Interactive Exploration over RDF Data using Formal Concept Analysis

International audienceWith an increased interest in machine processable data, many datasets are now published in RDF (Resource Description Framework) format in Linked Data Cloud. These data are distributed over independent resources which need to be centralized and explored for domain specific applications. This paper proposes a new approach based on interactive data exploration paradigm using Pattern Structures, an extension of Formal Concept Analysis, to provide exploration and navigation over Linked Data through concept lattices. It takes RDF triples and RDF Schema based on user requirements and provides one navigation space resulting from several RDF resources. This navigation space allows user to navigate and search only the part of data that is interesting for her

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 user’s 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

Structuring visual exploratory analysis of skill demand

The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on

TOOL FOR INTERACTIVE VISUAL ANALYSIS OF LARGE HIERARCHICAL DATA STRUCTURES

In the Big Data era data visualization and exploration systems, as means for data perception and manipulation are facing major challenges. One of the challenges for modern visualization systems is to ensure adequate visual presentation and interaction.  Therefore, within this paper, we present a tool for interactive visualization of data with a hierarchical structure. It is a general-purpose tool that uses a graph-based approach. However, its main focus is on the visual analysis of concept lattices generated as the output of the Formal Concept Analysis algorithm. As the data grow, concept lattice can become complex and hard for visualization and analysis. In order to address this issue, functionalities important for the exploration of the large concept lattices are applied within this tool. The usage of the tool is presented in the example of visualization of concept lattices generated based on the available data on the Canadas open data portal and can be used for exploring the usage of tags within datasets

Enhancing Workflow with a Semantic Description of Scientific Intent

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Linked data querying through FCA-based schema indexing

The efficiency of SPARQL query evaluation against Linked Open Data may benefit from schema-based indexing. However, many data items come with incomplete schema information or lack schema descriptions entirely. In this position paper, we outline an approach to an indexing of linked data graphs based on schemata induced through Formal Concept Analysis. We show how to map queries onto RDF graphs based on such derived schema information. We sketch next steps for realizing and optimizing the suggested approach