The proliferation of Linked Open Data on the Web has increased the amount of data available for analysis and reuse. However, casual users find it difficult to explore and use Semantic Web Data due to the prevalence of specialised browsers that require complex queries to be formed and intimate knowledge on the structure of datasets. We address this problem in the Rhizomer tool by applying the data analysis mantra of overview, zoom and filter. These interaction patterns are implemented using information architecture components users are already familiar with but that are automatically generated from data and ontologies. This approach makes it possible to obtain an overview of the dataset being explored using techniques, such as navigation menus, treemaps or sitemaps, which are usually not available in text-based semantic web browsers. From there, users can interactively explore the data using facets. Moreover, facets also feature a pivoting operation, motivated during tests with lay users, that removes the main constraint of most faceted browsers, i.e. the inability to combine filters for differently faceted views to build complex queries.

Our proposal is to develop generic Information Architecture components to facilitate publishing and browsing semantic data in the web, improving its usability and accessibility.

Abstract. In the last years, the amount of semantic data available in the Web has increased dramatically. The potential of this vast amount of data is enormous but in most cases it is very difficult for users to explore and use this data, especially for those without experience with Semantic Web technologies. Applying information visualization techniques to the Semantic Web helps users to easily explore large amounts of data and interact with them. In this article we devise a formal Linked Data Visualization Model (LDVM), which allows to dynamically connect data with visualizations. We report about our comprehensive implementation of the LDVM comprising a library of generic visualizations that enable both users and data analysts to get an overview on, visualize and explore the Data Web and perform detailed analyzes on Linked Data.

MedISys is a media monitoring system initially intended for news items related to human health. The tool has how been extended by the Joint Research Centre, Universitat de Lleida and IRTA to also deal with plant health threats. This EFSA-funded project was based on a knowledge representation approach that generated an ontology, a formal representation of knowledge related to plant health threats. The ontology models plant pests and diseases, together with other concepts related with them: affected crops, hosts, vectors and symptoms. First of all, a collection of news sources related to plant health threats was collected to be monitored by MedISys. These sources included already known manually curated Web pages but also additional ones discovered by performing global Web searches using terms appearing in the ontology. Then, the news items coming from these sources were filtered using MedISys using a set of categories with keywords to identify those actually about plant health threats. Most of these categories focused on known threats and used terms associated with the 117 pests and diseases selected at the beginning of the project. Additionally, categories for unknown threats were also developed. In this case the categories included keywords that are usually used by experts to describe unknown threats and keywords related with symptoms expressions. All these MedISys categories combined provide mechanism to monitor plant health threats mentions in media, from newspapers to social media, ranging from those that explicitly mention a named threat (useful to monitor re-emerging threats or their spread) to those related to unknown ones (to monitor potential new threats). The project concluded with an evaluation of the e-mail alerts and reports generated by MedISys based on the previous categories. A survey and tests with real users were conducted and the results analysed to generate a set of recommendations and improvements to facilitate the use of MedISys as a plant health threats monitoring tool.

Media monitoring for emerging risks has become an essential tool in public health. This approach also has the potential to deliver early warning of emerging risks to plant health. The European Food Safety Authority (EFSA) has launched a project in collaboration with the Joint Research Centre (JRC) of the European Commission to make use of the Medical Information System (MedISys) media monitoring tool for monitoring plant health threats. This paper provides a summary of the project, which is taking place in partnership with the University of Lleida and the Institut de Recerca I Tecnologia Agroalimentàries. The four specific objectives of the project are: (1) to collate new and appropriate media and information sources, (2) to develop a multilingual ontology for the global identification of emerging new plant health threats, (3) to develop strategies to monitor (re‐)emerging plant health threats at global and regional scales, and (4) to test approaches for reporting the identified warnings to the relevant EFSA units and experts through the MedISys interface, including mapping and georeferencing. The plan and structure of the project are presented with examples of preliminary results.

In order to make a Semantic Web dataset more usable to a wider range of users, specially Linked Data ones, Rhizomer constitutes a tool for data publishing in the web that in addition to common data browsing mechanisms based on HTML rendering, provides a set of components that facilitate awareness of the dataset at hand borrowed from Information Architecture. Rhizomer automatically generates navigation menus taking into account the ontologies used by the dataset and facets based on how properties are instantiated for each of the classes in the dataset. This makes it possible for users to easily be aware of the main kinds of things in the dataset but also their main properties and the values the take while they perform faceted navigation. These generic IA components are complemented with specialised interaction services that can be dynamically deployed and associated to resource using semantic web services. Among these services, Rhizomer features one that provides simple edition of the data using autocomplete forms guided by the ontologies used in the dataset and the available resources.

Structure AbstractPurpose. The growing volumes of semantic data available in the Web result in the need for handling the Information Overload phenomenon. The potential of this amount of data is enormous but in most cases it is very difficult for users to visualize, explore and use this data, especially for lay-users without experience with Semantic Web technologies.Design/methodology/approach. The Visual Information-Seeking Mantra "Overview first, zoom and filter, then details-on-demand" proposed by Shneiderman describes how data should be presented in different stages to achieve an effective exploration. The overview is the first user task when dealing with a dataset. The objective is that the user is capable of getting an idea about the overall structure of the dataset. Different Information Architecture (IA) components supporting the overview tasks have been developed, so they are automatically generated from semantic data, and evaluated with end-users.Findings. The chosen IA components are well known to Web users, as they are present in most web pages: navigation bars, site maps and site indexes. We complement them with Treemaps, a visualization technique for displaying hierarchical data. These components have been developed following an iterative User-Centered Design methodology. Evaluations with end-users have shown that they get easily used to them despite the fact that they are generated automatically from structured data, without requiring knowledge about the underlying semantic technologies, and that the different overview components complement each other as they focus on different information search needs.Originality/value. Obtaining semantic datasets overviews cannot be easily done with the current semantic web browsers. Overviews become difficult to achieve with large heterogeneous datasets, which is typical in the Semantic Web, because traditional Information Architecture techniques do not easily scale to large datasets. This can be a serious limitation when exploring a dataset for the first time, especially for lay-users. Our proposal is to reuse and adapt existing Information Architecture (IA) components to provide this overview to users and show that they can be generated automatically from the thesaurus and ontologies that structure semantic data while providing a comparable user experience to traditional web sites.

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