9,982 research outputs found

    NITELIGHT: A Graphical Tool for Semantic Query Construction

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    Query formulation is a key aspect of information retrieval, contributing to both the efficiency and usability of many semantic applications. A number of query languages, such as SPARQL, have been developed for the Semantic Web; however, there are, as yet, few tools to support end users with respect to the creation and editing of semantic queries. In this paper we introduce a graphical tool for semantic query construction (NITELIGHT) that is based on the SPARQL query language specification. The tool supports end users by providing a set of graphical notations that represent semantic query language constructs. This language provides a visual query language counterpart to SPARQL that we call vSPARQL. NITELIGHT also provides an interactive graphical editing environment that combines ontology navigation capabilities with graphical query visualization techniques. This paper describes the functionality and user interaction features of the NITELIGHT tool based on our work to date. We also present details of the vSPARQL constructs used to support the graphical representation of SPARQL queries

    Social Search: retrieving information in Online Social Platforms -- A Survey

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    Social Search research deals with studying methodologies exploiting social information to better satisfy user information needs in Online Social Media while simplifying the search effort and consequently reducing the time spent and the computational resources utilized. Starting from previous studies, in this work, we analyze the current state of the art of the Social Search area, proposing a new taxonomy and highlighting current limitations and open research directions. We divide the Social Search area into three subcategories, where the social aspect plays a pivotal role: Social Question&Answering, Social Content Search, and Social Collaborative Search. For each subcategory, we present the key concepts and selected representative approaches in the literature in greater detail. We found that, up to now, a large body of studies model users' preferences and their relations by simply combining social features made available by social platforms. It paves the way for significant research to exploit more structured information about users' social profiles and behaviors (as they can be inferred from data available on social platforms) to optimize their information needs further

    CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

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    After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

    Understanding and exploiting user intent in community question answering

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    A number of Community Question Answering (CQA) services have emerged and proliferated in the last decade. Typical examples include Yahoo! Answers, WikiAnswers, and also domain-specific forums like StackOverflow. These services help users obtain information from a community - a user can post his or her questions which may then be answered by other users. Such a paradigm of information seeking is particularly appealing when the question cannot be answered directly by Web search engines due to the unavailability of relevant online content. However, question submitted to a CQA service are often colloquial and ambiguous. An accurate understanding of the intent behind a question is important for satisfying the user's information need more effectively and efficiently. In this thesis, we analyse the intent of each question in CQA by classifying it into five dimensions, namely: subjectivity, locality, navigationality, procedurality, and causality. By making use of advanced machine learning techniques, such as Co-Training and PU-Learning, we are able to attain consistent and significant classification improvements over the state-of-the-art in this area. In addition to the textual features, a variety of metadata features (such as the category where the question was posted to) are used to model a user's intent, which in turn help the CQA service to perform better in finding similar questions, identifying relevant answers, and recommending the most relevant answerers. We validate the usefulness of user intent in two different CQA tasks. Our first application is question retrieval, where we present a hybrid approach which blends several language modelling techniques, namely, the classic (query-likelihood) language model, the state-of-the-art translation-based language model, and our proposed intent-based language model. Our second application is answer validation, where we present a two-stage model which first ranks similar questions by using our proposed hybrid approach, and then validates whether the answer of the top candidate can be served as an answer to a new question by leveraging sentiment analysis, query quality assessment, and search lists validation

    LDA-based Term Profiles for Expert Finding in a Political Setting

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    A common task in many political institutions (i.e. Parliament) is to find politicians who are experts in a particular field. In order to tackle this problem, the first step is to obtain politician profiles which include their interests, and these can be automatically learned from their speeches. As a politician may have various areas of expertise, one alternative is to use a set of subprofiles, each of which covers a different subject. In this study, we propose a novel approach for this task by using latent Dirichlet allocation (LDA) to determine the main underlying topics of each political speech, and to distribute the related terms among the different topic-based subprofiles. With this objective, we propose the use of fifteen distance and similarity measures to automatically determine the optimal number of topics discussed in a document, and to demonstrate that every measure converges into five strategies: Euclidean, Dice, Sorensen, Cosine and Overlap. Our experimental results showed that the scores of the different accuracy metrics of the proposed strategies tended to be higher than those of the baselines for expert recommendation tasks, and that the use of an appropriate number of topics has proved relevant
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