap due to their dependence on low-level visual features for describing image and content. The semantic gap can be narrowed by including high-level features. High-level descriptions of images and videos are more proficient of apprehending the semantic meaning of image and video content. It is generally understood that the problem of image and video retrieval is still far from being solved. This thesis proposes an approach for intelligent multimedia semantic extraction for search and retrieval. This thesis intends to bridge the gap between the visual features and semantics. This thesis proposes a Semantic query Interpreter for the images and the videos. The proposed Semantic Query Interpreter will select the pertinent terms from the user query and analyse it lexically and semantically. The proposed SQI reduces the semantic as well as the vocabulary gap between the users and the machine. This thesis also explored a novel ranking strategy for image search and retrieval. SemRank is the novel system that will incorporate the Semantic Intensity (SI) in exploring the semantic relevancy between the user query and the available data. The novel Semantic Intensity captures the concept dominancy factor of an image. As we are aware of the fact that the image is the combination of various concepts and among the list of concepts some of them are more dominant then the other. The SemRank will rank the retrieved images on the basis of Semantic Intensity. The investigations are made on the LabelMe image and LabelMe video dataset. Experiments show that the proposed approach is successful in bridging the semantic gap. The experiments reveal that our proposed system outperforms the traditional image retrieval systems

Un modèle de recherche d'information agrégée basée sur les réseaux bayésiens dans des documents semi-structurés

Nous proposons un modèle de recherche d'information basé sur les réseaux bayésiens. Dans ce modèle, la requête de l'utilisateur déclenche un processus de propagation pour sélectionner les éléments pertinents. Dans notre modèle, nous cherchons à renvoyer à l'utilisateur un agrégat au lieu d'une liste d'éléments. En fait, l'agrégat formulé à partir d'un document est considéré comme étant un ensemble d'éléments ou une unité d'information (portion d'un document) qui répond le mieux à la requête de l'utilisateur. Cet agrégat doit répondre à trois aspects à savoir la pertinence, la non-redondance et la complémentarité pour qu'il soit qualifié comme une réponse à cette requête. L'utilité des agrégats retournés est qu'ils donnent à l'utilisateur un aperçu sur le contenu informationnel de cette requête dans la collection de documents. Afin de valider notre modèle, nous l'avons évalué dans le cadre de la campagne d'évaluation INEX 2009 (utilisant plus que 2 666 000 documents XML de l'encyclopédie en ligne Wikipédia). Les expérimentations montrent l'intérêt de cette approche en mettant en évidence l'impact de l'agrégation de tels éléments.The work described in this thesis are concerned with the aggregated search on XML elements. We propose new approaches to aggregating and pruning using different sources of evidence (content and structure). We propose a model based on Bayesian networks. The dependency relationships between query-terms and terms-elements are quantified by probability measures. In this model, the user's query triggers a propagation process to find XML elements. In our model, we search to return to the user an aggregate instead of a list of XML elements. In fact, the aggregate made from a document is considered an information unit (or a portion of this document) that best meets the user's query. This aggregate must meet three aspects namely relevance, non-redundancy and complementarity in order to answer the query. The value returned aggregates is that they give the user an overview of the information need in the collection

Focused retrieval and result aggregation with political data

This paper presents a case-study in which we use a large semi-structured data set consisting of official transcripts of meetings of the Dutch parliament for focused retrieval and result aggregation. Transcripts of meetings are a document genre characterized by a complex narrative structure. The essence is not only what is said, but also by who and to whom. We have notes of more than 40 years of Dutch parliamentary debates where this structure is exploited to automatically make semantic annotations. These annotations yield numerous new ways of searching, browsing, mining and summarizing these documents. Concerning result aggregation, we summarise and visualise the structure of meetings into tables of content and interruption graphs. The contents of meetings or parts of meetings are condensed into word clouds that are created using a parsimonious language model. Furthermore, we have developed a search engine that exploits the structure and annotations of our data making it possible to provide entry points, to group search results, and to use faceted search techniques for data-exploration. Evaluation shows that our content and structure summarization tools provide a good first impression of a debate. Users reported that, compared to a standard document retrieval system, our search engine gives a better overview of the data. Search tasks are performed faster and the users felt more certain of their answers