4,628 research outputs found
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Using Document Indexers for Faceted Search in Dataspaces
Efficient information retrieval is essential to enrich user experience when searching for documents in dataspaces. With the continued growth in the volume and complexity of documents, the efficient information retrieval for searches has become increasingly challenging. To improve usersâ search experience, faceted search combines direct keyword search methods with faceted browsing using a predefined set of categories (facets). This paper studies a faceted search approach that integrates dynamic facets generation with search. To further enhance the faceted search, alternative indexers based on pre-defined ontology for data repositories within dataspaces are evaluated in terms of execution time and data size. Experimental results suggest that combining the proposed faceted search with appropriate indexers improves search performance enhancing user experience
Exploring scholarly data with Rexplore.
Despite the large number and variety of tools and services available today for exploring scholarly data, current support is still very limited in the context of sensemaking tasks, which go beyond standard search and ranking of authors and publications, and focus instead on i) understanding the dynamics of research areas, ii) relating authors âsemanticallyâ (e.g., in terms of common interests or shared academic trajectories), or iii) performing fine-grained academic expert search along multiple dimensions. To address this gap we have developed a novel tool, Rexplore, which integrates statistical analysis, semantic technologies, and visual analytics to provide effective support for exploring and making sense of scholarly data. Here, we describe the main innovative elements of the tool and we present the results from a task-centric empirical evaluation, which shows that Rexplore is highly effective at providing support for the aforementioned sensemaking tasks. In addition, these results are robust both with respect to the background of the users (i.e., expert analysts vs. âordinaryâ users) and also with respect to whether the tasks are selected by the evaluators or proposed by the users themselves
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
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 âenablersâ, 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
Faceted Search of Heterogeneous Geographic Information for Dynamic Map Projection
This paper proposes a faceted information exploration model that supports
coarse-grained and fine-grained focusing of geographic maps by offering a
graphical representation of data attributes within interactive widgets. The
proposed approach enables (i) a multi-category projection of long-lasting
geographic maps, based on the proposal of efficient facets for data exploration
in sparse and noisy datasets, and (ii) an interactive representation of the
search context based on widgets that support data visualization, faceted
exploration, category-based information hiding and transparency of results at
the same time. The integration of our model with a semantic representation of
geographical knowledge supports the exploration of information retrieved from
heterogeneous data sources, such as Public Open Data and OpenStreetMap. We
evaluated our model with users in the OnToMap collaborative Web GIS. The
experimental results show that, when working on geographic maps populated with
multiple data categories, it outperforms simple category-based map projection
and traditional faceted search tools, such as checkboxes, in both user
performance and experience
A Nine Month Report on Progress Towards a Framework for Evaluating Advanced Search Interfaces considering Information Retrieval and Human Computer Interaction
This is a nine month progress report detailing my research into supporting users in their search for information, where the questions, results or even thei
mSpace meets EPrints: a Case Study in Creating Dynamic Digital Collections
In this case study we look at issues involved in (a) generating dynamic digital libraries that are on a particular topic but span heterogeneous collections at distinct sites, (b) supplementing the artefacts in that collection with additional information available either from databases at the artefact's home or from the Web at large, and (c) providing an interaction paradigm that will support effective exploration of this new resource. We describe how we used two available frameworks, mSpace and EPrints to support this kind of collection building. The result of the study is a set of recommendations to improve the connectivity of remote resources both to one another and to related Web resources, and that will also reduce problems like co-referencing in order to enable the creation of new collections on demand
Multi Visualization and Dynamic Query for Effective Exploration of Semantic Data
Semantic formalisms represent content in a uniform way according to ontologies. This enables manipulation and reasoning via automated means (e.g. Semantic Web services), but limits the userâs ability to explore the semantic data from a point of view that originates from knowledge representation motivations. We show how, for user consumption, a visualization of semantic data according to some easily graspable dimensions (e.g. space and time) provides effective sense-making of data. In this paper, we look holistically at the interaction between users and semantic data, and propose multiple visualization strategies and dynamic filters to support the exploration of semantic-rich data.
We discuss a user evaluation and how interaction challenges could be overcome to create an effective user-centred framework for the visualization and manipulation of semantic data. The approach has been implemented and evaluated on a real company archive
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Ontology-based end-user visual query formulation: Why, what, who, how, and which?
Value creation in an organisation is a time-sensitive and data-intensive process, yet it is often delayed and bounded by the reliance on IT experts extracting data for domain experts. Hence, there is a need for providing people who are not professional developers with the flexibility to pose relatively complex and ad hoc queries in an easy and intuitive way. In this respect, visual methods for query formulation undertake the challenge of making querying independent of usersâ technical skills and the knowledge of the underlying textual query language and the structure of data. An ontology is more promising than the logical schema of the underlying data for guiding users in formulating queries, since it provides a richer vocabulary closer to the usersâ understanding. However, on the one hand, today the most of worldâs enterprise data reside in relational databases rather than triple stores, and on the other, visual query formulation has become more compelling due to ever-increasing data size and complexityâknown as Big Data. This article presents and argues for ontology-based visual query formulation for end-users; discusses its feasibility in terms of ontology-based data access, which virtualises legacy relational databases as RDF, and the dimensions of Big Data; presents key conceptual aspects and dimensions, challenges, and requirements; and reviews, categorises, and discusses notable approaches and systems
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